Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications.

NIST Artificial Intelligence for Materials Science (AIMS) 2026

Predicting dielectric constants of crystalline materials using explainable machine learning and composition-aware feature engineering

Thermodynamics-Informed Machine Learning for Energy Materials Discovery

Machine learning (ML) is transforming materials discovery by enabling rapid prediction of properties that previously required computationally expensive first-principles calculations. Yet most current ML models remain fundamentally limited to zero-temperature descriptions, learning static lattice energies while neglecting the thermodynamic effects that govern materials behaviour at finite temperature. Because phase stability, functional response, and performance are governed by free-energy landscapes rather than static energies alone, this limitation represents a major barrier to predictive materials design under realistic operating conditions. In this Perspective, we argue that developing thermodynamics-informed ML constitutes one of the most important and least explored frontiers in materials discovery. We examine the fundamental shortcomings of energy-based models, highlighting the essential roles of entropy and anharmonicity in determining free energies and materials functionality. We review emerging strategies, including machine-learned interatomic potentials and hybrid ML-statistical mechanics frameworks, while identifying key challenges related to data availability, transferability, and thermodynamic consistency. Building on these advances, we outline a roadmap for thermodynamics-informed ML centred on direct free-energy learning, entropy-aware representations, and adaptive sampling across temperature. We highlight the transformative opportunities this paradigm offers for energy materials and argue that the next generation of ML models must move beyond static energy predictions towards a thermodynamic description of materials behaviour under realistic operating conditions.

Modeling dual-range atomic interactions with physicochemical principles for molecular force fields

Abstract Motivation Machine Learning Force Fields (MLFFs) have emerged as promising tools for accelerating molecular dynamics simulations. However, existing approaches often struggle to capture the geometric characteristics of long-range interactions, including distance and direction, remain sensitive to conformational variations, and lack adaptive mechanisms for balancing short- and long-range forces. To address these limitations, we propose GeoNet, a physicochemical-principle-guided framework for modeling dual-range atomic interactions. GeoNet employs geometric attention over atom–fragment bipartite graphs to characterize long-range dependencies, introduces dual-level augmentation to enforce semantic consistency across molecular conformations, and uses an adaptive fusion module to dynamically balance short- and long-range interaction pathways according to local atomic environments. Results Extensive experiments show that GeoNet consistently outperforms ten state-of-the-art baselines across the evaluated benchmarks. Moreover, it achieves the smallest model size and the shortest training time, demonstrating both superior predictive performance and computational efficiency. Availability The source code is publicly available at https://github.com/XMUDM/GeoNet.

Computational Approaches for the Discovery of New Phase-Change Materials Using Two Method Machine Learning and First-Principles Density Functional Theory

Solid-solid phase-change materials (PCMs) are attractive for thermal energy storage due to the absence of leakage and their suitability for compact and safe storage systems. However, the discovery of new solid-solid PCMs with targeted transition temperatures and high latent heat remains challenging. This work evaluates two approaches for solid-solid PCM discovery: data-driven machine-learning (ML) screening and first-principles density functional theory (DFT) modelling. ML screening identified PEG-1000-glycerol mixtures as promising candidates near room temperature, with experimental validation confirming composition-dependent solid-solid transitions (between 14.10 to 31.40 °C) absent in the pure components. Furthermore, first-principles DFT calculations, implemented using Quantum ESPRESSO, reveal low-frequency phonon instabilities in a candidate PCM (2,2-dimethylpropane-1,3-diol (DMPD)) molecular crystal, providing atomistic insight into lattice-driven phase-transition mechanisms. Together, the results highlight the complementary roles of ML for rapid candidate identification via screening of known PCMs and DFT for mechanistic characterisation, supporting a more efficient pipeline for developing solid-solid PCMs for low-temperature thermal energy storage in decarbonised energy systems.

Machine learning-assisted first-principles investigation of structural, electronic, and photovoltaic properties of perovskite materials