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.
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.
N. A. Anoop Krishnan, A. Pedone, Xiao-Nan Lu et al.· Journal of The American Cera...· 0 citations
The limits of equivariant MLIPs are examined, and a family of foundation potentials in the NequIP and Allegro equivariant MLIP architectures are presented which achieve leading inference speeds and strong scalability as well as excellent accuracies across a range of community benchmarks.
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang et al.· 0 citations
This paper introduces a distance measure that assesses the output of material generative models by capturing both quality and novelty in a single distribution-based evaluation framework, and introduces the Coarse-Fine Transport Distance (CFTD), which is used as guidance for a material generative model.
P. Hagemann, Katharina Ueltzen, Simon Müller et al.· arXiv.org· 0 citations
Pretrained machine-learning interatomic potentials, so-called universal or foundation models offer an appealing starting point for atomistic simulations, but their accuracy for material-specific observables often remains limited without additional reference data (fine-tuning). Here, we systematically quantify how much...
Foundation machine-learning interatomic potentials (MLIPs) enable atomistic simulations at substantially lower computational cost than first-principles methods, but their reliability across structural geometries remains insufficiently understood. Here, we construct a density-functional-theory dataset of ZrO2 configurat...
P. Zanineli, B. Focassio, G. R. Schleder· 0 citations
This work investigates several aspects of developing MLIPs for polymers, utilizing polyethylene as a representative, yet simple model system, and finds that the ACE potential accurately reproduces key thermodynamic, structural and dynamical properties.
Mirko Fischer, A. Heuer· 0 citations
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