Idealized first-principles models of chemical plants can be inaccurate. ...
SchNetPack is a versatile neural networks toolbox that addresses both th...
Molecular dynamics (MD) simulations allow atomistic insights into chemic...
In recent years, the prediction of quantum mechanical observables with
m...
The rational design of molecules with desired properties is a long-stand...
Machine learning has enabled the prediction of quantum chemical properti...
In recent years, machine-learned force fields (ML-FFs) have gained incre...
Message passing neural networks have become a method of choice for learn...
Fast and accurate simulation of complex chemical systems in environments...
In recent years, the use of Machine Learning (ML) in computational chemi...
In recent years, deep learning has become a part of our everyday life an...
Deep learning has proven to yield fast and accurate predictions of
quant...
Molecular dynamics simulations are an important tool for describing the
...
Photo-induced processes are fundamental in nature, but accurate simulati...
Discovery of atomistic systems with desirable properties is a major chal...
In this work, we extend the SchNet architecture by using weighted skip
c...
With the rise of deep neural networks for quantum chemistry applications...
We introduce weighted atom-centered symmetry functions (wACSFs) as
descr...