Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices-Reference-Cited by-同舟云学术

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

Published:2020-05-19 Issue:12 Volume:32 Page:4954-4965
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Wang Anthony Yu-Tung¹^ORCID,Murdock Ryan J.²,Kauwe Steven K.²,Oliynyk Anton O.³^ORCID,Gurlo Aleksander¹^ORCID,Brgoch Jakoah⁴^ORCID,Persson Kristin A.⁵⁶^ORCID,Sparks Taylor D.²^ORCID

Affiliation:

1. Fachgebiet Keramische Werkstoffe/Chair of Advanced Ceramic Materials, Technische Universität Berlin, 10623 Berlin, Germany

2. Department of Materials Science & Engineering, University of Utah, Salt Lake City, Utah 84112, United States

3. Department of Chemistry & Biochemistry, Manhattan College, Riverdale, New York 10471, United States

4. Department of Chemistry, University of Houston, Houston, Texas 77204, United States

5. Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States

6. Department of Materials Science, University of California Berkeley, Berkeley, California 94720, United States

Funder

Welch Foundation

Deutscher Akademischer Austauschdienst

Deutsche Forschungsgemeinschaft

U.S. Department of Energy

National Science Foundation

Manhattan College

BIMoS graduate school of the Technische Universit?t Berlin

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.0c01907

Reference136 articles.

1. Machine Learning Directed Search for Ultraincompressible, Superhard Materials

2. Robust and synthesizable photocatalysts for CO2 reduction: a data-driven materials discovery

3. Accelerating the discovery of materials for clean energy in the era of smart automation

4. Unsupervised word embeddings capture latent knowledge from materials science literature

5. Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

Cited by 261 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of long-term creep modulus of thermoplastics using brief tests and interpretable machine learning;International Journal of Solids and Structures;2024-11

2. Interface Nucleus Complementarity: An Iterative Process for the Discovery of Modular Intermetallics Guided by Chemical Pressure;Journal of the American Chemical Society;2024-09-09

3. Advancing CO₂RR with O-Coordinated Single-Atom Nanozymes: A DFT and Machine Learning Exploration;ACS Catalysis;2024-09-09

4. Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent;Micromachines;2024-08-26

5. Supervised machine learning for multi-principal element alloy structural design;Materials Science and Technology;2024-08-16