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Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.
Focassio, Bruno; M Freitas, Luis Paulo; Schleder, Gabriel R.
Afiliação
  • Focassio B; Brazilian Nanotechnology National Laboratory (LNNano/CNPEM), Campinas 13083-100, São Paulo, Brazil.
  • M Freitas LP; Brazilian Nanotechnology National Laboratory (LNNano/CNPEM), Campinas 13083-100, São Paulo, Brazil.
  • Schleder GR; Brazilian Nanotechnology National Laboratory (LNNano/CNPEM), Campinas 13083-100, São Paulo, Brazil.
Article em En | MEDLINE | ID: mdl-38990833
ABSTRACT
Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Assunto da revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Assunto da revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos