用自优化机器学习势自动设计复杂材料，大幅提速且减少人工干预。
Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
- 基于注意力神经网络势，自动迭代优化模型以提升泛化能力。
- 在近千万构型上验证，速度远超第一性原理计算。
- 适合需要高效探索多组分功能材料的研究者使用。
机器学习原子间势能已革新复杂材料设计，通过具备从头算精度的晶体结构预测实现材料构型空间的快速探索。然而，确保对未知结构的稳健泛化能力，以及减少对专家知识和耗时人工干预的需求仍是关键挑战。本文提出一种基于注意力耦合神经网络势的自动化晶体结构预测框架，通过采样势能面局部极小值区域，使自演化流程能够自主迭代优化势能模型，同时最大限度减少人工介入。该工作在Mg-Ca-H三元体系与Be-P-N-O四元体系中进行了验证，共探索近1000万种构型，相较于第一性原理计算展现出显著加速效果。结果表明，该方法在加速复杂多组分功能材料的探索与发现方面具有高度有效性。
原文摘要 · Abstract (English)
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural networks potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the self-evolving pipeline autonomously refines the potential iteratively while minimizing human intervention. The workflow is validated on Mg-Ca-H ternary and Be-P-N-O quaternary systems by exploring nearly 10 million configurations, demonstrating substantial speedup compared to first-principles calculations. These results underscore the effectiveness of our approach in accelerating the exploration and discovery of complex multi-component functional materials.
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