通过机器学习加速稳定材料的预测.
Sean D Griesemer1, Yi Xia1,2, Chris Wolverton3
1Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
Nature computational science
|January 4, 2024
概括
机器学习 (ML) 通过预测其特性来加速稳定材料的发现,克服传统计算和实验方法的局限性. 本次审查强调了ML在预测零和有限温度稳定性方面的进展.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 机器学习 机器学习
背景情况:
- 高通量材料的发现受到计算成本的限制,特别是在不同条件下的复杂材料.
- 基于物理的模拟和实验往往无法用于大规模调查.
- 机器学习 (ML) 为材料建模提供了一个快速而强大的替代方案.
研究的目的:
- 审查最近应用ML方法的进展,以预测材料稳定性.
- 专注于ML在预测零和有限温度稳定性的有效性.
- 在材料稳定性预测中确定未来ML开发的领域.
主要方法:
- 对材料稳定性预测中ML应用的最新文献的综述.
- 专注于机器学习框架和数据利用,用于预测热力学特性.
- 分析ML在预测零和有限温度稳定性方面的性能.
主要成果:
- ML在预测材料稳定性参数方面是有效的,加速了新稳定的材料的发现.
- 在基于ML的零和有限温度稳定性预测方面取得了显著进展.
- 现有的ML方法显示出克服材料科学中的计算障碍的前景.
结论:
- 机器学习是加速材料发现和预测稳定性的强大工具.
- 为了预测压力和表面能量等其他关键热力学因素,需要进一步开发ML.
- 机器学习集成对于有效探索广的材料空间至关重要.
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