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Inverse molecular design using machine learning: Generative models for matter engineering

Sanchez-Lengeling, Benjamin, Aspuru-Guzik, Alán·2018·Science
DOI 10.1126/science.aat2663
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Abstract

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

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Metadata

Authors
Sanchez-Lengeling, B., & Aspuru-Guzik, A.
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2018-07-27
Volume
361
Issue
6400
Pages
360-365
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.aat2663
OpenAlex
W2883583109
Cited by
2,050
References
17
FWCI
58.55
Type
journal article
Language
en
Open access
closed
Funders
Dr. Anders Fröseth

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.