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Found 18 papers, 6 papers with code
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning.
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes.
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-standing challenge in chemistry.
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.
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.
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.
Machine learning of solvent effects on molecular spectra and reactions
We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction.
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.
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.
Molecular Dynamics with Neural-Network Potentials
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time.
Machine learning enables long time scale molecular photodynamics simulations
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales.
Generating equilibrium molecules with deep neural networks
Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science.
Analysis of Atomistic Representations Using Weighted Skip-Connections
In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation.
Quantum-chemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers.
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning.
Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
Machine learning has emerged as an invaluable tool in many research areas.
