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

Preprint Aug 2026

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 through...

Rutchapon Hunkao, U. Patil, S. Sanvito · 0 citations

Preprint Aug 2026

Generalizing Abell-Tersoff bond-order potential with explicit high-order many-body correlations for robust extrapolation of potential energy surfaces

A semiparametric interatomic potential is introduced based on a generalization of the Abell--Tersoff bond-order potential, incorporating a chemically informed functional form and explicit high-order many-body correlations to address this challenge of reliability for out-of-distribution configurations far beyond the tra...

Ikuma Kohata · 0 citations

Preprint Sep 2026

Machine-Learning Exploration of Defect Topologies and Thermodynamic Stability in Graphene with Atomic Vacancies

Atomic vacancies and vacancy aggregates control the thermodynamic stability and the functional response of graphene, yet the configurational space spanned by many vacancies at variable concentration and separation is too large to be mapped exhaustively by first-principles methods. Here, we map and rationalize this stab...

M. V. D. da Costa, José R. da M. Lima, Kádila R. de S. Oliveira et al. · 0 citations

Open access Aug 2026

An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space

As the number of theoretically predicted materials continues to expand, it is increasingly important to evaluate not only their thermodynamic stability but also their kinetic resilience against transformation to competing polymorphs. In this study, we investigate the hypothesis that the kinetic persistence of a metasta...

Max C. Gallant, David Mrdjenovich, Kristin A. Persson · Journal of Chemical Theory a... · 0 citations

Open access Sep 2026

Critical benchmarking of machine-learned interatomic potentials for intermolecular and noncovalent interactions

Accurate benchmarking of intermolecular interaction energies is central to evaluating quantum chemical methods and guiding the development of reliable machine-learned interatomic potentials (MLIPs) for chemical and biological applications. In this work, we benchmark five MLIPs, namely AIMNet2(2023), AIMNet2(2025), MACE...

Kamal Singh Nayal, Ilkwon Cho, O. Isayev · Machine Learning: Science an... · 0 citations

Preprint Sep 2026

Chemical site bases and average-atom potentials for the atomic cluster expansion

Interatomic potentials are central tools in the atomistic modeling of materials. The atomic cluster expansion (ACE) parameterizes such potentials from ab initio data, conventionally encoding the chemical degrees of freedom with a one-hot representation that yields chemically stratified models. The alternative chemical...

Lorenzo Piersante, A. Natarajan · 1 citation