对于晶体材料的生成模型
Houssam Metni1,2, Laura Ruple2, Lauren N Walters3,4
1Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Advanced materials (Deerfield Beach, Fla.)
|February 26, 2026
概括
机器学习 (ML) 通过生成新的晶体结构来加速材料发现. 本综述调查了用于预测和设计材料的生成模型,帮助研究人员逆向设计.
科学领域:
- 凝聚物质物理学和材料科学.
- 计算材料科学.计算材料科学.
- 机器学习在科学发现中的应用.
背景情况:
- 了解材料结构与性能关系对于科学进步至关重要.
- 机器学习 (ML) 对于加速材料发现和设计越来越重要.
- 最近的重点已经从选转移到用于预测晶体结构的生成模型.
研究的目的:
- 审查用于晶体结构预测和de novo生成的生成模型的当前格局.
- 分析晶体表示,生成模型类型及其局限性.
- 为了指导实验科学家和ML专家在反向材料设计.
主要方法:
- 对各种晶体结构表示的检查.
- 概述和评估不同的晶体设计端到端生成模型.
- 对当前生成方法的优点和局限性的分析.
主要成果:
- 生成模型为新的晶体结构设计提供了强大的功能.
- 关键考虑因素包括晶体表示,模型架构和实验验证.
- 新兴领域包括建模缺陷,混乱和合成约束.
结论:
- 生成型建模正在改变反向材料设计和发现.
- 该审查提供了对模型选择,评估和未来研究方向的见解.
- 桥梁ML专家和实验科学家是实际应用的关键.
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