DOAJ Open Access 2023

Machine-learning atomic simulation for heterogeneous catalysis

Dongxiao Chen Cheng Shang Zhi-Pan Liu

Abstrak

Abstract Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review briefly the history of atomic simulations in catalysis and then focus on the recent trend shifting toward ML potential calculations. The advanced methods developed by our group are outlined to illustrate how complex structures and reaction networks can be resolved using the ML potential in combination with efficient global optimization methods. The future of atomic simulation in catalysis is outlooked.

Topik & Kata Kunci

Materials of engineering and construction. Mechanics of materials Computer software

Penulis (3)

Dongxiao Chen

Cheng Shang

Zhi-Pan Liu

Format Sitasi

APA MLA BibTeX

Chen, D., Shang, C., Liu, Z. (2023). Machine-learning atomic simulation for heterogeneous catalysis. https://doi.org/10.1038/s41524-022-00959-5

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.1038/s41524-022-00959-5
Akses: Open Access ✓