Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

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

Atomistic simulations of high-entropy alloys: from density functional theory to machine-learning interatomic potentials

This review aims to provide a comprehensive perspective on the ongoing transition from conventional DFT-based simulations toward scalable, statistically rigorous, and predictive atomistic modeling frameworks for HEAs and related compositionally complex materials.

Hierarchical Site‐to‐Composition Machine Learning for High‐Entropy Electrocatalysts Design

A Hierarchical Site to Composition Machine Learning (HS2C‐ML) framework to accelerate the discovery of efficient HEA catalysts for alkaline hydrogen evolution reaction (HER) using high‐entropy alloys.

A high-dimensional neural network potential for finite-temperature phenomena in NiTi martensite

We present a high-dimensional neural network potential (HDNNP) for the martensitic phase of the NiTi shape-memory alloy trained to density functional theory (DFT) data. A central aspect of this work is the systematic validation of the potential with respect to the underlying DFT reference method for key properties governing structural evolution, including equilibrium crystal structures, elastic constants, generalized-stacking fault energies, and vibrational spectra. The HDNNP accurately describes the relative stability of the B19$^\prime$ and B33 phases, including subtle energy differences on the order of meV/atom. The predicted stacking-fault energy landscape is strongly anisotropic and reveals a preferential shear pathway, providing atomistic insight into deformation and twinning mechanisms. Finite-temperature molecular dynamics simulations further enable the investigation of unconstrained structural evolution as a function of temperature. Overall, the developed HDNNP provides a robust basis for atomistic simulations of the complex structural and functional behavior of martensitic NiTi systems containing hundreds of thousands of atoms on nanosecond time scales.

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.

Machine Learning Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

The sluggish kinetics of the ammonia oxidation reaction constitute a critical bottleneck in the development of low‐temperature direct ammonia fuel cells. High‐entropy alloys (HEAs), owing to their diverse active sites, have emerged as promising catalysts. However, their vast compositional space makes traditional quantum chemical screening prohibitively expensive. In this study, we provide a computational proof of concept showing that the classical d ‐band center theory fails to predict the ammonia oxidation activity of quinary HEAs and exhibits a negligible correlation with the energy barrier of the rate‐determining step. To address this limitation, we employed the Sure Independence Screening and Sparsifying Operator (SISSO) method to construct a transparent and interpretable symbolic descriptor, achieving excellent predictive accuracy ( R 2  = 0.981) within the range of the training data. This descriptor extends beyond simple single‐electron parameters by integrating the synergistic effects of electron‐donating ability, lattice stiffness, and local electronegativity perturbations. This data‐driven approach reduces computational costs by several orders of magnitude relative to exhaustive density functional theory (DFT) screening and identifies an “isolated‐surrounded” geometric configuration as a highly active site that significantly enhances intrinsic catalytic activity from a thermodynamic perspective. Crucially, the predicted motif should be interpreted within the hydrazine‐mediated thermodynamic framework used here, and its thermodynamic superiority may shift if alternative kinetic pathways dominate under operating conditions. This structural motif promotes efficient NN coupling while suppressing site poisoning. Overall, this study provides a coordination chemistry‐based blueprint for the rational design of next‐generation catalysts with reduced platinum‐group‐metal content and offers a theoretical framework for future experimental validation.