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07/25/2023
Scaling machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points
Idealized first-principles models of chemical plants can be inaccurate. ...
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12/11/2022
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both th...
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05/17/2022
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemic...
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03/30/2022
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with m...
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09/10/2021
Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-stand...
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06/04/2021
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Machine learning has enabled the prediction of quantum chemical properti...
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05/01/2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
In recent years, machine-learned force fields (ML-FFs) have gained incre...
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02/05/2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Message passing neural networks have become a method of choice for learn...
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10/28/2020
Machine learning of solvent effects on molecular spectra and reactions
Fast and accurate simulation of complex chemical systems in environments...
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10/14/2020
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemi...
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02/17/2020
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
In recent years, deep learning has become a part of our everyday life an...
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06/02/2019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Deep learning has proven to yield fast and accurate predictions of quant...
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12/18/2018
Molecular Dynamics with Neural-Network Potentials
Molecular dynamics simulations are an important tool for describing the ...
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11/22/2018
Machine learning enables long time scale molecular photodynamics simulations
Photo-induced processes are fundamental in nature, but accurate simulati...
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10/26/2018
Generating equilibrium molecules with deep neural networks
Discovery of atomistic systems with desirable properties is a major chal...
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10/23/2018
Analysis of Atomistic Representations Using Weighted Skip-Connections
In this work, we extend the SchNet architecture by using weighted skip c...
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06/27/2018
Quantum-chemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications...
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12/15/2017
