一个基于深度学习的框架,用于选择价值债券结构和预测权重
Tao Xia1, Tingzhen Chen1, Wei Wu1
1State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.
Journal of chemical theory and computation
|October 20, 2025
概括
深度学习增强了价值键 (VB) 理论,用于复杂的化学分析. DLVB可以有效地预测VB结构,为更大,更复杂的分子提供准确的结合分析.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 机器学习在化学中的应用
背景情况:
- 价值键 (VB) 理论为结合和反应提供了化学直觉.
- 高计算成本限制了VB理论对复杂系统的应用.
研究的目的:
- 开发一个深度学习框架 (DLVB),以克服VB理论的计算限制.
- 为了能够准确地预测VB结构重量和有效地识别关键配置.
主要方法:
- 集成VB理论与图形变换器使用化学有意义的表示.
- 开发基于深度学习的选择配置交互 (SCI) 方案.
- 在没有*ab initio*计算的情况下预测VB结构重量.
主要成果:
- DLVB准确地预测了VB的结构重量.
- 基于DLVB的SCI方案有效地识别了关键的VB结构.
- 该方法在准确性和可扩展性方面优于传统的选择方法.
结论:
- DLVB为VB理论提供了一种计算效率高的方法.
- 这一框架将VB理论的适用性扩展到更大的活性空间和复杂分子.
- 为先进的结合分析提供了一条新的途径.
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