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Found 3 papers, 1 papers with code
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability.
Learning Collective Variables with Synthetic Data Augmentation through Physics-inspired Geodesic Interpolation
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs.
Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions
This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions.
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