Inverse design for materials discovery from the multidimensional electronic density of states-Reference-Cited by-同舟云学术

Inverse design for materials discovery from the multidimensional electronic density of states

Published:2024 Issue:10 Volume:12 Page:6004-6013
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Bang Kihoon¹^ORCID,Kim Jeongrae¹²,Hong Doosun¹,Kim Donghun¹^ORCID,Han Sang Soo¹^ORCID

Affiliation:

1. Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea

2. Department of Artificial Intelligence Software Convergence, Korea Polytechnics, Chuncheon Campus, Gangwon-do, 24409, Republic of Korea

Abstract

To accelerate materials discovery, a deep learning method for inverse design of inorganic materials using multidimensional DOS properties was developed.

Funder

Samsung

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D3TA06491C

Reference59 articles.

1. Combinatorial approaches to materials discovery

2. Combinatorial Materials Science and Catalysis

3. Combinatorial and High-Throughput Materials Science

4. First principles high throughput screening of oxynitrides for water-splitting photocatalysts

5. High-Throughput Screening to Investigate the Relationship between the Selectivity and Working Capacity of Porous Materials for Propylene/Propane Adsorptive Separation

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