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Found 9 papers, 5 papers with code
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks
Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions.
BIGDML: Towards Exact Machine Learning Force Fields for Materials
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.
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
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science.
Chemical Physics
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Ranked #6 on
Formation Energy
on Materials Project
Formation Energy
Chemical Physics
Materials Science
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
Ranked #1 on
Time Series
on QM9
