58 citations · 82 across the 9 of their papers we have counts for
9 papers
Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
Jan Weinreich, Konstantin Karandashev, Daniel Jose Arismendi Arrieta +2
We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound's utility as a lithium battery electrolyte. The first pr…
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…
Exact sampling of molecules in chemical space
Jan Weinreich, Konstantin Karandashev, Guido Falk von Rudorff
The concept of molecular similarity appears in many machine-learning algorithms based on the assumption that molecules with similar representations will also share similar properti…
Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design
Konstantin Karandashev, Jan Weinreich, Stefan Heinen +4
Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast…
Encrypted machine learning of molecular quantum properties
Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary with…
Ab initio machine learning of phase space averages
Jan Weinreich, Dominik Lemm, Guido Falk von Rudorff +1
Equilibrium structures determine material properties and biochemical functions. We propose to machine learn phase-space averages, conventionally obtained by {\em ab initio} or forc…
Properties of α-Brass Nanoparticles II: Structure and Composition
Jan Weinreich, Martín Leandro Paleico, Jörg Behler
Nanoparticles have become increasingly interesting for a wide range of applications, because in principle it is possible to tailor their properties by controlling size, shape and c…
Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
Jan Weinreich, Nicholas J. Browning, O. Anatole von Lilienfeld
Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes or homogeneous cata…
Properties of -Brass Nanoparticles I: Neural Network Potential Energy Surface
Jan Weinreich, Anton Römer, Martín Leandro Paleico +1
Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and c…