A call for caution in the era of AI-accelerated materials science

被引：2

作者：

Li, Kangming ^{[1
]}

Kim, Edward ^{[1
,2
]}

Fehlis, Yao ^{[3
]}

Persaud, Daniel ^{[1
]}

DeCost, Brian ^{[4
]}

Greenwood, Michael ^{[5
]}

Hattrick-Simpers, Jason ^{[1
]}

机构：

[1] Univ Toronto, Dept Mat Sci & Engn, 27 Kings Coll Cir, Toronto, ON, Canada

[2] Cohere, 171 John St, Toronto, ON, Canada

[3] Adv Micro Devices Inc, 7171 Southwest Pkwy, Austin, TX USA

[4] NIST, Mat Measurement Lab, 100 Bur Dr, Gaithersburg, MD USA

[5] Canmet Mat, Canmet MAT, 183 Longwood Rd South, Hamilton, ON, Canada

来源：

MATTER | 2023年 / 6卷 / 12期

关键词：

D O I：

10.1016/j.matt.2023.10.027

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

It is safe to state that the field of matter has successfully entered the fourth paradigm, where machine learning and artificial intelligence (AI) are universally seen as useful, if not truly intelligent. AI's utiliza-tion is near-ubiquitous from the prediction of novel materials to reducing computational overhead for material simulations; its value has been demonstrated time and again by both theorists and experimentalists. There is, however, a worrying trend toward large datasets and overparameterized models being all we need to accelerate science through accurate and robust machine learning systems.

引用

页码：4116 / 4117

页数：2

共 50 条

[41] Materials Science and Engineering - MSE 2012-Call for Abstracts
不详
INTERNATIONAL JOURNAL OF MATERIALS RESEARCH, 2011, 102 (12) : 1520 - 1520
[42] Foundational biomedical informatics research in the clinical and translational science era: a call to action
Payne, Philip R. O.
Embi, Peter J.
Niland, Joyce
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2010, 17 (06) : 615 - 616
[43] Scientific AI in materials science: a path to a sustainable and scalable paradigm
DeCost, B. L.
Hattrick-Simpers, J. R.
Trautt, Z.
Kusne, A. G.
Campo, E.
Green, M. L.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2020, 1 (03):
[44] How AI application in pharmaceutical industries is beneficial to materials science
Suzuki, Hideo
Kurosawa, Shin
Marcella, Stephen
Kanba, Masaru
Koretaka, Yuichi
Tsuji, Akio
Okumura, Toshiyuki
JOURNAL OF PHYSICS D-APPLIED PHYSICS, 2022, 55 (24)
[45] Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery
Kareem S. Aggour
Andrew Detor
Alfredo Gabaldon
Varish Mulwad
Abha Moitra
Paul Cuddihy
Vijay S. Kumar
Integrating Materials and Manufacturing Innovation, 2022, 11 : 467 - 478
[46] Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery
Aggour, Kareem S. S.
Detor, Andrew
Gabaldon, Alfredo
Mulwad, Varish
Moitra, Abha
Cuddihy, Paul
Kumar, Vijay S. S.
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION, 2022, 11 (04) : 467 - 478
[47] Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials
David B Brough
Daniel Wheeler
Surya R. Kalidindi
Integrating Materials and Manufacturing Innovation, 2017, 6 : 36 - 53
[48] Convergence of computational and experimental chemistries driving the new era of accelerated materials discovery
Hedrick, J.
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2015, 250
[49] A Materials Screening Methodology for Scaled Non-Volatile Memory in the AI Era
Lanzillo, Nicholas A.
Robison, Robert R.
2019 IEEE ALBANY NANOTECHNOLOGY SYMPOSIUM (ANS), 2019,
[50] Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials
Kalidindi, Surya R.
INTERNATIONAL MATERIALS REVIEWS, 2015, 60 (03) : 150 - 168

← 1 2 3 4 5 →