在使用互补的能源景观的全球优化算法中生成候选者
Andreas Møller Slavensky1, Mads-Peter V Christiansen1, Bjørk Hammer1
1Department of Physics and Astronomy, Center for Interstellar Catalysis, Aarhus University, DK-8000 Aarhus C, Denmark.
The Journal of chemical physics
|July 11, 2023
概括
本研究介绍了互补的能量 (CE) 景观,以改善原子结构优化. 通过简化潜在能量表面 (PES),该方法有助于更有效地发现新的全球最低能量结构.
科学领域:
- 计算材料科学 计算材料科学
- 化学物理 化学物理
- 机器学习在化学中的应用
背景情况:
- 全球优化原子结构对于发现新材料至关重要.
- 探索潜在能量表面 (PES) 以找到全球最小值在计算上具有挑战性.
- 目前的方法需要有效的候选结构生成来导航复杂的PES.
研究的目的:
- 引入和评估使用互补能源 (CE) 景观的新型结构生成策略.
- 展示CE景观中的结构优化如何有助于全球优化.
- 报告一组橄 (Mg2SiO4) 4 的新全球最低能量结构.
主要方法:
- 使用机器学习潜力 (MLP) 制定临时补充能量 (CE) 景观.
- 构建故意不完整的MLP,比真正的PES更流.
- 在本地优化这些CE景观中的候选结构.
- 在减少的鲁SnO2(110)-(4 × 1) 表面和橄 (Mg2SiO4) 4集群上测试方法.
主要成果:
- 作为简化的MLP,CE景观具有较少的局部最小值,从而促进更顺的勘探.
- 在CE景观中的本地优化有效地指导了在真正的PES中寻找新的道的搜索.
- 为 (Mg2SiO4) 4 星团确定了一个新的全球最低能量结构.
结论:
- 互补的能源景观为增强原子结构的全球优化提供了一个有前途的战略.
- 这种方法可以加速发现稳定的材料配置.
- 该方法通过对复杂的表面和集群系统的成功应用得到了验证.
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