晶体组合变压器:用于材料的生成和修补设计的自学神经语言模型
Lai Wei1, Qinyang Li1, Yuqi Song1,2
1Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29201, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|August 5, 2024
概括
一个新的材料填空语言模型 (BLMM) 晶体转换器使无机材料的生成设计成为可能. 这种人工智能模型学习"材料语法"以进行高效,可解释和高质量的材料发现和修改.
科学领域:
- 人工智能的人工智能是人工智能.
- 材料科学 是一种材料科学.
- 计算化学是一种计算化学.
背景情况:
- 自主监督的神经语言模型在生物和分子序列分析方面表现出色.
- 现有的模型往往缺乏可解释性,并且没有针对生成设计进行优化.
- 基于口罩的预训练模型与材料设计的独特挑战作斗争.
研究的目的:
- 介绍一种用于无机材料设计的新型概率生成模型.
- 开发一种能够同时产生新材料和建议修改 (修饰) 的模型.
- 在人工智能驱动的材料发现中提高解释性和数据效率.
主要方法:
- 开发了一种填空材料语言模型 (BLMM) 晶体变压器.
- 从自然语言处理适应的空白填写技术学习材料语法.
- 利用无监督的变压器语言模型用于基于人工智能的生成设计.
主要成果:
- 在产生的材料中实现了高化学有效性 (89.7%的电荷中性,84.8%的平衡电子负性).
- 在伪随机抽样基线上表现出优越的性能.
- 成功地应用了BLMM来发现通过密度函数理论 (DFT) 计算验证的新材料.
结论:
- BLMM为生成材料设计提供了一种强大,可解释和数据效率高的方法.
- 该模型通过学习化学原理来促进材料修补和兴奋剂.
- 这项工作将无监督的变压器语言模型集成到无机材料发现中,并提供支持的网络应用程序.
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