Evidence map›Paper›PMID 42329421›Full record

ArticleJournal of molecular modeling2026

TGMin-MLP: a new package for predicting the structures of the mono- and poly-nuclear clusters by the machine learning method.

Yu Cheng, Shu-Wen Zhang, Xin-Yu Dai, Yang-Yang Zhang

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Article in Journal of molecular modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yu ChengSchool of Rare Earths, University of Science and Technology of China, Hefei, 230026, China.
Shu-Wen ZhangSchool of Rare Earths, University of Science and Technology of China, Hefei, 230026, China.
Xin-Yu DaiSchool of Rare Earths, University of Science and Technology of China, Hefei, 230026, China.
Yang-Yang ZhangSchool of Rare Earths, University of Science and Technology of China, Hefei, 230026, China. yyzhang@gia.cas.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

contextNuclear power provides a sustainable energy with high efficiency and develops fast in the past decades. To process the nuclear wastes, the behaviors, structures, coordination number, and average radius of solvated actinide ions are required to be explored theoretically due to the high-level radioactivity. Herein, we developed the TGMin-MLP package and trained the machine learning potentials (MLP) for the mono-nuclear americium-water clusters. Then, the trained MLP was applied to searching the most stable structures of both mono-nuclear Am(H

methodsWe developed the TGMin-MLP package to search the most stable structures of clusters by using the well trained MLP, and investigate the coordination number and average radius of Am

Indexed as

ClusterGlobal minimumMachine learningStructureTGMin-MLP

Identifiers

PMID42329421

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