Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange-correlation functionals.

Machine-learning approach for the phase stability and mechanical properties of disordered alloys at finite temperature

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.

Training and validation of the interatomic potential for Al-Sc alloy based on deep-learning framework

A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions.

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.

Exploring the con fi guration space of elemental carbon with empirical and machine learned interatomic potentials

Interpretable machine learning for Curie temperature prediction of magnetic materials: compositional descriptors, shap analysis, and a perovskite case study

The Northeast Materials Database is leveraged to develop machine learning models that predict magnetic materials with targeted Curie temperatures from composition-derived descriptors rooted in molecular-level elemental properties, supplemented by a small set of coarse crystal-system and structure-family indicators.

Electron Alchemy with Machine-Learned Interatomic Potentials: Case Studies of Local Charge in Bond Dissociation Curves.

The convergence of molecular dynamics simulations and machine-learned interatomic potentials (MLIPs) promises density functional theory (DFT) level accuracy at near-classical force-field computational costs. However, while the average fidelity to reference energies and forces approaches perfection, several failure modes limit MLIP reliability in production simulations. These include spurious bond formation, inconsistent reproduction of long-range interactions, and inconsistent spin-state references. Here, the origins of these behaviors are studied by benchmarking the UMA, ORB, MACE, and AIMNet2 models against reference DFT bond dissociation curves for an illustrative range of species. These benchmarks reveal that models without explicit atomic charge resolution predict spurious stable bonds between like-charged halide anions, effectively transmuting two Cl- ions into neutral Cl2. Models with atomic partial charge equilibration correctly predict repulsion in these systems. Conversely, several secondary limitations are exposed in these benchmarks, including inconsistent agreement with unrestricted DFT (uDFT) versus restricted DFT (rDFT) energies and inconsistent core-region treatment. This comparative analysis suggests that, while artifacts related to core repulsion and asymptotic electrostatics are readily repairable through improved physical priors and better data curation, the issue of spurious bond formation is intrinsic to the inability of global charge specification to disambiguate similar local geometries at different charge and spin states.