Want to learn more about TREX? On this page, you can find our archive of informative publication materials: reports, deliverables, presentations and articles from experts within the TREX community speaking about various TREX related topics. All the public materials published by TREX partners are available on the TREX Zenodo Community: https://zenodo.org/communities/trex/
14 Dec 2020

Ab initio machine learning in chemical compound space

Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual exploration of this space, e.g.~for designing and discovering novel molecules and materials which exhibit desirable properties, is therefore prohibitive for all but the smallest sub-sets and simplest properties, and typically requires substantial allocations on modern high-performance computing hardware.

Chemical Physics (2020) https://arxiv.org/abs/2012.07502 as part of the special issue "Machine Learning in Chemistry" of Chemical Reviews

Authors: Bing Huang, O. Anatole von Lilienfeld

06 Mar 2021

Probing anharmonic phonons by quantum correlators: A path integral approach

The authors devise an efficient scheme to determine vibrational properties from Path Integral Molecular Dynamics (PIMD) simulations. The method is based on zero-time Kubo-transformed correlation functions and captures the anharmonicity of the potential due to both temperature and quantum effects.

17 Dec 2020

Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation

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 catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. Chemical Physics (2021) https://arxiv.org/abs/2012.09722 Authors: Jan Weinreich, Nicholas J. Browning, O. Anatole von Lilienfeld
18 Jan 2021

Elucidating atmospheric brown carbon -- Supplanting chemical intuition with exhaustive enumeration and machine learning

To unravel the structures of C12H12O7 isomers, identified as light-absorbing photooxidation products of syringol in atmospheric chamber experiments, we apply a graph-based molecule generator and machine learning workflow. To accomplish this in a bias-free manner, molecular graphs of the entire chemical subspace of C12H12O7 were generated, assuming that the isomers contain two C6-rings; this led to 260 million molecular graphs and 120 million stable structures. 

04 Feb 2021

Energy-free machine learning predictions of {\em ab initio} structures

The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Within conventional force-field or {\em ab initio} calculations, structure is determined through energy minimization, which is either approximate or computationally demanding.