基于机器学习的研究,研究了结构上类似于岩建筑单元的酸寡合体的形成和演变
Xin Liu1, Yongbo Zhao1, Huimin Guo1
1School of Chemistry, State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, Dalian Key Laboratory of Intelligent Chemistry, Dalian University of Technology, Dalian 116024, P. R. China.
The journal of physical chemistry letters
|June 5, 2025
概括
一个机器学习模型预测了酸寡合体形成能量. 这揭示了凝结路径有利于类似于石建筑单元的结构,突出了它们的优越稳定性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 酸寡合体 (OSAs) 是化物形成的关键中间体.
- 了解OSA进化途径是控制热带石合成的关键.
- 目前用于预测OSA热力学的方法有限.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测OSA形成的自由能量.
- 研究OSAs的形成和演变机制,特别是那些类似于二氧化二级建筑单元 (S/CBU) 的机理.
- 阐明OSA整合到热石中的热力学驱动力.
主要方法:
- 开发一种利用SMILES字符串来预测自由能量形成的ML模型.
- 对分子间凝结 (IEC) 和分子内凝结 (IAC) 路径的计算研究.
- 分析凝结度和结构扭曲对反应强度的影响.
主要成果:
- ML模型准确地预测了OSA形成的自由能量.
- 大多数形成OSAs第一个环的IEC和IAC反应都是运动性,代表了主要的进化途径.
- 内部分子凝聚力对凝聚程度和扭曲敏感.
- 形成S/CBU-OSAs在热力学上比非S/CBU-OSAs更有利.
结论:
- 机器学习模型可用于预测复杂系统中的反应热力学.
- 类似于岩结构单位的酸寡合体具有优越的热力学稳定性.
- 这些发现提供了对OSA集成到热带石的机制性见解.
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