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.
Abstract
Phase diagrams encode the thermodynamic equilibria that govern alloy processing, but finite-temperature construction remains slow because candidate phases must be identified and their Gibbs free energies evaluated accurately. We report 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. As demonstrated for the Li-Ga-Sn ternary system, the workflow constructs 0 and 300 K Gibbs phase-equilibrium diagrams and predicts a temperature-induced switch near the 3Li-2Ga-2Sn composition from the {LiGaSn, LiGa, Li8Sn3} assemblage to {LiGaSn, LiGa, LiSn} at approximately 230 K. X-ray diffraction of two synthesized compositions supports the predicted room-temperature assemblages. The approach offers a practical route for scalable finite-temperature phase-diagram construction and thermodynamic screening of intermetallic systems.
The prediction of stable alloys forming solid-state solutions across large portions of the composition space is a serious theoretical challenge, since one has to evaluate the Gibbs free energy, including both configurational and vibrational contributions. This requires an energy theory capable of extremely high throughput. By taking the Ni-Pd system as prototype, we construct an efficient Jacobi-Legendre machine-learning potential based on density-functional-theory data, which provides accurate energies and forces across the entire composition space. Based on a cluster expansion up to three-body terms and only 873 trainable parameters, this allows us to compute the partition function by directly integrating all accessible microstates, differing for composition, atomic configuration and thermal agitation. We confirm that Ni and Pd are fully miscible, forming an $fcc$ solid-state solution. This is only metastable at room temperature, while becomes thermodynamically stable at around 600~K, with the stability achieved first at the Pd-rich end of the composition range. Interestingly, entropy and heat capacity analysis reveal a competition between the solid-state solution and two intermetallic phases with long-period L1$_0$ structure for NiPd and NiPd$_3$. All in all, our approach offers a powerful and high-throughput workflow for the study of disordered alloys, an approach that can be extended to multi-component systems such as high-entropy alloys.
Rutchapon Hunkao, U. Patil, S. Sanvito· 0 citations
Thermodynamic integration (TI) is a widely used approach for computing free energies and phase diagrams. However, TI calculations driven by machine learning interatomic potentials (MLIPs) remain technically challenging because they require careful design of reversible integration paths and many closely related molecular dynamics (MD) tasks for each phase and state point. To address these challenges, we present dpti, an open-source Python package that automates TI workflows for phase diagram calculations with MLIPs. dpti connects reference systems with analytically known free energies to MLIP-described atomic and molecular solids and liquids through reversible integration paths. Given JSON input files, dpti generates and runs the required MD tasks, computes free energy contributions, estimates errors, and propagates coexistence points into phase boundaries. We demonstrate the usage of dpti with two examples driven by Deep Potential models: a silica phase diagram involving beta-quartz, coesite, and melt, and the ice Ih-liquid water phase boundary. dpti provides a useful tool for automated phase diagram calculations of materials modeled by MLIPs.
Fengbo Yuan, Xin Zhong, Donghao Zheng et al.· 0 citations
It is argued that developing thermodynamics-informed ML constitutes one of the most important and least explored frontiers in materials discovery and that the next generation of ML models must move beyond static energy predictions towards a thermodynamic description of materials behaviour under realistic operating conditions.
Pol Benítez, Cibr'an L'opez, Claudio Cazorla· 0 citations
Accurate prediction of a material's melting temperature is critical for materials design and high-temperature applications. In this work, we investigate melting behavior across a deliberately selected set of elemental metals spanning systems where cohesive-energy trends suggest that PBE and PBEsol are expected to perform differently, as well as cases where their performance is ambiguous. Melting temperatures are computed using the two-phase coexistence (TPC) approach in conjunction with a machine-learned interatomic potential based on the moment tensor potential (MTP) framework, enabling large-scale simulations that minimize finite-size effects and ensure sufficient equilibration. The TPC-MTP results reveal a clear functional dependence in the predicted melting temperatures. PBE provides good agreement for several lighter elements, whereas PBEsol gives the best overall agreement across the full dataset. However, the element-resolved trends are not governed by cohesive energy alone, indicating that liquid-phase energetics, anharmonicity, and finite-temperature phase stability also contribute to the observed functional dependence. For intermediate and structurally complex systems, both functionals exhibit less systematic performance. Overall, this study provides a systematic assessment of functional-dependent melting temperature predictions, highlighting both the strengths and limitations of the combined TPC-MTP methodology and underscoring the need for carefully selected exchange-correlation treatments in high-accuracy melting-point simulations.
Pandu Wisesa, Christopher M. Andolina, W. Saidi· Journal of Chemical Physics· 0 citations
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.
Sébastien Junier, C. Barreteau, David B'erardan et al.· Solid State Sciences· 0 citations
This work evaluates two approaches for solid-solid PCM discovery: data-driven machine-learning screening and first-principles density functional theory (DFT) modelling, highlighting the complementary roles of ML for rapid candidate identification via screening of known PCMs and DFT for mechanistic characterisation.
Mohamed Katish, V. Ferrandiz-Mas· Eurotherm seminar #119: Cont...· 0 citations