Jul 2026· Solid State Sciences· Vol 181, pp. 108443· 0 citations· 40 references
Physics
Abstract
Entropy-stabilized oxides (ESOs) open access to vast multicomponent compositional spaces, but identifying promising candidates remains challenging because of the large number of possible mixtures and the need to assess their stability against competing phases. In this work, we develop a high-throughput computational framework to screen equimolar quinary ESOs in the NaCl structure type by combining density functional theory (DFT), special quasirandom structures (SQS), convex-hull thermodynamics, and supervised machine learning. A consistent reference database of binary and ternary ordered oxides, including disordered phases such as all binary cation combinations in the NaCl-type oxide, is first constructed using GGA and meta-GGA calculations. Quinary disordered phases are then described by SQS supercells and used to train machine-learning models that predict the distance to the convex hull and the corresponding stabilization temperature over the full set of 4368 possible equimolar quinary compositions generated from 16 cation species. Among the tested models, an optimized multilayer perceptron provides the best predictive performance, with a test error of about 4 kJ/mol, while requiring explicit DFT calculations for only about 10% of the quinary systems. Comparison with experimental synthesis tests and computed decomposition paths further shows that the approach captures the main stability trends and the dominant competing phases, although absolute stabilization temperatures remain affected by systematic thermodynamic approximations. These results establish an efficient route for the data-driven exploration of multicomponent oxides and provide practical guidance for the experimental search for new ESOs.
Predicting the properties of multicomponent molten salts using density functional theory (DFT) remains challenging because the spatial and temporal scales required to evaluate transport properties and phase behavior are computationally prohibitive. In this work, we develop a moment tensor potential trained using a a DFT dataset of NaCl, KCl, NaCl-KCl mixtures, and the NaK alloy, enabling large-scale molecular dynamics simulations across wide ranges of temperatures and compositions. We systematically evaluate the effect of D3 dispersion corrections and apply the resulting potential to predict liquid densities, diffusion coefficients, radial distribution functions, heat capacities, thermal conductivities, and the NaCl-KCl phase diagram. The model successfully reproduces many temperature- and composition-dependent trends. However, systematic deviations in several absolute properties persist, highlighting the importance of experimental validation and calibration. These findings support a hybrid modeling framework in which first-principles-informed machine-learning potentials provide transferable predictive capability and mechanistic insight, while experimental data incorporated during model development or subsequent engineering assessments is necessary to improve quantitative accuracy.
K. Zongo, Hao Sun, Zijian Meng et al.· 0 citations
Early thinking in the field of high entropy oxides (HEOs) emphasized their likely abundance, with combinatorial arguments hinting at a myriad of new materials. The experimental reality has proven more challenging: the stability of HEOs cannot be straightforwardly predicted based on ionic radii, lattice geometry, and charge-balancing considerations alone. In this work, we employ machine learning interatomic potentials (MLIPs) to predict the synthesizability of HEOs of the form $A_2$O$_3$ derived from a selection of trivalent cations. From nearly 500 possible compositions, we identify 16 promising candidates for experimental validation with solid-state and combustion synthesis. We discover three new HEOs in the corundum structure, including (Al,Cr,Fe,Rh,Sc)$_2$O$_3$, and one novel cation-ordered phase, (Al,Fe,Ga,Sc)$_2$O$_3$. By far the most common synthesis outcome was a mixture of competing phases, sometimes involving redox reactions. Our results also reveal profound synthesis method dependence for the final product, where qualitatively equivalent outcomes between the two synthesis methods were only observed for 3 of the 16 tested compositions. We conclude that the occurrence rate of HEOs is far rarer than initially believed and that machine learning approaches can effectively guide us to the"needle in the haystack".
Abraham A. Mancilla, O. A. Dicks, S. Aamlid et al.· 0 citations
A machine-learning workflow that couples the crystal generator MatterGen with a fine-tuned MatterSim interatomic potential to expand the candidate phase space and compute temperature-dependent phase stability with accuracy approaching density functional theory is reported.
Chen Su, Jie Lu, Yuchen Fu et al.· Journal of Physical Chemistr...· 0 citations
The vast compositional space of high-entropy alloys (HEA) holds promise for next-generation functional materials, yet its exploration is stifled by a combinatorial bottleneck: conventional first-principles accuracy is computationally prohibitive for complex disordered lattices, while black-box machine learning (ML) lacks physical interpretability. We overcome this by establishing a minimal-supercell principle, demonstrating that the magnetostructural behaviour is an emergent feature of local atomic environments rather than long-range configurations. This enables a physics-informed ML framework that inverts the conventional screening approach, transitioning from limited local identification to global design-space mapping, reducing computational time by 40 – 60% without compromising accuracy. Applying this framework to MM’X family uncovers a fundamental design dichotomy: mean electronic descriptors govern baseline phase stability, while dispersion descriptors, specifically the magnetic-moment dispersion, drive the critical transformation response. This analysis reveals an intrinsic stability-performance trade-off, where the disorder required to maximise the transformation driving force inevitably penalises thermodynamic stability. By quantifying this Pareto frontier, we propose a hierarchical tuning strategy that decouples phase transition temperature from hysteresis, providing a scalable paradigm to transform HEA exploration from serendipitous discovery into rational design.
Zhe Cui, C. Romero-Muñiz, J. Law et al.· Nature Communications· 0 citations
This work introduces a verified and interpretable pathway for high‐throughput screening of orthorhombic perovskites and provides fundamental insight into the descriptor‐property connections governing different perovskite polymorphs.
Q. Fatima, A. A. Haidry, Usaid Ahmad et al.· Advanced Theory and Simulati...· 0 citations
A data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation with large-scale virtual library generation is presented, establishing a practical route from experimental data to actionable catalyst designs.
Xuefeng Li, Haoke Qiu, Hanwen Pei et al.· Journal of Physical Chemistr...· 0 citations