Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, ZT. To accelerate the discovery of high-ZT materials, efforts have focused on identifying compounds with low thermal conductivity κ. Using a curated dataset of 71,913 entries, we show that high-ZT materials reside not only in the low-κ regime but also cluster near a lattice-to-total thermal conductivity ratio (κL/κ) of approximately 0.5. This empirically derived ratio provides a quantitative descriptor for the well-known phonon-glass electron-crystal (PGEC) design concept. Building on this insight, we construct a framework consisting of two machine learning models for the lattice and electronic components of thermal conductivity that jointly provide both κ and κL/κ for screening and guiding the optimization of TE materials. By applying this framework to 104,567 inorganic compounds, we identify 2522 ultralow-κ candidates while simultaneously evaluating their proximity to the PGEC regime. A follow-up case study on chemical doping demonstrates how the framework can qualitatively provide optimization strategies that shift pristine materials toward the empirical design target of κL/κ ≈ 0.5 while maintaining their low total κ. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework takes a critical step toward closing the gap between materials discovery and performance enhancement.

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

Two-step thermochemical fuel production, including H2O and CO2 splitting, offers a promising route to sustainable fuel manufacturing, with performance governed by redox-active oxides that enable cyclic reduction-oxidation reactions. Maximizing thermal-to-fuel conversion efficiency demands materials that simultaneously satisfy multiple stringent thermodynamic and kinetic targets. Addressing these requirements has increasingly driven materials design toward complex, multi-cation oxides, such as mixed-cation fluorites, perovskites, and high-entropy oxides, wherein composition, defect chemistry, phase stability, and morphology should be co-optimized. This creates a challenging materials optimization problem that is poorly suited to traditional trial-and-error approaches. In this review, we argue that thermochemical fuel production provides a compelling frontier for autonomous materials design and optimization. We first examine why redox-active complex oxides are difficult to develop, owing to multidimensional phase spaces, harsh operating conditions, and competing functional targets. We then discuss how high-throughput computation, automated synthesis, characterization and testing, and machine learning can be integrated into closed-loop workflows to address these challenges. Building on broader oxide materials research, we organize recent progress into a capability roadmap for complex-oxide optimization, spanning compositionally diverse synthesis, operando characterization, robotic testing, operation-condition computation, and multi-objective optimization. Finally, we outline key experimental, computational, and data challenges for building self-improving materials development platforms for materials development in thermochemical fuel production.

Low-dimensional polytellurides are of broad interest because their flexible bonding gives rise to unusual electronic states, structural instabilities, and transport phenomena, yet their average crystal structures can obscure the local tellurium motifs that actually control these properties. The electronic character of LnCu x Te2 (Ln = lanthanide) remains unresolved because electronic structure calculations based on the average crystal structure predict metallic behavior, whereas experiments consistently show semiconducting transport. Here we examine this discrepancy in LaCu x Te2 by combining low-temperature single-crystal X-ray diffraction, total-scattering analysis, electrical transport measurements, high-pressure studies, and first-principles calculations. Single-crystal diffraction at 100 K reveals an incommensurately modulated structure that is absent at room temperature, and X-ray total scattering indicates local unit-cell tripling along the direction of the apparent linear Te chains. These results show that the crystallographic infinite chain is not a real chemical entity but an average motif that masks locally distorted Te arrangements. In this picture, the anomalous transport of LaCu x Te2 is linked to chain distortion and structural disorder rather than to an idealized delocalized Te chain electronic state. Consistent with this interpretation, LaCu x Te2 exhibits semiconducting behavior over the full temperature range studied, variable-range hopping at low temperature, and quasi-linear nonsaturating magnetoresistance attributable to spatially inhomogeneous carrier mobility. Under pressure, the room-temperature structure remains unchanged and the charge transport activation energy decreases, yet metallic behavior does not emerge. Together, these results reconcile the apparent conflict between ideal-chain calculations and experiment, demonstrating that hidden local distortion within an average one-dimensional tellurium motif governs the electronic behavior of LaCu x Te2.

Photovoltaic materials facilitate the conversion of sunlight into electricity by harnessing the interaction between light and matter, offering an eco-friendly and cost-efficient energy solution. Combining data-driven approaches with static and time-dependent density-functional theories and nonadiabatic molecular dynamics simulations, we predict 14 high-performance photoabsorber materials from a family of known quaternary semiconductors. Among these, we investigate four compounds SrCuGdSe3, SrCuDyTe3, BaCuLaSe3, and BaCuLaTe3 in greater detail. Their crystal structures reveal that Cu and Gd/La/Dy possess tetrahedral and octahedral coordination environments, respectively. Such bonding features suggest that these materials would possess optical activity, defect tolerance, and stability complementary to the known high-performance photoabsorber materials with either tetrahedral (e.g., Si, GaAs, CdTe) or octahedral (e.g., methylammonium lead iodide) coordination chemistries. Hybrid density-functional theory calculations including spin-orbit coupling reveal that SrCuGdSe3, SrCuDyTe3, BaCuLaSe3, and BaCuLaTe3 possess direct band gaps of 1.65, 1.01, 1.79, and 1.05 eV, respectively. These band-gap values lie close to an optimal range ideal for visible-light absorption. Consequently, the calculated optical absorption coefficient and spectroscopic limited maximum efficiency for these compounds become comparable to, or larger than, those of crystalline silicon, GaAs, and methylammonium lead iodide. The calculated exciton binding energies for these compounds are relatively small (9-80 meV), signifying easy separation of the electron-hole pairs and hence enhanced power-conversion efficiencies. Investigations of photoexcited carrier dynamics reveal a relatively long carrier lifetime (approximately 30-40 ns), suggesting suppressed nonradiative recombination and enhanced photoconversion efficiencies. Temperature-dependent structural dynamics and electronic band energies reveal relatively low local structural fluctuation, signifying their high stability and small electronic-ionic sublattice coupling, leading to a weaker nonradiative recombination process for photogenerated charge carriers, which is beneficial for high-performance photovoltaics. We have further determined the defect-formation energies in these compounds, which show that despite the likely formation of cation vacancy and antisite defects, midgap states remain absent, making these defects nondetrimental to carrier recombination. Our theoretical predictions invite experimental verification and encourage further investigations of these and similar compounds in this quaternary-semiconductor family.

Solid-state electrolytes with high ionic conductivity and excellent interfacial stability are essential for the development of next-generation all-solid-state lithium-ion batteries. Among all known lithium-ion conductors, lithium-rich antiperovskite (LRAP) Li3ClO exhibits high ionic conductivity, mechanical processability, low cost, and promising potential for compatibility with lithium metal anodes. In this work, we systematically investigate the structural, thermodynamic, ionic transport, and interfacial electrochemical stability of LRAP Li3ClO using finite temperature first-principles calculations. Our results show that crystalline Li3ClO is a metastable phase at 0 K and becomes thermodynamically accessible at temperatures above 425 K. The antiperovskite framework enables fast Li+ migration along interconnected channels in three dimensions. At the same time, the amorphous counterpart, formed via glassification, exhibits even higher ionic conductivity due to the broader and shorter lithium-ion migration pathways. Aliovalent doping (e.g., Ba2+) further promotes lithium-ion mobility with additionally introduced mobile lithium vacancies, at the cost of a reduced band gap, especially in its amorphous phase. Moreover, interfacial modeling indicates that Li3ClO decomposes spontaneously upon direct contact with metallic lithium, which explains the self-discharge observed in previous experiments. To stabilize the interface between lithium metal and the antiperovskite Li3ClO, we propose an interface-morphogenesis strategy that introduces the metal Na phase, effectively suppressing side reactions and improving chemical stability. These findings provide insights into the design of lithium-rich antiperovskite electrolytes and promising strategies for interface engineering in solid-state lithium-ion batteries.

Short-range order (SRO) in the cation-disordered state is a controlling factor influencing the probability of finding Li 4 ${\rm Li}_{4}$ tetrahedron clusters in disordered rocksalt (DRX) cathode materials. However, the prevalent Li 4 ${\rm Li}_4$ probability below the random limit across reported DRX compositions has not been systematically investigated, active strategies to surpass the random limit of Li 4 ${\rm Li}_4$ probability are lacking, and the fundamental ordering behavior on the face-centered cubic (FCC) lattice remains insufficiently explored. This research quantitatively examines pair SRO parameters and Li x TM 4 - x ${\rm Li}_x{\rm TM}_{4-x}$ probabilities via exhaustive Monte Carlo mapping across a simplified subset of the parameter space. The results indicate that, in the disordered state, the Li 4 ${\rm Li}_4$ probability is governed by the nearest neighbor (NN) pairwise SRO parameter, and that these quantities do not necessarily represent a simple attenuation of their corresponding low-temperature long-range order, particularly for the important cases of Layered and Spinel-like orderings. Strategies are proposed to mitigate or even reverse the lithium and transition metals mixing tendency of NN pair SRO to achieve Li 4 ${\rm Li}_4$ probabilities that exceed the random limit. This study advances the fundamental thermodynamic understanding of ordering behaviors, which can be generalized to any FCC system.

Anion ordering in perovskite oxyhydrides can remain significant even in disordered states, particularly at non-dilute hydrogen concentrations. Nevertheless, hydride substitution poses challenges for accurate simulations based on density functional theory due to configurational complexity and Ti 3d reduction. Here, we develop a cluster expansion (CE) framework for SrTiO_3-xH_x incorporating HSE06 hybrid-DFT energetics. We first demonstrate that calculated mixing energies and ordering stability are highly sensitive to the choice of DFT+U, with maximum variations on the order of 100 meV/anion. We benchmark ordering energetics against HSE06 calculations and identify U = 3.3 eV as an HSE06 proxy, which enables extensive configurational exploration while limiting costly HSE06 calculations to key configurations for efficient learning of ordering energetics. Together, ground-state orderings, correlations between octahedral configurations and structural stability, and MC sampling of CE models all support a strong preference for the O_4H_2 cis configuration in SrTiO_3-xH_x, in which two hydride ions occupy first-nearest-neighbor anion sites. This cis-type preference was overlooked in previous ATiO_3-xH_x studies, despite its sizable stabilization of 200 meV per hydride comparable to reported anion-migration and polaron-formation energies. This study addresses both the previously underexplored sensitivity of CE-based ordering analyses to DFT+U and anion-ordering in perovskite oxyhydrides.

Perovskite oxides can reversibly accommodate substantial changes in oxygen stoichiometry, making them attractive for clean-energy technologies including chemical looping and oxygen storage. Despite extensive efforts to optimize their redox properties, predictive descriptors capable of assessing oxygen capacity across diverse compositions remain under development. Here, we combine experiments and first-principles calculations to establish composition and oxygen-capacity relationships in the model perovskite series LnxSr1-xCoO3. We confirm that increasing Sr2+ content promotes the formation of high-valence Co4+, expanding the cationic redox reservoir available during oxygen release and thereby enhancing oxygen capacity. In this regime, oxygen-vacancy formation energy captures the observed trend because oxygen release is primarily compensated by Co4+/Co3+/Co2+ redox. Across the rare-earth series, however, oxygen capacity decreases from La to Lu despite progressively lower oxygen-vacancy formation energies. We reveal that this counterintuitive behavior originates from an alternative charge-compensation pathway, in which lattice oxygen is partially oxidized to O1- -like species during oxygen removal. Heavy rare-earth compositions (Tb-Lu) preferentially stabilize these oxygen-hole species through distinct local bonding environments, with charge compensation involving both oxidized lattice oxygen and reduced rare-earth and cobalt cations, thereby suppressing net oxygen release despite favorable vacancy thermodynamics. We further identify average metal-oxygen bond strength, quantified by integrated crystal orbital Hamilton population, as a physically meaningful descriptor for oxygen capacity when anionic redox becomes dominant.

Accurate predictions of the properties of transition-metal oxides using density functional theory (DFT) calculations are essential for the computational design of energy materials. In this work, we investigate the anomalous reversal of the stability of structural distortions (where distorted structures go from being energetically favorable to sharply unfavorable relative to undistorted ones) induced by DFT + Ueff on Mo d orbitals in layered AMoO2 (A = Li, Na, K) and rutilelike MoO2. We highlight the significant impact of varying Ueff values on the structural stability, convex hull, and thermodynamic stability predictions, noting that deviations can reach up to the order of 100 meV/atom across these energetic quantities. We find the transitions in stability are coincident with changes in the electron localization, magnetic behavior, and volume. The anomalous reversal persists across PBE, r2SCAN functionals, and also with vdW-dispersion energy corrections (PBE + D3). In Mo-containing oxide systems, high Ueff leads to inaccurate descriptions of physical quantities and structural relaxations under artificial symmetry constraints, as demonstrated by the phonon band structures, the Heyd-Scuseria-Ernzerhof hybrid functional results, and comparisons with experimental structural data. We conclude that high Ueff values (around 4 eV and above, depending on the specific structures and compositions) might be unsuitable for energetic predictions in A-Mo-O chemical spaces. Our results suggest that the common practice of applying DFT + Ueff to convex hull constructions, especially with high Ueff values derived from fittings, should be carefully evaluated to ensure that ground states are correctly reproduced, with careful consideration of dynamic stability and possible energetically favorable distortions.

Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models lack either interpretability or fidelity for elucidating the correlation between crystal geometry and property. Here, we report a three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contribution quantification. Trained with a constructed database and retrieved datasets, our model achieves state-of-the-art accuracy and data efficiency for property prediction on gas storage, separation, and electrical conduction. Meanwhile, this approach enables the interpretation of the prediction and allows for accurate identification of significant local sites for targeted properties. Through identifying transferable high-performance sites across diverse nanoporous frameworks, our model paves the way for interpretable, symmetry-aware nanoporous materials design, which is extensible to other materials, like molecular crystals and beyond.

Ordered crystalline compounds exhibiting ultralow and glasslike thermal conductivity are both fundamentally and technologically important, where phonon quasiparticles dominate their heat transport. Understanding the microscopic mechanisms that govern such unusual transport behavior is necessary to unravel the complex interplay of crystal structure, phonons, and collective excitations of these quasiparticles. Here, we use state-of-the-art firstprinciples calculations based on quantum density functional theory to investigate the origin of experimentally measured unusually low and glassy thermal conductivity in semiconducting TlAgTe that possesses disconnected chains of Tl atoms within its three-dimensional crystalline framework made up of distorted AgTe4 tetrahedra. Utilizing a unifying framework of anharmonic lattice dynamics theory that combine phonon self-energy induced frequency renormalization, particlelike Peierls (kappa lP), and wavelike coherent (kappa lC ) thermal transport contributions, including three- and four-phonon scattering channels, we successfully explain the experimental results both in terms of magnitude and temperature dependence. Our analysis reveals that TlAgTe exhibits several localized phonon modes arising from concerted rattlinglike vibrations of Tl and Ag atoms, which show strong temperature dependence and enhanced four-phonon scattering rates that are dominated by Umklapp processes, suppressing kappa lP to ultralow values. The ensuing strong anharmonicity induced by local structural distortions, lone-pair electrons, and rattlinglike vibrations of the cations lead to a transition from particlelike behavior to wavelike tunneling (i.e., coherent) characteristics of the phonon modes above 40 cm-1, contributing significantly to kappa lC, which increases with temperature. Our analysis uncovers an important structure-property relationship, which may be used in designing materials with tunable thermal conductivity.

The Effective Bond Energy Formalism (EBEF) is used in the present work to describe the mu (mu) and sigma (6) phases in the Co-Cr-Ni-W system. This represents the first time that: i) mu is described using the EBEF, and ii) two Topologically Closed-Packed (TCP) phases are simultaneously described with the EBEF. Both phases are described with thermodynamic models that are more consistent with their real crystallography and their formation energies are determined using new Density Functional Theory (DFT) calculations. The good agreement obtained between the calculated phase diagrams and the experimental results indicates once again that the EBEF is suitable to describe complex TCP phases. Furthermore, the implementation of the EBEF led to a significant reduction in the number of adjustable/ternary parameters needed, when the present thermodynamic assessment is compared to descriptions available in the literature.

Here, we investigate PbSnS 2 , a wide band gap (1.13 eV) compound, as a promising thermoelectric material for power generation. Single crystal X‐ray diffraction analysis reveals its 2D‐layered structure, akin to the GeSe structure type, with Pb and Sn atoms sharing the same crystallographic site. The polycrystalline PbSnS 2 exhibits an intrinsically ultralow lattice thermal conductivity ( κ lat ) of 0.37 W m −1 K −1 at 573 K. However, the low carrier concentration ( n ) leads to suboptimal electrical conductivity ( σ ), capping the ZT value at 0.1. Accordingly, the halogen elements (Cl, Br, and I) are employed as the n‐type dopants to improve the n . The DFT results indicate a significant weakening of Pb/Sn─S bonds upon halogen‐doping, contributing to the observed reduction in κ lat . Our analysis indicates the activation of multiconduction band transport driven by halogen substitution. The PbSnS 1.96 Br 0.04 has a high power factor of five times that of intrinsic PbSnS 2 . Halogen‐doping weakens the Pb/Sn─S bonds and enhances the phonon scattering, leading to an ultralow κ lat of 0.29 W m −1 K −1 at 873 K for PbSnS 1.96 Br 0.04 . Consequently, PbSnS 1.96 Br 0.04 achieved a maximum ZT value of 0.82 at 873 K.

Accurate first-principles prediction of lattice thermal conductivity (κL) remains challenging in identifying materials with extreme thermal behavior. While the harmonic approximation with three-phonon scattering (HA + 3ph) is now routine, reliable κL prediction often requires higher-order anharmonic effects, including self-consistent phonon renormalization, three- and four-phonon scattering, and off-diagonal heat flux (SCPH + 3, 4ph + OD). We present a state-of-the-art high-throughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures. From 562 dynamically stable compounds, we assess the hierarchical impacts of higher-order anharmonicity. For around 60

Low-dimensional materials with charge density waves (CDW) are attractive for their potential to exhibit superconductivity and nontrivial topological electronic features. Here we report the two-dimensional (2D) chalcogenide, BaSbTe2S which acts as a new platform hosting these phenomena. The crystal structure of BaSbTe2S is composed of alternating atomically thin Te square-net layers and double rock-salt type [(SbTeS)2]2- slabs separated with Ba2+ atoms. Due to the electronic instability of the Te square net, an incommensurately modulated structure is triggered and confirmed by both single-crystal X-ray diffraction, electron diffraction, and the presence of an energy bandgap in this compound. Our first-principles electronic structure analysis and investigation of structural dynamical instability suggest that the Te network plays a dominant role in its origin. The incommensurate structure is refined with a modulation vector of q = 0.351(1)b* using an orthorhombic cell of a = 4.4696(5) Å, b = 4.4680(5) Å, and c = 15.999(2) Å under superspace group Pmm2(0β0)000 at 293 K. The modulation vector q varies as a function of both occupancy of Te in the square net and temperature, indicating the CDW order can be modulated by local distortions. The CDW can be suppressed by pressure, leading to the emergence of superconductivity with a Tc up to 7.5 K at 13.6 GPa, suggesting a competition between the CDW order and superconductivity. Furthermore, electrical transport under the magnetic field reveals the existence of compensated high mobility electron- and hole-bands near the Fermi surface (μ ∼600-3500 cm2V-1s-1), suggesting Dirac-like band dispersion.

Combinatorial synthesis and high-throughput characterization have become powerful tools to accelerate the discovery and design of novel materials. Correctly extracting information about the constituent phases and gaining materials insight from high-throughput X-ray diffraction data of combinatorial libraries is a crucial step in establishing the composition–structure–property relationship. Basic information includes the number, identity, and fraction of present phases in all the samples, while advanced information includes the lattice change, texture information, solid solution behavior, etc. Encoding domain-specific knowledge, such as crystallography, X-ray diffraction, thermodynamics, kinetics, and solid-state chemistry, into automated algorithms is crucial for the development of automated phase mapping algorithms. In this study, we present an unsupervised optimization-based solver to tackle the phase mapping challenge in high-throughput X-ray diffraction datasets. Besides leveraging robust fitting abilities of neural-network optimization algorithms, we integrated various material information, including first-principles calculated thermodynamic data, crystallography, X-ray diffraction, and texture into our automated solver. Our approach exhibits robust performance across multiple experimental datasets. We emphasize the importance of correctly integrating material information for automated solvers, contributing to the development of future automated characterization tools.

Novel Li-ion battery cathode materials with high capacity and greater compositional flexibility are essential for the growing electric vehicle market. Cathode structures with cation disorder were once considered suboptimal, but recent demonstrations have highlighted their potential in Li1 + xM1 - xO2 chemistries with a wide range of metal combinations M. By relaxing requirements of maintaining ordered Li diffusion pathways, countless multi-metal compositions in LiMO2 may become viable, aiding the quest for high-capacity cobalt-free cathodes. A challenge presented by this freedom in composition space is designing compositions that possess specific, tailored types of both long- and short-range orderings, which can ensure both phase stability and Li diffusion. Ordering design frameworks are proposed based on computational ordering descriptors, which in tandem with low-cost heuristics and elemental statistics can be used to simultaneously achieve compositions that possess favorable phase stability as well as configurations amenable to Li diffusion. Utilizing this computational framework, accompanied by illustrative synthesis and characterization experiments, we not only demonstrate the design of LiCr0.75Fe0.25O2, showcasing initial charge capacity of 234 and 320 mAhg-1 in its 20% Li-excess variant Li1.2Cr0.6Fe0.2O2, but also present the elemental ordering statistics for 32 elements, informed by one of the most extensive first-principles studies of ordering tendencies.

We present the development of a machine-learning (ML) model for predicting the congruency of compound melts by utilizing a combination of density functional theory-calculated formation energies and a database of experimental melting reactions. Among the various ML models tested, the XGBoost model is found to be the most suitable. Therefore, the XGBoost model is trained with a labeled compound database to determine whether a compound melted congruently. Feature importance from the trained model is extracted and compared to identify descriptors crucial for predicting melting behavior. Notably, the change in slope of the DFT convex hull at the composition of the compound (i.e., the “convex hull sharpness”) exhibited significantly higher importance than the other features. The methodology employed in this study has the potential to enhance the design of alloy processing and can be applied to more complex higher-order alloys.

Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate high-throughput harmonic phonon calculations using machine learning universal potentials (MLIPs) combined with an efficient training dataset generation strategy. Instead of computing phonon properties from a large number of supercells with small atomic displacements of a single atom, our approach uses a smaller subset of supercell structures where all atoms are randomly displaced by 0.01 to 0.05 & Aring;, significantly reducing computational costs. We train a state-of-the-art MLIP based on multi-atomic cluster expansion (MACE), on a comprehensive dataset of 2738 materials with 77 elements, totaling 15,670 supercell structures, computed using high-fidelity density functional theory (DFT) calculations. The trained model is validated against phonon calculations for a held-out subset of 384 materials, achieving a mean absolute error (MAE) of 0.18 THz for vibrational frequencies from full phonon dispersions, 2.19 meV/atom for Helmholtz vibrational free energies at 300K, as well as a classification accuracy of 86.2% for dynamical stability of materials. A thermodynamic analysis of polymorphic stability in 126 systems demonstrates good agreement with DFT results at 300 K and 1000 K. In addition, the diverse and extensive high-quality DFT dataset curated in this study serves as a valuable resource for researchers to train and improve other machine learning interatomic potential models.