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多态晶体结构预测与自适应空间组多样性控制
Sadman Sadeed Omee1, Lai Wei1, Sourin Dey1
1Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29201, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 26, 2025
概括
一个新的算法ParetoCSP2,增强了对无机材料的晶体结构预测 (CSP). 它准确地识别了材料的多态性,加速了具有所需性质的新材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 晶体学 晶体学是指结晶学.
背景情况:
- 晶体材料表现出多态性,不同的结构 (多态性) 决定独特的物理性质.
- 通过计算预测这些多形态对于材料发现和理解稳定性至关重要,但目前对无机材料的算法是有限的.
- 现有的晶体结构预测 (CSP) 方法经常与无机多态物相斗争,需要改进预测能力.
研究的目的:
- 介绍ParetoCSP2,一种新的多目标遗传算法,旨在增强无机材料的多态晶体结构预测 (CSP).
- 通过提高材料结构预测的准确性,融合速度和多样性来解决当前CSP算法的局限性.
- 提供一种计算工具,有助于理解材料稳定性,并指导发现具有特定性质的新材料.
主要方法:
- 开发了ParetoCSP2,一个多目标的遗传算法,结合了自适应空间组多样性控制和年龄适应的帕雷托优化.
- 利用神经网络的原子间潜力来指导进化过程,并防止特定空间群体的过度代表.
- 实施了改进的人口初始化策略和代结构放松,以增强收并防止过早收.
主要成果:
- 帕雷托CSP2在多形态预测方面表现出色,在空间组和结构相似性方面取得了高准确性,对于具有简单多形态的材料.
- 该算法显著优于基线方法,准确度提高了2.46至8.62倍,常规CSP的关键性能指标提高了44.8至87.04%.
- 与现有方法相比,ParetoCSP2有效地缓解了过早的融合,并改善了整体的融合速度.
结论:
- 帕雷托CSP2代表了计算材料科学的重大进展,用于预测无机材料的多态性.
- 算法的准确性和速度的有效性促进了合理的设计和新材料的发现.
- 帕雷托CSP2的开源可用性 (https://github.com/usccb.edu/ParetoCSP2) 促进了该领域的进一步研究和开发.
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