Stefan Chmiela

TL;DR: In this article, a deep tensor neural network is used to predict atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure.

TL;DR: The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.

TL;DR: A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.

TL;DR: In this article, the authors present an overview of applications of ML-based force fields and the chemical insights that can be obtained from them, and a step-by-step guide for constructing and testing them from scratch is given.