30 citations · 34 across the 8 of their papers we have counts for
8 papers
A cost-effective strategy of enhancing machine learning potentials by transfer learning from a multicomponent dataset on ænet-PyTorch
An Niza El Aisnadaa, Kajjana Boonpalit Robin van der Kruit, Koen M. Draijer +4
Machine learning potentials (MLPs) offer efficient and accurate material simulations, but constructing the reference ab initio database remains a significant challenge, particularl…
Machine Learning Potential Powered Insights into the Mechanical Stability of Amorphous Li-Si Alloys
Zixiong Wei, Nongnuch Artrith
Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive an…
Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Lingyu Kong, Jielan Li, Lixin Sun +7
Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexi…
Constructing and Compressing Global Moment Descriptors from Local Atomic Environments
Vahe Gharakhanyan, Max Aalto, Aminah Alsoulah +2
Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potential…
Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes
Chuntian Cao, Matthew R. Carbone, Cem Komurcuoglu +15
Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in…
ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training
Jon Lopez-Zorrilla, Xabier M. Aretxabaleta, Inwon Yue +3
In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extensio…