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Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems
Alessandro Coretti, Sebastian Falkner, Jan Weinreich +2
The paper by Noé et al. [F. Noé, S. Olsson, J. Köhler and H. Wu, Science, 365:6457 (2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model that can pr…
Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
While sophisticated numerical methods for studying equilibrium states have well advanced, quantitative predictions of kinetic behaviour remain challenging. We introduce a reactant-…
Genetic optimization of training sets for improved machine learning models of molecular properties
Nicholas J. Browning, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld +1
The training of molecular models of quantum mechanical properties based on statistical machine learning requires large datasets which exemplify the map from chemical structure to m…