使用MACE-MP机器学习的原子间电位模型
Jamal Abdul Nasir1, Jingcheng Guan1, Woongkyu Jee1
1Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK. c.r.a.catlow@ucl.ac.uk.
Physical chemistry chemical physics : PCCP
|September 2, 2025
概括
机器学习的潜能准确地模拟了二氧化的多态和石,预测了相位过渡和离子的行为. 这证明了MACE机器学习潜力的有效性,用于各种材料模拟.
科学领域:
- 材料科学
- 计算化学
- 矿物物理
背景情况:
- 由于其结构多样性和稳定性,在矿物学和工业中非常重要.
- 计算机建模对于理解和酸盐的结构功能关系至关重要.
- 现有的原子间电位 (IP) 在处理不同协调数方面存在局限性.
研究的目的:
- 应用MACE机器学习潜力 (MACE MP) 来建模质和多态相转换.
- 评估MACE MP在处理不同协调状态中的多功能性.
- 通过实验和密度函数理论 (DFT) 数据验证MACE MP的准确性.
主要方法:
- 使用MACE机器学习的原子间潜力 (MACE MP) 进行模拟.
- 模拟的化的框架能量.
- 模拟了多态二氧化的高压相变 (石英,coesite,stishovite).
- 在化物中研究离子的行为.
主要成果:
- MACE MP 准确地复制了与α-石英相对的化的转移稳定性.
- 计算的能量差异与实验热量计数据密切匹配.
- 高压模拟显示了石英,coesite和stishovite的不同压缩行为.
- 预测的石英石和石石的相变压与实验值保持一致.
- MACE MP成功捕获了化物中的离子相互作用,包括五度协调单位.
结论:
- MACE MP是一个可靠的工具,用于模拟多态的结构和能量特性.
- 该研究验证了现成的机器学习基础模型对材料的适用性.
- 在地球科学,电子学和催化学方面,MACE MP具有广泛的适用性.
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