通过基于物理的人工智能加速构成复杂材料的设计
Dierk Raabe1, Jaber Rezaei Mianroodi2, Jörg Neugebauer3
1Max-Planck-Institut für Eisenforschung, Düsseldorf, Germany. d.raabe@mpie.de.
Nature computational science
|January 4, 2024
概括
由于巨大的化学可能性和有限的AI数据,发现新材料是很困难的. 结合物理和人工智能的混合方法为加速材料发现提供了有希望的途径.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 材料设计的化学空间几乎是无限的,阻碍了基于物理的传统建模以破坏性进步.
- 当前的人工智能 (AI) 方法面临挑战,原因是培训数据稀少,用于建立组成-结构-属性关系.
研究的目的:
- 探索发现新型,化学复杂材料的机会.
- 解决材料设计中传统和仅基于人工智能的方法的局限性.
主要方法:
- 讨论混合方法,将基于物理的规律与人工智能相结合.
- 利用人工智能更有效地导航广的化学空间.
主要成果:
- 确定混合物理-人工智能方法作为材料发现的可行策略.
- 强调了人工智能在材料科学中克服数据稀疏性挑战的潜力.
结论:
- 结合物理和人工智能的混合方法显示了加速新材料发现的重大前景.
- 将已确立的物理原理与人工智能集成,可以在材料设计中打开新的前沿.
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