Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions-Reference-Cited by-同舟云学术

Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

Published:2022-08-05 Issue:16 Volume:34 Page:7323-7336
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Huo Haoyan¹²^ORCID,Bartel Christopher J.¹²^ORCID,He Tanjin¹²^ORCID,Trewartha Amalie²,Dunn Alexander¹³,Ouyang Bin¹²^ORCID,Jain Anubhav³^ORCID,Ceder Gerbrand¹²^ORCID

Affiliation:

1. Department of Materials Science and Engineering, University of California, Berkeley, 210 Hearst Memorial Mining Building, Berkeley, California 94720, United States

2. Materials Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States

3. Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States

Funder

Basic Energy Sciences

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c01293

Reference67 articles.

1. Looking into the Black Box of Solid‐State Synthesis

2. Progress toward Solid State Synthesis by Design

3. In situ studies of a platform for metastable inorganic crystal growth and materials discovery

4. Mechanistic insight of KBiQ2 (Q = S, Se) using panoramic synthesis towards synthesis-by-design

5. Kinetically Stabilized Cation Arrangement in Li 3 YCl 6 Superionic Conductor during Solid‐State Reaction

Cited by 33 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. Extraction of optimal synthesis conditions from scientific literature using a knowledge graph;Science and Technology of Advanced Materials: Methods;2024-09-02

2. From Design to Device: Challenges and Opportunities in Computational Discovery of p -Type Transparent Conductors;PRX Energy;2024-08-05

3. Novelty detection in the design of synthesis of garnet-structured solid electrolytes;Journal of Solid State Chemistry;2024-06

4. Advancing materials science through next-generation machine learning;Current Opinion in Solid State and Materials Science;2024-06

5. Computationally Guided Synthesis of Battery Materials;ACS Energy Letters;2024-05-22