Data-efficient machine learning for molecular crystal structure prediction.
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Publication Date
2021-02-11Journal Title
Chem Sci
ISSN
2041-6520
Publisher
Royal Society of Chemistry (RSC)
Volume
12
Issue
12
Pages
4536-4546
Language
eng
Type
Article
This Version
VoR
Physical Medium
Electronic
Metadata
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Wengert, S., Csányi, G., Reuter, K., & Margraf, J. T. (2021). Data-efficient machine learning for molecular crystal structure prediction.. Chem Sci, 12 (12), 4536-4546. https://doi.org/10.1039/d0sc05765g
Abstract
The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level-for which an efficient implementation is available-with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP.
Identifiers
External DOI: https://doi.org/10.1039/d0sc05765g
This record's URL: https://www.repository.cam.ac.uk/handle/1810/321618
Rights
Attribution-NonCommercial 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc/4.0/
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