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Scalable molecular dynamics on CPU and GPU architectures with NAMD
American Institute of Physics | 2020| -
A generally applicable atomic-charge dependent London dispersion correction
American Institute of Physics | 2019| -
SchNet – A deep learning architecture for molecules and materials
American Institute of Physics | 2018| -
Recent developments in the general atomic and molecular electronic structure system
American Institute of Physics | 2020| -
Implicit self-consistent electrolyte model in plane-wave density-functional theory
American Institute of Physics | 2019| -
B97-3c: A revised low-cost variant of the B97-D density functional method
American Institute of Physics | 2018| -
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
American Institute of Physics | 2018| -
Hierarchical modeling of molecular energies using a deep neural network
American Institute of Physics | 2018| -
r2SCAN-3c: A “Swiss army knife” composite electronic-structure method
American Institute of Physics | 2021| -
TheoDORE: A toolbox for a detailed and automated analysis of electronic excited state computations
American Institute of Physics | 2020| -
The MRCC program system: Accurate quantum chemistry from water to proteins
American Institute of Physics | 2020| -
ωB97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation
American Institute of Physics | 2016|
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