Machine Learning at the (Nano)materials-biology Interface

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Machine learning has a long history of success in the pharmaceutical sector, helping discover and optimize new drugs and predicting useful physicochemical properties like aqueous solubility. Materials science has embraced similar approaches and transferred useful technologies from the pharmaceutical sector. Although materials are more complex than small organic molecules, ML approaches have shown impressive results in predicting the properties of materials for application in diverse fields like 2D photonics, porous materials for energy and environmental applications, and in the development of biomaterials and regenerative medicine therapies. Here, we summarize some of the challenges in ML modelling of materials and highlight some exciting recent applications.

Original languageEnglish
Title of host publicationMachine Learning in Chemistry
Subtitle of host publicationThe Impact of Artificial Intelligence
EditorsHugh M Cartwright
Place of PublicationUnited Kingdom
PublisherRoyal Society of Chemistry (RSC)
Number of pages21
ISBN (Electronic)9781839160240
ISBN (Print)9781788017893
Publication statusPublished - 2020

Publication series

NameTheoretical and Computational Chemistry Series
PublisherRoyal Society of Chemistry
ISSN (Print)2041-3181
ISSN (Electronic)2041-319X

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