Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
10 Nov 2023
10 Nov 2023
Historique:
medline:
30
11
2023
pubmed:
30
11
2023
entrez:
30
11
2023
Statut:
epublish
Résumé
Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design.
Identifiants
pubmed: 38035192
doi: 10.1016/j.patter.2023.100863
pii: S2666-3899(23)00247-7
pmc: PMC10682757
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
100863Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
Références
Curr Genomics. 2011 Aug;12(5):333-41
pubmed: 22294876
Nat Commun. 2016 Apr 15;7:11241
pubmed: 27079901
Philos Trans A Math Phys Eng Sci. 2016 Nov 13;374(2080):
pubmed: 27698035
Nat Rev Phys. 2022;4(12):761-769
pubmed: 36247217
Phys Rev Lett. 1996 Apr 22;76(17):3168-3171
pubmed: 10060892
Proc R Soc Lond A Math Phys Sci. 1946;186(1007):453-61
pubmed: 20998741
Nature. 2015 Feb 26;518(7540):529-33
pubmed: 25719670
IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1798-828
pubmed: 23787338
J Math Psychol. 2000 Mar;44(1):92-107
pubmed: 10733859
IEEE/ACM Trans Comput Biol Bioinform. 2014 Jan-Feb;11(1):202-18
pubmed: 26355519
Ann Appl Stat. 2012 Jan 1;6(1):334-355
pubmed: 22408712
IEEE/ACM Trans Comput Biol Bioinform. 2019 Mar-Apr;16(2):524-537
pubmed: 29990066
EURASIP J Bioinform Syst Biol. 2013 Aug 20;2013(1):10
pubmed: 23958425
IEEE Trans Signal Process. 2016 Dec 1;64(23):6243-6253
pubmed: 28824268
BMC Bioinformatics. 2015;16 Suppl 13:S2
pubmed: 26423515
Phys Rev Lett. 2015 Mar 13;114(10):105503
pubmed: 25815947
Proc Natl Acad Sci U S A. 2016 Nov 22;113(47):13301-13306
pubmed: 27821777
Sci Rep. 2018 Feb 27;8(1):3738
pubmed: 29487307
IEEE/ACM Trans Comput Biol Bioinform. 2015 Jul-Aug;12(4):938-50
pubmed: 26357334
BMC Bioinformatics. 2017 Dec 28;18(Suppl 14):552
pubmed: 29297278
J Multivar Anal. 2015 Mar;135:11-24
pubmed: 27330230
Nature. 2023 Aug;620(7972):47-60
pubmed: 37532811
J R Stat Soc Series B Stat Methodol. 2008;70(5):903
pubmed: 19603084
Phys Rev Lett. 2018 Apr 6;120(14):145301
pubmed: 29694125
IEEE/ACM Trans Comput Biol Bioinform. 2015 Nov-Dec;12(6):1304-21
pubmed: 26671803
Proc Natl Acad Sci U S A. 2005 Aug 16;102(33):11635-8
pubmed: 16087868