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Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditiona…
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela +2
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level methods, but at a much lower computational cos…
Coulomb Interactions between Dipolar Quantum Fluctuations in van der Waals Bound Molecules and Materials
Martin Stöhr, Mainak Sadhukhan, Yasmine S. Al-Hamdani +2
Mutual Coulomb interactions between electrons lead to a plethora of interesting physical and chemical effects, especially if those interactions involve many fluctuating electrons o…
Fine-Structure Constant Connects the Polarizability of Atoms and Vacuum
Alexandre Tkatchenko, Dmitry V. Fedorov
We examine the recently derived quantum-mechanical relation between atomic polarizabilities and equilibrium internuclear distances in van der Waals (vdW) bonded diatomic systems [P…
QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
Johannes Hoja, Leonardo Medrano Sandonas, Brian G. Ernst +3
We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to se…
Dynamical Strengthening of Covalent and Non-Covalent Molecular Interactions by Nuclear Quantum Effects at Finite Temperature
Huziel E. Sauceda, Valentin Vassilev-Galindo, Stefan Chmiela +2
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the inclusion of the zero point energy and its coupling with the anharmonicities in interatomic…
Accurate Many-Body Repulsive Potentials for Density-Functional Tight-Binding from Deep Tensor Neural Networks
Martin Stöhr, Leonardo Medrano Sandonas, Alexandre Tkatchenko
We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vib…