Aug 2026· Journal of The American Ceramic Society· 0 citations· 154 references
TL;DR
This perspective examines three interconnected issues, namely, glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials.
Abstract
Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely, glass formation procedures, interatomic potential development, and machine learning (ML) applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for standardized validation protocols and curated benchmark datasets with complete metadata. A systematic pathway forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with ML‐based approaches. Examples include the integration of swap Monte Carlo with ML potentials, leveraging foundation models through transfer or active learning, and fine‐tuning ML potentials with experimental data. Progress depends on community awareness and efforts committed to validating models, creating reproducible protocols, and sustained data sharing.
Pre-trained or'foundational'machine-learned interatomic potentials (MLIPs) are now widely used in materials modelling. However, early pre-trained models and benchmarks have largely focused on ordered, crystalline structures, and their transferability to non-crystalline solids remains unclear. Here, we show that the amorphous state is indeed a central challenge for future universal MLIPs, based on a systematic evaluation of current mainstream models in this domain. We introduce a benchmarking framework built on a curated reference dataset of canonical amorphous systems, as well as validation for structures and properties. Our study identifies limitations in the transferability of many current pre-trained models and investigates fine-tuning strategies tailored to disordered phases. Together, our results can facilitate future applications of MLIPs in the fast-growing field of amorphous functional materials, and they provide guidance for designing next-generation training datasets and transferable atomistic models.
Natascia L. Fragapane, Volker L. Deringer· 2 citations
Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent gap between prediction and synthesis. Here, we introduce a hierarchical screening framework that combines PBE-based thermodynamic stability, efficient dynamical-stability screening enabled by universal machine-learning interatomic potentials, and SCAN-based thermodynamic refinement. Applying this protocol to the 894 stable materials previously reported in Sci. Data 9, 302 (2022), we first curate 603 unique structures, of which only 298 remain thermodynamically stable on the complete PBE phase diagrams, demonstrating the critical role of competing phases in stability assessment. Dynamical screening then identifies 166 materials stable under both harmonic-phonon and finite-temperature molecular dynamics criteria, and SCAN phase diagrams further narrow this set to 109. Finally, by combining decomposition enthalpy with chemical-space completeness, we prioritize 25 candidates as high-confidence targets for experimental synthesis. This work provides a practical protocol for translating stability predictions into experimentally actionable synthesis targets, closing a key gap in machine-learning-driven materials discovery.
Yuqi An, Sihong Zhu, Joseph H. Montoya et al.· 0 citations
Machine learning force fields (MLFFs) combine the high accuracy of first-principles methods with the high efficiency of classical force fields, offering new opportunities for atomic-level studies of inorganic crystalline materials. We systematically summarize the research progress on MLFFs, elucidate their fundamental principles and developmental history, and categorically introduce the technical characteristics of representative models and relevant benchmarking platforms. We aim to review the advantages of MLFFs in overcoming traditional computational limitations across four domains: structural prediction and optimization, physical properties, defect and interface properties, and phase transitions and kinetic processes. The challenges of MLFFs are also examined in computational efficiency and simulation scale, accuracy and generalization ability, data requirements and training samples, model interpretability, and physical constraints, which offer a reference for the research and application of MLFFs in the field of inorganic crystalline materials.
Jing Yi, Yuxin Zhan, Yuanmao Hu et al.· Physical Chemistry, Chemical...· 0 citations
The Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics, machine learning, and robotic synthesis to bridge the gap between simulation and experiment, is introduced.
Felix Arendt, T. Waurischk, Stefan Reinsch et al.· npj Computational Materials· 0 citations
Universal machine learning interatomic potentials (MLIPs) are foundation AI models transforming atomistic simulations, but their practical use remains hindered by fragmented software ecosystems, dependency conflicts, and the lack of accessible benchmarking tools. These models approach first-principles density functional theory (DFT) accuracy at a fraction of the computational cost. We introduce MLIP Studio (available at https://mlipstudio.iisc.ac.in), an open and free platform that brings more than 60 universal MLIPs into a unified interactive interface for molecules and materials. The platform enables end-to-end MLIP-driven workflows, including property prediction, geometry optimization, vibrational and equation-of-state analysis, spin-state determination, custom model deployment, and high-throughput benchmarking against reference data. Automated parity plots and sortable error tables facilitate rapid identification of element-wise outliers and problematic data points. We demonstrate that MLIP-based pre-optimization can reduce subsequent DFT optimization effort by ~33$\times$. Additionally, the application enables benchmarking of computational performance. Through a comprehensive case study involving the 2D magnetic material CrCl$_3$ on a sapphire substrate, we show how cross-model comparisons of various properties and potential-energy landscapes can guide task-specific MLIP selection. Overall, MLIP Studio lowers the barrier to the reliable use of foundation models in end-to-end research workflows, benchmarking, and education in computational chemistry and materials science.
Manas Sharma, Sudeep N. Punnathanam, A. Rajan· 0 citations
Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition temperature, conventional molecular dynamics and Monte Carlo become inefficient because equilibration relies on rare barrier-crossing events, while data-driven generative models are constrained by scarce and biased reference ensembles. Here, we introduce ATLAS, an efficient sampler that learns a diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function. Parameterized by an equivariant graph neural network, ATLAS generalizes across system size, temperature, and composition. By exploiting the time reversal of the diffusion process, it enables efficient estimation of thermodynamic quantities and steering toward target observables. In two-dimensional Kob-Andersen systems, ATLAS reproduces parallel tempering Markov chain Monte Carlo structural distributions, free energies and entropies, achieving below 0.2% free energy error in the low-temperature glass regime with over 500-fold fewer energy evaluations. In Cu-Zr and Cr-Co-Ni metallic glasses, ATLAS recovers experimentally observed short-range-order trends and steers structures toward prescribed order parameters and optimized bulk moduli. Moreover, composition-amortized pretraining outperforms composition-specific training from scratch, reduces inverse-design costs by several hundred-fold, and enables sampling with expensive universal machine learning interatomic potentials. Coupled to a large language model agent, ATLAS searches an eight-element space for high-entropy metallic glasses balancing stiffness and ductility, identifying a converged Pareto frontier within 480 oracle evaluations. Together, these results establish ATLAS as a foundation model for sampling, steering and designing amorphous materials.
Mouyang Cheng, Denis Blessing, Botao Yu et al.· 1 citation