Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential

• URL: http://arxiv.org/abs/2505.08159v1
• Date: Tue, 13 May 2025 01:34:34 GMT
• Title: Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential
• Authors: Jiaxiang Li, Junwei Feng, Jie Luo, Bowen Jiang, Xiangyu Zheng, Jian Lv, Keith Butler, Hanyu Liu, Congwei Xie, Yu Xie, Yanming Ma,
• Abstract summary: We propose a flexible and automated workflow to build a highly generalizable and data-efficient machine learning potential.<n>The workflow is validated on both Mg-Ca-H ternary and Be-P-N-O quaternary systems.
• Score: 7.981545383991065
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding multicomponent complex material systems is essential for design of advanced materials for a wide range of technological applications. While state-of-the-art crystal structure prediction (CSP) methods effectively identify new structures and assess phase stability, they face fundamental limitations when applied to complex systems. This challenge stems from the combinatorial explosion of atomic configurations and the vast stoichiometric space, both of which contribute to computational demands that rapidly exceed practical feasibility. In this work, we propose a flexible and automated workflow to build a highly generalizable and data-efficient machine learning potential (MLP), effectively unlocking the full potential of CSP algorithms. The workflow is validated on both Mg-Ca-H ternary and Be-P-N-O quaternary systems, demonstrating substantial machine learning acceleration in high-throughput structural optimization and enabling the efficient identification of promising compounds. These results underscore the effectiveness of our approach in exploring complex material systems and accelerating the discovery of new multicomponent materials.

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