Work

Evaluating Machine Learning Interatomic Potentials for Accurate and Scalable Modeling of Organometallic Precursors

Year: 2025

Type: article

Source: ACS Applied Materials & Interfaces

DOI: 10.1021/acsami.5c09107

Authors: Seungpyo Kang, JunHo Song, Jinyoung Jeong, Taehyun Park, Joonghee Won, Jungim Han, Kyoungmin Min

Institutions: Soongsil University, Yonsei University, Samsung (South Korea), Samsung Advanced Institute of Technology (South Korea)

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: closed

Funders: Samsung Advanced Institute of Technology, National Research Foundation of Korea

Award: G1631130952

OpenAlex ID: W4414789529

FWCI: 0.209

Cites: 55

Cited by: 1

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