Symposium FA

Session FA-1 Computational-aided energy materials design

FA-1:IL01 Rational Engineering of Low Thermal Conductivity Materials through Machine Learning and Chemical Insights
C. WOLVERTON, Department of Materials Science and Eng., Northwestern University, USA

Thermal energy conversion and management technologies such as thermoelectrics require materials with low lattice thermal conductivity (LTC). This talk presents several examples of a data-driven framework combining high-throughput first-principles calculations, machine learning (ML), and chemical bonding principles to accelerate the discovery and design of such materials. High-throughput studies reveal critical roles of anharmonicity and chemical structure.  For example, (1) Quartic anharmonicity reduces LTC sixfold in zinc-blende HgTe; (2) Rattling cations in full Heuslers induce phonon-glass behavior; (3) Quaternary chalcogenides  achieve low LTC through a combination of anharmonicity and scattering channels.  We also demonstrate how screening local coordination environments (e.g., lone-pair cation motifs) enables inverse design of chalcogenides near the amorphous limit (0.3–0.4 W/m·K). We have also constructed a model of “minimum LTC” (mLTC) that unifies the phonon gas and diffuson pictures of thermal transport, and have evaluated mLTC for ~10^4 compounds.  We unveil the underlying physics by showing that for a given parent compound, κLmin is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder.  We also construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured low LTC. These strategies spanning database screening, bonding descriptors, and ML-predicted LTC establish a roadmap for rational thermal engineering in energy materials.


FA-1:IL02  Tailoring Phonon and Electronic Transport in 2D Metal-Organic Frameworks for Thermoelectric Applications
C. MELIS, H.L. KAGDADA, R. DETTORI, L. COLOMBO, C. MELIS, Department of Physics, University of Cagliari, Monserrato (CA), Italy

Two-dimensional metal–organic frameworks (2D MOFs) combine low lattice thermal conductivity with tunable electronic properties, making them promising candidates for thermoelectric energy conversion. Here, we integrate ab initio lattice-dynamics calculations with electronic-transport modeling to uncover phonon–electron interplay in copper benzenehexathiolate (Cu₃BHT). Using the Boltzmann and Wigner transport formalisms, we demonstrate that coherent phonon transport significantly contributes to heat conduction, modifying the temperature scaling of κ and enabling ultralow lattice thermal conductivity (~0.5 W m⁻¹ K⁻¹). In parallel, we explore routes to enhance the thermoelectric performance through lattice engineering and controlled pseudo-redox tuning of the Cu²⁺/Cu⁺ ratio, which induces a transition from metallic to semiconducting behavior and markedly increases the Seebeck coefficient while reducing the electronic thermal conductivity. These combined effects highlight the unique potential of Cu₃BHT and related 2D MOFs as model systems for coherent phonon transport and tunable thermoelectric materials.


FA-1:IL03  AI / ML Accelerated Simulations via the Neuroevolution Potential Approach: Dynamics and Mixing in Halide Perovskites
P. ERHART, Chalmers University of Technology, Gothenburg, Sweden

Understanding the phase behavior of mixed-cation halide perovskites is essential for optimizing both their structural stability and optoelectronic performance. Due to the vast configurational space and strongly anharmonic nature of these materials, efficient sampling methods are required to obtain not only detailed insight but statistically robust results. Here, the neuroevolution potential (NEP) framework enables the construction of machine-learned interatomic potential models that strike a pareto-optimal balance between accuracy and computational speed. Combining NEP-based molecular dynamics simulations moreover with phonon mode projection analysis, allows us to gain a comprehensive picture of the dynamic processes governing phase transitions and octahedral tilt patterns in halide perovskites. We apply this approach to map the phase diagram of MA(1−x)FA(x)PbI3, revealing a morphotropic phase boundary (MPB) near 27% FA content that separates out-of-phase from in-phase octahedral tilt regimes. Our results provide a systematic and internally consistent description of this important system, reconciling previously partial and sometimes conflicting experimental findings. Complementary density functional theory calculations show that band-edge fluctuations are maximized near the MPB.


FA-1:IL04  A “DFT-ML” Framework for Rational Design of Next-Generation Perovskites for Optoelectronic Applications
A. MANNODI KANAKKITHODI, M.H. RAHMAN, M. BISWAS, R. DESAI, Y. YANG, S. STRADER, J. POTTS, J. AHN, Purdue University School of Materials Engineering, West Lafayette, IN, USA

We developed a rational virtual materials design strategy powered by high-throughput density functional theory (DFT) computations and surrogate machine learning (ML) models to perform multi-objective optimization and discovery of novel crystalline halide and chalcogenide perovskite semiconductors. This strategy involved compiling massive DFT datasets of relevant properties within a multi-fidelity active learning framework that combined semi-local and hybrid nonlocal functionals, and training predictive models to accurately obtain any property of interest as well as synthesis likelihood directly from the semiconductor composition or structure. “DFT-ML” models were trained for both bulk and defect properties, especially using graph neural network (GNN)-based interatomic potentials. The positive and unlabeled learning approach was applied to obtain synthesis probability scores by training on a combined dataset of DFT computations and known experimental results from the literature. Using these “DFT-ML” strategies, we successfully designed novel stable and synthesizable halide and chalcogenide perovskites with suitable electronic, optical, and defect properties for a variety of optoelectronic applications.


FA-1:L05  Multi-Scale Simulation for Designing Electrode Structure of Dye-Sensitized Solar Cell Devices
MARI ONODERA¹, MOMOJI KUBO^1,2, ¹Institute for Materials Research, Tohoku University, Sendai, Japan; ²New Industry Creation Hatchery Center, Tohoku University, Sendai, Japan

Features of Dye-Sensitized Solar Cells (DSSC) are low-cost production, wide variety of designs, independence of installation sites, etc. In order to contribute to a better understanding of the energy conversion mechanisms and precising design optimization, we have developed an original multi-scale DSSC simulator and then have continuously extended its function and predictability. In this study, we added a new computational algorithm which enables us to simulate the honeycomb-like TiO2 structures in addition to the porous TiO2 structures. The DSSC characteristics of the above TiO2 structures were examined by means of meso- and macro-scale simulations. The meso-scale simulation evaluates diffusion coefficients of electrons and ionic species inside the network of several TiO2 particles and the electrolyte. The macro-scale simulation gives the I-V characteristics for DSSC devices using the data from the meso-scale simulations. We clarified the difference of the electron diffusivity and their effect on the I-V characteristics in various porous TiO2 structures and honeycomb-like TiO2 structures, and finally proposed that highly ordered honeycomb-like structure gives the best performance as electrode of DSSC among the structures we examined.


FA-1:IL06  NaSICON Materials as a Rich Platform for Understanding Metastability
SUNKYU PARK^1,2,3, ZILIANG WANG⁴, K. CHOUDHARY¹, J.-N. CHOTARD^1,5, D. CARLIER^2,5,  F. FAUTH⁶, C. MASQUELIER^1,5,7, L. CROGUENNEC^2,5, P. CANEPA^4,8, ¹Laboratoire de Réactivité et de Chimie des Solides, Université de Picardie Jules Verne, CNRS-UMR 7314, Amiens Cedex, France; ²CNRS, Univ. Bordeaux, Bordeaux INP, ICMCB UMR 5026, Pessac, France; ³TIAMAT, Amiens, France; ⁴Department of Materials Science and Engineering, National University of Singapore, Singapore, Singapore; ⁵RS2E, Réseau Français sur le Stockage Electrochimique de l’Energie, FR CNRS 3459, Amiens Cedex, France; ⁶CELLS-ALBA Synchrotron, Cerdanyola del Vallès, Barcelona, Spain; ⁷Institut Universitaire de France, Paris, France; ⁸Department of Electrical and Computer Engineering, Houston, TX, USA

Polyatomic materials, such as those based on the sodium (Na) superionic conductor (NASICON), offer a rich chemical and structural foundation for developing high-energy-density electrodes and electrolytes. Energy storage applications aside, the structural versatility of NASICON phases is accompanied by significant structural and electrochemical complexity. In this talk, I will discuss the new NASICON-type single-phase NaxV2(PO4)3 compositions (1.5 ≤ x ≤ 2.5) that have never been reported before, derived from an innovative and straightforward synthesis method informed by computational simulations.8,9,10 Guided by predicted phase diagrams from first-principles calculations, the chemically c-Na2V2(PO4)3  phase was synthesized by annealing an equimolar mixture of Na3V2(PO4)3 and NaV2(PO4)3, which exhibits a new distribution of sodium ions with an occupancy of the Na(1) site of only 0.66(4), whereas that of the electrochemically obtained e-Na2V2(PO4)3 (from Na3V2(PO4)3) is close to 1.15. Unlike conventional Na3V2(PO4)3, when used as positive electrode materials in Na-ion batteries, the new NaxV2(PO4)3 compositions identified lead to unusual single-phase Na+ extraction/insertion mechanisms with continuous voltage changes upon Na+ extraction/insertion.9,10 We demonstrate that the average equilibrium operating voltage observed upon Na+ deintercalation from the single-phase Na2V2(PO4)3 is significantly increased up to an average value of ~3.70 V vs. Na+/Na (thanks to the activation of the VIV/VV redox couple) compared to 3.37 V vs. Na+/Na in conventional Na3V2(PO4)3, thus leading to an increase in the theoretical energy density from ~396.3 Wh/kg to ~458.1 Wh/kg. Furthermore, never reported before NASICON-type compositions, such as NaxV2(PO4)3 with 0 ≤ x < 1 were obtained by electrochemical and chemical Na+ deintercalation from the new c-Na2V2(PO4).9,10  The intrinsic complexity revealed by the phase space of NaxV2(PO4)3 can be leveraged to our advantage in developing new NaSICON (or other polyatomic) electrode compositions with improved electrochemical characteristics. 
(1)    H. Y. P. Hong, Crystal structures and crystal chemistry in the system Na1+xZr2SixP3-xO12, Mater. Res. Bull., 11, 173 (1976). 
(2)    J. B. Goodenough, H. Y. P. Hong and J. A. Kafalas, Fast sodium (1+) ion transport in skeleton structures, Mater. Res. Bull., 11, 203 (1976).
(3)    C. Masquelier and L. Croguennec, Polyanionic (Phosphates, Silicates, Sulfates) Frameworks as Electrode Materials for Rechargeable Li (or Na) Batteries, Chem. Rev. 113, 8, 6552 (2013).
(4)    J. N. Chotard, G. Rousse, R. David, O. Mentré, M. Courty, and C. Masquelier, Discovery of a Sodium-Ordered Form of Na3V2(PO4)3 below Ambient Temperature, Chem. Mater., 27, 5982 (2015)
(5)    B. Singh, Z. Wang, S. Park, G. S. Gautam, J.-N. Chotard, L. Croguennec, D. Carlier, A. K. Cheetham, C. Masquelier, and P. Canepa, A chemical map of NaSICON electrode materials for sodium-ion batteries, J. Mater. Chem. A, 9, 281 (2021).
(6)    S. Park, Z. Wang, Z. Deng, I. Mong, P. Canepa, F. Fauth, D. Carlier, L. Croguennec, C. Masquelier, and J.-N. Chotard, Crystal Structure of Na2V2(PO4)3, an Intriguing Phase Spotted in the Na3V2(PO4)3–Na1V2(PO4)3 System, Chem. Mater. 34, 1, 451 (2022).
(7)    Z. Wang, T. P. Mishra, W. Xie, Z. Deng, G. S. Gautam, A. K. Cheetham, and P. Canepa, Kinetic Monte Carlo Simulations of Sodium Ion Transport in NaSICON Electrodes, ACS Materials Lett.  5, 9, 2499–2507 (2023).
(8)    Z. Wang, S. Park, Z. Deng, D. Carlier, J.-N. Chothard, L. Croguennec, G. S. Gautam, A. K. Cheetham, C. Masquelier, and P. Canepa, Phase stability and sodium-vacancy orderings in a NaSICON electrode, J. Mater. Chem. A, 10, 209 (2022).
(9)    New Nasicon-Type High Voltage Sodium Vanadium Phosphates Materials For Na-Ion Batteries, WO-2023209113-A1
(10)    S. Park, Z. Wang, K. Choudhary, J.-N. Chotard, D. Carlier, F. Fauth, P. Canepa, L. Croguennec, and C. Masquelier, Reaching V2(PO4)3 by Sodium Extraction from Single-Phase NaxV2(PO4)3 (1 < x < 3) NaSICON Positive Electrode Materials, under review (2024).


FA-1:L07  Design of Cathode Catalyst Layer Structures through Large-Scale Reactive MD Simulation for Advancing Polymer Electrolyte Fuel Cell Performance
KAITO MORI, T. NAKAMURA, S. FUKUSHIMA, Y. OOTANI, Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan; N. OZAWA, New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan; M. KUBO, Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan, and New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan

To advance polymer electrolyte fuel cell (PEFC) performance, it is necessary to enhance the oxygen reduction reaction (ORR) activity in the cathode catalyst layer (CL), which consists of Pt nanoparticles, carbon supports, ionomer, and water. To develop high-performance CLs, molecular dynamics (MD) method is promising approach. Then, we developed a large-scale reactive MD simulator to calculate multi-million-atom CL structures and analyzed the effect of CL structures on ORR activity under different ionomer/carbon weight ratio (I/C) conditions (0.3 to 1.0). Our results showed that lower I/C leads to the increment of O₂ adsorption on Pt due to thinner ionomer layers, suggesting high ORR activity. On the other hand, at lower I/C = 0.3, SO₃⁻ poisoning was highest, suggesting low ORR activity. At I/C = 0.3, water molecules do not aggregate within the ionomer due to insufficient ionomer content and instead adsorb on the hydrophilic Pt surface. Hydrophilic SO₃⁻ groups oriented toward the water-absorbed Pt surface, which facilitated SO₃⁻ adsorption on the Pt surface and resulted in high SO₃⁻ poisoning. These findings suggest that controlling I/C and water distribution to optimize the balance between O₂ adsorption and SO₃⁻ poisoning leads to high ORR activity.


FA-1:IL08  Understanding Activity and Selectivity of Catalysts
A. BAGGER, Technical University of Denmark, Department of Physics, Kgs. Lyngby, Denmark

I will discuss how experiments and computational simulations can support each other. I will focus on electrochemical reduction of NOx, CO2, N2, and the combinations. Importantly, all these reactions share a direct competition with hydrogen, and furthermore, several products are formed from each reactant of these reactants. I will give minimalistic models that do not overfit or over interpretating experimental data: i) eCO2 reduction show multiple different products depending on metal catalyst[1,2]. ii) eNOx reduction produce N2O, N2 and NH3[3,4]. Interestingly, reactive metals work close to their reduction potential[5-6]. iii) eN2 reduction to NH3 at ambient conditions has been confirmed on a Li-mediate system[7]. I will discuss systems beyond lithium[8]. iv) Finally, I will show predictive schemes for the synthesis of urea[9,10].
[1] Hori et. al., J. Chem. Soc., Faraday Trans., 1989; [2] Bagger et. al., ChemPhysChem, 2017; [3] Rosca, Duca, Groot, Koper, Chem. Rev. 2009; [4] Wan, Bagger, Rossmeisl, Angew. Chem., 2021; [5] Carvalho, …, Stoerzinger, JACS 2022; [6] Riyaz, Bagger, Electrochim. Acta. 2025; [7] Tsuneto et al. JEAC. 1994. [8] Bagger, et. al., ACS Energy Letters. 2024; [9] Wan, …, Bagger, ACS Catalysis, 2023; [10] Wuttke, Bagger, Commun. Chem. 2025.


FA-1:IL09  Structures and Dynamics of Metal/Oxide Interfaces
YI GAO, Shanghai Advanced Research Institute, Chinese Academy of Sciences, China

The interface between supported metal nanoparticles and oxide substrates is critical in energy, catalysis, and bioengineering. Yet, the atomic structure and dynamic evolution of these interfaces remain elusive, limited by the resolution of experimental techniques and the capabilities of theoretical methods. To address this, we have developed a first-principles-based kinetic Monte Carlo algorithm and high-accuracy machine learning potentials. Our simulations reveal that the atomic-scale migration of nanoparticles stems from the continuous, collective rearrangement of interfacial atoms to achieve an optimal fit, which can be accelerated under reaction conditions. This insight advances the fundamental understanding of metal-oxide interfaces and provides a foundation for their rational design of efficient catalysts.


FA-1:L10  How Subsurface Carbon Affects Hydrogen Interactions at Transition Metal Electrodes
R. LIPIN, M. VANDICHEL, School of Chemical Sciences and Chemical Engineering, Bernal Institute, University of Limerick, Limerick, Republic of Ireland

Subsurface species can form or be explicitly engineered under reaction conditions and persist, quietly steering catalytic behavior, an effect increasingly recognized over the past decades. The growing importance of subsurface carbon (Csub) now extends to electrocatalysis, where a recent study shows that Csub suppresses hydrogen absorption in palladium, thereby promoting selective alkyne semihydrogenation. Motivated by this “hydrogen concentration knob,” we examine how Csub modulates hydrogen adsorption, spill-in, and absorption on transition-metal electrodes. In this work, we use ab initio thermodynamics to construct phase and surface Pourbaix diagrams, showing that Csub tunes hydrogen ad/absorption across pH, potential, temperature, and pressure, shifting thresholds between surface binding and subsurface uptake. The framework identifies stability regimes where Csub enhances HER-relevant hydrogen supply or suppresses over-hydrogenation. We also show that subsurface species can be explicitly engineered to tune hydrogen binding and diffusion, yielding operando-testable, “carbiding-aware” design rules for metal electrodes.
1. ACS Energy Lett. 2025, (in Press) 10.1021/acsenergylett.5c02339 2. ACS Catal. 2025, 15, 17467−17477 3. Science 2008,320, 86-89.


FA-1:L11  Modeling Metal Electrocatalysts as Cathodes: The Urgent Need to Look Beneath the Surface
M. VANDICHEL, R. LIPIN, M. UMER, S. JAVAN NIKKHAH, University of Limerick, Limerick, Republic of Ireland

DFT-based simulations are indispensable for probing nano catalyst behavior at the atomic level. By resolving each elementary step, they disentangle competing pathways, establish quantitative structure–activity relationships and guide the rational design of novel electrocatalysts. However, when applied to electrochemical systems, DFT models must also account for variables such as electrode potential, pH, temperature, adsorbate partial pressures, and surface structure. These factors collectively dictate whether chemical species adsorb, absorb into the subsurface, or incorporate into the lattice, driving nanomaterial behavior that far exceeds “simple” surface chemistry. In this talk, an overview will be given of how subsurface hydrogen affects the surface structure of the active sites. We will also discuss how the hydrogen evolution reaction pathways for Pd-based electrocatalysts are altered and provide a future outlook on the description of electrocatalysts.
[R. Lipin, M. Vandichel, ACS Energy Letters 2025, https://doi.org/10.1021/acsenergylett.5c; A. Ngoipala, M. Vandichel, E. Gubanova et al. Advanced Materials 2025, https://doi.org/10.1002/adma.202410951; etc.]


FA-1:IL12  Discovering Stable MOFs for Gas Capture with Machine Learning
H.J. KULIK, Departments of Chemical Engineering and Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of complex materials by leveraging experimental datasets. I will discuss metal-organic framework (MOF) materials and their application for catalysis as well as gas separations and storage. I will describe how we have curated a dataset of thousands of MOFs with natural language processing and LLMs that have been experimentally synthesized and used this data to train ML models to predict experimentally reported measures of stability. These models predict experimental thermal, activation, water, and acid/base stability, which would be extremely difficult to predict using computational modeling. By analyzing the curated datasets, we also uncover surprising design principles to make stable materials. I will describe how we have leveraged these models to then screen for mechanically stable materials as well as stable catalysts in the direct conversion of methane to methanol and in CO2 capture and separations. I will also describe how we have used these models to accelerate the discovery of novel stable MOFs, creating a dataset of transition metal complexes enriched with stability and diversity 1-2 orders of magnitude beyond what is typically included in hypothetical MOF sets.


FA-1:IL13  Discovery of Salt Hydrates for Thermal Energy Storage
D.J. SIEGEL, Walker Department of Mechanical Engineering, Texas Materials Institute, and Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA

Thermal energy storage (TES) has the potential to improve the efficiency of many applications but has not been widely deployed. The viability of a TES system depends upon the performance of its underlying storage material; increasing the energy density of TES materials is a helpful step in accelerating the adoption of TES systems. Salt hydrates are a promising class of thermochemical energy storage materials due to their high energy densities and reversibility. Nevertheless, relatively few hydrates have been characterized, suggesting that new compositions with superior properties may exist. Here, the energy densities, turning temperatures, and stabilities of 12,000+ hypothetical hydrates are predicted using high-throughput DFT calculations. The hydrates of several metal fluorides are identified as stable TES materials with high energy densities and operating temperatures suitable for use in domestic applications. The performance of these materials is projected to the system level by parameterizing an operating model of a solar thermal TES system. Finally, ML models for hydrate thermodynamics are used to identify design guidelines for maximizing the energy density. In sum, the materials and design rules reported here are expected to nurture the implementation of TES systems.


Session FA-2 Advanced characterization of energy materials

FA-2:IL14  Off-stoichiometry in Full Heusler Compounds: A Promising Path to Tune Thermoelectricity
M. PARZER, F. GARMROUDI, E. BAUER, Institute of Solid State Physics, TU Wien, Wien, Austria; T. MORI, International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan

Full-Heusler alloys based on Fe2VAl turn out to bear large potentials for being used in the 100°C temperature range for power generation, cooling, or wireless sensing. Superior power factors (pf) and excellent mechanical properties, the easy synthesis and the non-poisonous and reasonably priced constituting elements, as well as their respective abundance are essential assets in this respect. The present talk focuses on a specific tool to modify the electronic structure of full-Heusler compounds and thus to enhance the respective thermoelectric performance, i.e., off-stoichiometry. The underlying cubic crystal structure of such compounds with interpenetrating fcc sublattices allows on each of the constituting elements of the X2YZ formula unit to slightly shift away the atomic content from the integer number. This can dramatically modify the electronic bandstructure causing e.g., the conversion of a p-type to an n-type conductor, while mechanical properties keep unchanged. Several examples of off-stoichiometric Heusler systems will be discussed.
Research supported by the Japanese Science and Technology Agency (JST, project MIRAI).


FA-2:IL15  Critical Interface Phenomena in Nanostructured Materials for Thermoelectric Devices
D. NARDUCCI, University of Milano Bicocca, Dept. Materials Science, Milan, Italy

Internal interfaces largely rule transport properties of thermoelectric materials, either polycrystalline or nanostructured. In general terms, interfaces cause a reduction of the carrier mean free path (mfp), decreasing both thermal and electrical conductivity. Nonetheless, a wise design of materials and interfaces has allowed to decouple electric and thermal conductivity, enhancing the figure of merit ZT of many thermoelectric materials. In this talk I will focus on silicon, discussing three examples of interface engineering impacting its ZT. Phonon scattering at grain boundaries has been the oldest tool used to this aim, taking advantage of the large difference between the mfp of phonons and electrons in silicon [1]. As a second example, the effect of interfaces in holey silicon will be discussed [2], showing how neck size and pitch may be used to reduce Si thermal conductivity with marginal effects on the electrical conductivity. Finally, I will show how interfaces could also enhance power factors when energy-selective carrier scattering reduces charge carrier density while increasing their mobility [3].
[1] B. Lorenzi et al., J. Electron. Mater., 47 (2018) 5148. [2] J. Lim et al., ACS Nano, 10 (2016) 124. [3] N. Neophytou et al., EPJ B, 93 (2020) 213.


FA-2:IL16  Weighted Mobility Ratio as a Key Parameter for Understanding Narrow-Bandgap Thermoelectric Materials
HYUN-SIK KIM, University of Seoul, Seoul, Republic of Korea

Thermoelectrics, which can generate electricity from a temperature difference, or vice versa, is a key technology for solid-state cooling and energy harvesting; however, its applications are constrained owing to low efficiency. Since the conversion efficiency of thermoelectric devices is directly obtained via a figure of merit of materials, zT, which is related to the electronic and thermal transport characteristics, the aim here is to elucidate physical parameters that should be considered to understand transport phenomena in semiconducting materials. It is found that the weighted mobility ratio of the majority and minority carrier bands is an important parameter that determines zT. For nanograined Bi–Sb–Te alloy, the unremarked role of this parameter on temperature-dependent electronic transport properties is demonstrated. This analysis shows that the control of the weighted mobility ratio is a promising way to enhance zT of narrow bandgap thermoelectric materials.


FA-2:IL17  Developing X-ray Spectroscopies for Advanced Characterisation of Electrochemical Mechanisms in Battery Materials
A. LONGO, ESRF, Grenoble, France; A. IADECOLA, PHENIX, CNRS, Sorbonne Université, Paris, France; L. STIEVANO, ICGM, Univ. Montpellier, CNRS, Montpellier, France

X-ray spectroscopies have progressively become an important tool for the understanding of electrochemical mechanisms in battery materials. By following the evolution of both transition-metal and light elements states during operation, these techniques allow tracing how charge is shared, redistributed, and sometimes trapped at the heart of complex electrodes. From hard to tender and soft X-rays, and from absorption to emission and Raman scattering, each technique offers a different sensitivity to local structure and bonding, revealing the hidden interplay between cationic and anionic redox processes. Recent developments in high-energy-resolution and operando approaches now permit the observation of these transformations in real time, directly under working conditions. Through selected examples (from layered oxides to polyanionic systems) this contribution will illustrate how advanced X-ray spectroscopies can help to unveil the intimate connection between atomic rearrangements and electrochemical properties, opening new perspectives for the design of more efficient and durable battery materials.


FA-2:IL18  Advanced Cross-characterization Techniques of Phase Change Materials for Thermal Energy Storage
K. PIELICHOWSKI, Department of Chemistry and Technology of Polymers, Cracow University of Technology, Krakow, Poland

Polymeric phase change materials (PCMs) are nowadays considered as a promising group of materials for various thermal energy storage applications. PCMs can store energy in the form of sensible heat, latent heat and heat associated with a physical transition; fusion-solidification is the reversible phase-change process that is frequently utilized practical applications. In the class of polymeric PCMs, systems based on poly(ethylene oxide) (PEO) are broadly studied. PEO is a highly crystalline polymer, and the presence of ether oxygen in macromolecular structure enables specific interactions to occur, including intermolecular hydrogen bonds. Importantly, PEO does not show adverse or irritating effects on the human body. However, to fully utilize its energy storage potential, proper cross-characterization techniques should be applied, and the obtained experimental results interpreted in a complementary way. Hence, in this lecture polymeric PCMs, especially those based on PEO, will be presented, and results of investigations of the thermal behavior of PEO and its blends and composites will be discussed based on FTIR, WAXD, SEM, AFM, (MT)-DSC and TG data.


FA-2:IL19  Neutron Imaging as a Non-destructive Tool for Hydrogen Concentration Evaluation in Nuclear Fuel Cladding Tubes of Light Water Reactors
M. GROSSE, Karlsruhe Institute of Technology, Institute for Applied Materials, Applied Materials Physics, Karlsruhe, Germany

Neutron imaging can be a powerful tool for investigations of the element distribution in samples and components. Particularly, systems consisting of light elements in a heavy element matrix or elements close together in the periodic table of elements can have a large contrast because the total neutron cross section do not depend on the atomic number. An example is the zirconium hydrogen system. Hydrogen degrades the mechanical properties of zirconium alloys used as materials for nuclear fuel cladding tubes. Hydrogen is visible by neutrons in zirconium alloys even at low concentrations Because hydrogen has a large total neutron cross section and zirconium a very low one, hydrogen becomes visible even et relative low concentrations. The neutron images can be analysed full quantitatively if the correlation between total macroscopic neutron cross section and hydrogen concentration is measured using samples with known hydrogen content. The high penetration ability of the neutrons allows the application of dedicated sample environments like for instance high temperature furnaces. The paper will introduce into the basic of neutron imaging and gives examples for ex-situ and in-situ Investigations of the hydrogen behaviour in cladding tube materials made of zirconium alloys.


FA-2:IL20  Sustainable Lignin-Derived Ionic Thermoelectric Membranes and Hydrogels for Low-Grade Thermal Energy Harvesting
A. CANTARERO, University of Valencia, Valencia, Spain

The development of sustainable materials for low-grade thermal energy harvesting is essential for mitigating greenhouse gas emissions and accelerating the transition toward clean energy technologies. In this talk, we present a comprehensive study on lignin-derived ionic thermoelectric materials, demonstrating two complementary material platforms: hierarchical ionic conducting membranes and high-performance hydrogels. Lignin, an abundant by-product of the pulp and paper industry, is utilized as the primary functional component to fabricate structurally engineered ionic systems via tailored crosslinking strategies. In the first approach, lignin-derived membranes with vertically aligned hierarchical channels are synthesized through a double-network crosslinking method. The resulting architecture promotes nanoscale confinement and enhanced ion diffusion, leading to an ionic conductivity of 51.5 mS cm⁻¹, a low thermal conductivity of 0.195 W m⁻¹ K⁻¹, and an ionic Seebeck coefficient of 5.71 mV K⁻¹ under an axial temperature gradient. An optimized membrane containing 69.2 wt.% lignin and infiltrated with 0.5 M KOH achieves an ionic figure of merit (ZTi) of 0.25. A complementary numerical model validates the experimental findings and elucidates the mechanisms governing ion transport within confined aligned channels. In the second approach, lignin-derived hydrogels are developed as highly efficient ionic thermoelectric materials. Systematic optimization of lignin content, crosslinking density, electrolyte type, and concentration results in a hydrogel infiltrated with 6 M KOH exhibiting a remarkable ionic conductivity of 226.5 mS cm⁻¹ and a Seebeck coefficient of 13 mV K⁻¹. This yields an exceptional power factor of 3831 µW m⁻¹ K⁻² and a record ionic figure of merit (ZTi) of 3.75, positioning it among the most efficient sustainable ionic thermoelectric materials reported to date. Together, these findings demonstrate the versatility of lignin as a sustainable platform for ionic thermoelectric energy conversion. By coupling hierarchical structural engineering with electrolyte optimization, we establish scalable, bio-derived materials capable of efficiently harvesting low-grade waste heat. This work advances the field of sustainable thermoelectrics and opens new pathways for high-performance, environmentally responsible energy harvesting technologies.


FA-2:IL21  Ba-122 Superconducting Powders and Wires Development towards High Field Generation
A. TRAVERSO, A. LEVERATTO, F. LORIA, E. BELLINGERI, C. BERNINI, V. BRACCINI, A. MALAGOLI, CNR-SPIN, Genova, Italy; M. BORDONARO, Physics Department, Università di Genova, Genova, Italy; A. BALLARINO, The European Organization for Nuclear Research (CERN), Geneva, Switzerland

The iron-based superconductor Ba(1-x) KxFe2As2 (Ba-122), remains a top candidate for next-generation high-field magnets. The main challenge for industrial use is scaling up the Powder-In-Tube (PIT) process while maintaining high transport properties (J_c) over long conductor lengths. Transferring short-sample performance is difficult: impurities are a critical bottleneck, and PIT fabrication conventionally relies on expensive High-Pressure Heat Treatment (HPP) post-deformation to achieve the necessary core density and texture. In this work, we present an approach that overcomes these critical scalability hurdles. Firstly, we focus on an innovative and easily scalable fabrication process for synthesizing pure, homogeneous Ba-122 superconducting powders in large batches, which is a prerequisite for long-length wire manufacturing. Secondly, we demonstrate the successful implementation of the Groove-Die-Groove (GDG) cold deformation path, developed at CNR-SPIN, in the PIT technique to make Ba122 long length wires. This specific path allows us to achieve higher density and texture in the Ba-122 core without requiring any subsequent heat treatment performed under high pressure.


FA-2:IL22  Robust Spin-triplet Superconductivity in Actinide UTe2
J. PAGLIONE, University of Maryland, College Park, MD, USA

The spin-triplet superconductor UTe2 has recently emerged as a rich system that incorporates aspects of unconventional superconductivity, heavy-electron physics and non-trivial topologies. Superconductivity in UTe2 is characterized by enormous upper critical fields that exceed the paramagnetic limit and re-enter in ultra-high fields, nodal quasiparticle excitations, chiral in-gap surface states, and multiple distinct superconducting phases that span regimes of temperature, magnetic field and applied pressure. Recent advances in crystal quality have been achieved by varying synthesis methods - including variations in techniques, composition and and even temperature ranges used for growth - to enable enhancements in conductivity, transition temperature and ability to measure quantum oscillations. This talk will review the evolution of sample variations and resultant physical properties of UTe2 that shed light on the nature of superconductivity in different parameter regimes, focusing on milliKelvin measurements of thermal conductivity as a function of temperature, magnetic field angle, and sample disorder.


FA-2:IL23  Superconductivity Induced by Intercalation in SnSe2
RONGYING JIN, SmartState Center for Experimental Nanoscale Physics, Department of Physics and Astronomy, University of South Carolina, Columbia, SC, USA

Unconventional superconductivity often occurs in materials with low dimensionality. We report unusual superconductivity observed in layered AxSnSe2 with Tc ~ 6 K, which is realized by A intercalation in semiconducting SnSe2. The intercalant A can be atoms or molecules. Single crystal x-ray diffraction, Hall effect, and first-principles calculations indicate that the A concentration x can be minimal, which is at least two orders lower than the required superconductivity onset condition. More surprisingly, superconductivity is observed in both in-plane and out-of-plane directions. The implication of these results will be discussed.


FA-2:IL24  Advanced Spectroscopies for Topological Material Engineering
PEIZHE TANG, School of Materials Science and Engineering, Beihang University, Beijing, People's Republic of China

Analogues of the elementary particles have been extensively searched for in condensed-matter systems for both scientific interest and technological applications. Herein, I will show that new fermions beyond the Dirac and Weyl models, such as spin-1 excitations with 3-fold degeneracy and spin-3/2 Rarita-Schwinger-Weyl fermions, exist in a family of transition metal silicides, including CoSi, RhSi, RhGe, and CoGe, when the spin-orbit coupling (SOC) is considered. Under the circularly polarized light (CPL) pumping, topological fermions in the CoSi family could be Floquet-engineered. The intense light pumping does not compromise the gapless nature of topological fermions, but displaces the crossing points in momentum space along the direction of light propagation. Furthermore, I will show that the coherent excitation of the infrared-active phonon mode in HgTe could result in a distortion of the atomic geometry with a lifetime of several picoseconds. Four Weyl points are located exactly at the Fermi level in this nonequilibrium geometry, making it an ideal long-lived metastable Weyl semimetal. Our findings provide insights into exploring novel applications in optoelectronic devices by leveraging the degree of freedom of chirality in the non-equilibrium regime.


Session FA-3 Data science and artificial intelligence for materials development

FA-3:IL25  Discovering a New Rare-Earth-Free Magnet through Machine Learning and Quantum Simulations
J.R. CHELIKOWSKY, Oden Institute of Computational Engineering and Sciences, Departments of Physics and Chemical Engineering University of Texas, Austin, TX, USA

Modern sustainable energy technologies, including electric vehicles and wind turbines, rely on high-performance magnets such as Nd₂Fe₁₄B and SmCo₅. These magnets derive their exceptional properties from rare earth elements like Nd, Sm and Dy. However, rare earth resources are unevenly distributed, geopolitically sensitive, and environmentally costly to extract. With projected demand expected to outpace global supply of Nd and Dy, and no major breakthroughs in magnet materials since the discovery of Nd₂Fe₁₄B in the early 1980s, the need for alternatives is urgent. Developing magnets from earth-abundant elements would accelerate reduce environmental impact, and enhance energy security. To address this challenge, our work integrates artificial intelligence, adaptive genetic algorithms, density functional theory, and a magnetic materials database to discover rare earth-free magnetic compounds. Through this data-driven approach, we identified and synthesized Fe₃CoB₂, a promising new magnet with predicted high saturation magnetization and strong magnetocrystalline anisotropy, both essential traits of effective permanent magnets. Our discovery establishes a scalable framework for designing high-performance, rare earth-free magnets and advancing sustainable energy technologies.


FA-3:L26  Role of Coexisting Molecules in ZnDTP Tribofilm Formation: Molecular Dynamics Simulation based on Neural Network Potentials
HIROKI NUMATA, S. SEKITA, C. SUZUKI, S. FUKUSHIMA, Y. OOTANI, Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan; N. OZAWA, New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan; M. KUBO, Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan, and New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan

Lubricants in automotive engines contain zinc dialkyldithiophosphate (ZnDTP), an anti-wear additive that forms a protective tribofilm on sliding components. Optimizing lubricant performance requires a fundamental understanding of this formation process. While experimental studies have shown that water and oxygen in engines influence the growth of ZnDTP-derived tribofilms, observing these complex tribochemical reactions at the atomic scale in-situ remains a significant challenge, leaving the underlying mechanisms poorly understood. In this study, we employ reactive molecular dynamics (MD) simulations based on neural network potential (NNP) to elucidate how coexisting molecules affect the formation of the ZnDTP tribofilm. Our simulations reveal that the initial step of film formation involves the shear-induced decomposition of ZnDTP, leading to the adsorption of sulfur and zinc onto the iron surface. We also find that alkyl groups, generated during this decomposition, adsorb onto this surface and subsequently react with water to form alcohols. This presentation will further discuss other key reaction pathways, including the formation of phosphate networks through interactions with these coexisting molecules.


FA-3:L27  Computing Hybrid Materials: Towards Additive Construction of Functionally Graded Structures
S. NAZARIAN, Architectural Robotic Construction Laboratory (ARCL) School of Architecture, College of Architecture, Planning and Public Affairs, The University of Texas at Arlington, Arlington, TX, USA

This project introduces an AI-driven computational design framework for developing functionally graded composite materials (FGMs) that enable high-performance building components beyond the capabilities of conventional fabrication. The system integrates multi-objective optimization, parametric modeling, and machine learning to predict and control material gradients for large-scale additive manufacturing using 3D concrete printing. The goal is to optimize structural, thermal, optical, and environmental performance while minimizing embodied and operational energy. Building on prior work, the project unites two research thrusts: (1) seamless geopolymer (GP)–glass composites (U.S. Patent No. 11,414,345) achieving impermeable graded transitions between ceramics and glass, and (2) a computational framework for functionally graded cork concrete that achieved up to 26% energy savings. The proposed system replaces cement with sustainable GP—derived from metakaolin and industrial byproducts—to reduce CO₂ emissions and enable voxel-level gradient design via AI-assisted inverse modeling. This work advances computational materials design by coupling digital optimization and experimental validation, establishing a pathway toward scalable, intelligent, and energy-efficient construction systems.


FA-3:L28  ML-DFT Design of Novel Proton-conducting High-entropic Perovskite Electrolytes
M.V. BARP, J.S. VECINO-MANTILLA, L.G. ZANDAVALLI, E. GALLO, M. LO FARO, Institute of Advanced Energy Technologies (ITAE) of the Italian National Research Council (CNR), Messina, ME, Italy

The design of high-performance electrolytes is key for advancing next-generation solid oxide electrochemical cells. High-entropy perovskite oxides offer exceptional tunability and compositional flexibility, enabling the development of proton-conducting materials while reducing the use of critical raw materials. However, the vast compositional space makes it challenging to identify compounds with optimal charge carrier mobility and electrochemical performance. In this work, we present a data-driven approach combining machine learning (ML) and density functional theory (DFT) to accelerate the discovery of novel electrolytes. ML models, trained on first-principles-generated datasets, predict properties including hydration, oxygen vacancy formation and protonation energies, helping to identify promising candidates. DFT calculations then validate and provide deeper insight into proton transport. This ML-DFT workflow provides an efficient strategy for exploring complex perovskite compositions, complementing traditional high-throughput methods.
Dr. Barp acknowledges NextGeneration EU, CUPB93C22000630006; The authors also acknowledge PRIN SUPERH2, CUPE53D23009400006; PRIN PNRR OxCellenT CUPB53D23027440001; ITELECTROLAB financed by CNR; FISA SUS-CELLS, CUPB47H23004330001.


FA-3:L29  Fracture Simulations of SiO2/TiO2 Passive Films on MoSiBTiC Alloys under High-temperature Water Environment by Reactive Molecular Dynamics Method
KEAKI WATANABE, S. FUKUSHIMA, Y. OOTANI, Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan; N. OZAWA, New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan; M. KUBO, Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan, and New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan

MoSiBTiC alloys coated with SiO2/TiO2 passive films exhibit excellent high-temperature strength. However, the films can be fractured by corrosive and stress environment. Thus, elucidating the fracture mechanism is essential. In this study, we performed tensile simulations of SiO2/TiO2 film models under high-temperature water environment by the molecular dynamics method. The SiO2/TiO2 film models were prepared by embedding a rutile TiO2 particle into amorphous SiO2. Both Para-model, in which c-axis of the TiO2 is parallel to the tensile direction, and Ortho-model, in which the c-axis is orthogonal to the tensile direction, were prepared, respectively. The simulation results showed that larger voids formed in the SiO2 of the Ortho-model than that in the Para-model. In the Para-model, the TiO2 elongated uniformly, and numerous voids were formed throughout the entire film. In the Ortho-model, the TiO2 fractured and several large voids were formed around the cracked TiO2. The results indicate that fracture process of the SiO2/TiO2 film is affected by the deformation behavior of the TiO2, which depends on its anisotropy. In the presentation, we will present a fracture process analysis using persistent homology technique which is one of the topological data science analysis method.


FA-3:IL30  Computational Materials Science at Toyota Research Institute
S. TORRISI, Toyota Research Institute, Los Altos, CA, USA

Computational materials discovery campaigns are now at a scale where it is commonplace to produce many more candidates than could be experimentally verified, even with the advantage of automated laboratories. This motivates the development of tools which let us leverage computational predictions to improve the synthesis success rate by promoting or ruling out particular candidates. I will share motivating case studies from ongoing work that has occurred both internal to Toyota Research Institute and its consortium, including exploring the role of disorder, the study of phase transformations within solid-state synthesis, and finding synthesizability measures. Relevant methodologies will include DFT, the interface of DFT and experiment, high-throughput processing of data, and the use of artificial intelligence.


FA-3:L31  Role of Vacancies and Impurities on Properties of MoSi2/TiSi2 Disilicide Nanocomposites
M. VSIANSKA, J. PAVLU, M. SOB, Dept. of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic

Materials for energy-related applications rely often on combining various constituents that may form complex interfaces. Recently, it turned out that transition-metal disilicides (and perhaps their nanocomposites) are promising candidates of recombination layers in some specific solar cells. Here we investigate, with the help of ab initio analysis, the C11b (tetragonal) MoSi2/C54 (orthorhombic) TiSi2 nanocomposites containing 14 types of interfaces formed by planes with similar arrangements. We identify two most stable nanocomposite configurations, both with the formation energy (related to standard element reference states) equal to −0.615 eV/atom and with the lowest interface energies. Further, in these most stable arrangements and in one higher-energy interface configuration, we examine the effect of the impurities (Al, Si) and vacancies on their stability and structure. We find the location of these impurity atoms and Si vacancies as well as the least destabilizing divacancy configurations. Si and Al impurity simplify the formation of vacancies. As there is very little experimental information on the structure and properties of these interfaces, most of the present results are theoretical predictions which may motivate future experimental work.


FA-3:IL32  Understanding Activity and Stability of Pt Metal Alloys for the Oxygen Reduction Reaction
C.A. CAMPOS-ROLDAN, R. ALALAM, H. GUESMI, J.-S. FILHOL, R. CHATTOT, P.-Y. BLANCHARD, J. ROZIERE, D.J. JONES, S. CAVALIERE, ICGM, University Montpellier, CNRS, ENSCM, Montpellier cedex, France

Developing electrocatalysts that combine high activity with long-term stability for the sluggish oxygen reduction reaction (ORR) remains a major scientific challenge. Platinum alloys incorporating rare-earth metals (REMs) have emerged as particularly promising¹˒², but their synthesis via conventional chemical routes is particularly challenging. We synthesized a series of Pt-REM nanocatalysts using a solid-state preparation method³ and performed a comprehensive study to assess the effects of the REM species, the Pt/REM ratio⁴, and post-synthesis treatments⁵ on the structural, morphological, and electrochemical properties of the catalysts. Operando characterization allowed to monitor nanoalloy evolution under electrochemical conditions, revealing structural transformations and strain effects influencing ORR performance⁶. To address the deactivation observed during Pt-REM conditioning⁷, we are currently developing novel intermetallic transition-metal alloys, whose high alloy formation energies and strong Pt-M interactions are expected to enhance chemical/structural stability⁸. Online ICP-MS measurements combined with theoretical modeling demonstrated enhanced resistance to metal dissolution in these intermetallic catalysts⁹. In this presentation an overview of the results obtained on the different electrocatalysts will be given together with the challenges for their application at the PEMFC cathode. 
1.    J. Greeley et al., Nat. Chem., 1, 552–556 (2009).
2.    C. A. Campos-Roldán et al., ChemCatChem, 14, e202200334 (2022)
3.    Y. Hu et al., J. Am. Chem. Soc., 142, 953–961 (2020)
4.    C. A. Campos-Roldán et al., ACS Catal., 11, 13519–13529 (2021).
5.    C. A. Campos-Roldán et al., Nanoscale Adv., 4, 26–29 (2022).
6.    C. A. Campos-Roldán et al., Phys. Chem. Chem. Phys. 27, 6400–6407 (2025).
7.    C. A. Campos-Roldán et al., ACS Catal., 13, 7417–7427 (2023).
8.    F. Lin et al., Chem. Rev., 123, 12507–12593 (2023).
9.    C. A. Campos-Roldán et al., ACS Catal., 15, 17950–17957 (2025).


FA-3:L33  Predicting Ionic Motion in Solids using Transfer Learning
R. DEVI¹, K. BUTLER², S.G. GOPALAKRISHNAN¹, ¹Indian Institute of Science, Bengaluru, India; ²University College London, London, UK

Ionic mobility, which determines the rate performance of several applications, such as batteries, is exponentially dependent on the ionic migration barrier (E_m) within solids, a quantity that is difficult to measure experimentally or estimate computationally. Here, we present a graph neural network based architecture that leverages principles of transfer learning to efficiently and accurately predict E_m across a diverse set of materials. Modifying a pre-trained model on bulk material properties and adding suitable modifications to the framework, we fine-tuned our models on a manually-curated literature-derived calculated dataset of 619 E_m data points. Importantly, our best performing fine-tuned models display R^2 scores and mean absolute errors that are ~40-70% better than scratch and classical machine learning models and universal interatomic potentials. Moreover, our best model generalizes well across migration pathways, intercalant compositions, and chemistries and also acts as a robust classification tool (80% accuracy and 82.7% sensitivity in identifying good conductors). Thus, we establish a pathway for discovering novel materials with high ionic mobility as well as to predict data-scarce material properties for different applications.