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Found 3 papers, 2 papers with code
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods.
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy.
Chemical Physics Computational Physics Data Analysis, Statistics and Probability
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
Computational Physics
