Electron density learning of non-covalent systems
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
- Laboratory of Computational Science and Modeling, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
DOI10.24435/materialscloud:2019.0071/v1 (version v1, submitted on 28 October 2019)
How to cite this entry
Alberto Fabrizio, Andrea Grisafi, Benjamin Meyer, Michele Ceriotti, Clemence Corminboeuf, Electron density learning of non-covalent systems, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0071/v1.
Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density ρ(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrödinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or very large chemical systems, such as peptides and proteins. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying ρ(r) in a diverse ensemble of sidechain–sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.
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|552.6 KiB||All the structures (xyz format) of the molecules included in the training and the test set.|
|15.6 MiB||All the reference density coefficients of the molecules included in the training and the test set.|
|102.7 MiB||All the predicted density coefficients of the molecules included in the test set.|
|504.4 KiB||Data (structure and coefficients) to reconstruct the density of the extrapolated biomolecules.|
|880 Bytes||More detailed description of the files content|
28 October 2019 [This version]