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Exploring Chemical Compound Space with Quantum-Based Machine Learning
O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko
Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the…
Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller +1
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning re…
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields…
Quantum Mechanics of Proteins in Explicit Water: The Role of Plasmon-Like Solute-Solvent Interactions
Martin Stöhr, Alexandre Tkatchenko
Quantum-mechanical van der Waals dispersion interactions play an essential role for both intra-protein and protein-water interactions -- the two main driving forces for the structu…
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
K. T. Schütt, M. Gastegger, A. Tkatchenko +2
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fa…
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) app…