Work

Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design

Year: 2023

Type: article

Source: Matter

DOI: 10.1016/j.matt.2023.03.031

Authors: Andrew J. Lew, Cayla A. Stifler, Astrid Cantamessa, Alexandra Tits, Davide Ruffoni, Pupa U.P.A. Gilbert, Markus J. Buehler

Institutions: Massachusetts Institute of Technology, University of Wisconsin–Madison, University of Liège, Lawrence Berkeley National Laboratory

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Energy, National Sleep Foundation, Massachusetts Institute of Technology, Office of Science, Quest for Intelligence, Massachusetts Institute of Technology, Office of Naval Research, Basic Energy Sciences, Army Research Office, Lawrence Berkeley National Laboratory

Awards: G1102702650, G1109517538, Discovery of new crystallization pathways in forming biominerals, G3174313537, G419881185, G5226628132

OpenAlex ID: W4366086393

FWCI: 1.296

Cites: 38

Cited by: 18

Related to: 10