AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.
J Chem Inf Model
; 63(6): 1756-1765, 2023 03 27.
Article
em En
| MEDLINE
| ID: mdl-36897781
ABSTRACT
This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8% for energy and 28.6% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO2. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8% for Al and even 81.2% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Ligas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China