A Sparse Bayesian Committee Machine Potential for Oxygen-Containing Organic Compounds

Chemistry, Organic Chemistry, Physical Chemistry, Computational Chemistry, Chemical Engineering, Materials Science,
machine learning   Bayesian committee machine   oxygen-containing organic compounds   interatomic potentials   computational chemistry  

Academic Background In the fields of materials science and chemistry, understanding the properties of materials at the atomic level is crucial. However, traditional methods for calculating interatomic potentials, such as Density Functional Theory (DFT), while highly accurate, are computationally expensive and difficult to apply to large-scale syste...

Geometry-enhanced pretraining on interatomic potentials

Chemistry, Computational Chemistry, Information Science, Computer Science, Artificial Intelligence,
machine learning   molecular dynamics   graph neural networks   self-supervised learning   interatomic potentials  

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...