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19922026
most citedFast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

2.4k citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing 2018 · physics.chem-phShow all

7 papers · 2 filters

physics.chem-ph2018★ 79 cited

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

Annika Stuke, Milica Todorović, Matthias Rupp +4

Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmar…

physics.chem-ph2018★ 101 cited

Elucidating the NuclearQuantum Dynamics of Intramolecular Double Hydrogen Transfer in Porphycene

Yair Litman, Jeremy O. Richardson, Takashi Kumagai +1

We address the double hydrogen transfer (DHT) dynamics of the porphycene molecule: A complex paradigmatic system where the making and breaking of H-bonds in a highly anharmonic pot…

physics.chem-ph2018★ 382 cited

i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations

Venkat Kapil, Mariana Rossi, Ondrej Marsalek +23

Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomi…

physics.chem-ph2018★ 102 cited

Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry

Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke

A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms.

physics.chem-ph2018

Interlocking mechanism between molecular gears attached to surfaces

Rundong Zhao, Yan-Ling Zhao, Fei Qi +3

While molecular machines play an increasingly significant role in nanoscience research and applications, there remains a shortage of investigations and understanding of the molecul…

physics.chem-ph2018★ 706 cited

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller +1

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. How…

physics.chem-ph2018★ 8 cited

Microsolvation of phthalocyanine molecules in superfluid helium nanodroplets as revealed by the optical line shape at the electronic origin

Stefan Fuchs, Johannes Fischer, Alkwin Slenczka +2

We investigate the solvent shift of phthalocyanine (Pc) doped into superfluid helium droplets and probed by optical spectroscopy at the electronic origin. Our present work compleme…