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Found 11 papers, 5 papers with code
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.
Ranked #6 on
Formation Energy
on QM9
Drug Discovery
Formation Energy
Chemical Physics
Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces
It is used to run finite-temperature molecular dynamics simulations, and to determine the infrared spectra and the hydrogen transfer rates for the three molecules.
Chemical Physics
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.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies.
Ranked #6 on
Drug Discovery
on QM9
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.
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations.
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes.
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods.
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations.
E3x: $\mathrm{E}(3)$-Equivariant Deep Learning Made Easy
This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group $\mathrm{E}(3)$, consisting of translations, rotations, and reflections of three-dimensional space.
