第一个原则,机器学习和象征回归建模用于在二维CaO表面上吸附有机分子
1Department of Materials Physics, School of Chemistry and Materials Science, Nanjing University of Information Science & Technology, 210044, Nanjing, China.
Journal of molecular graphics & modelling
|June 15, 2023
概括
数据驱动的方法,包括机器学习和符号回归,有效地模拟有机分子在低维表面的吸附. 极化性和键类型等关键分子性质预测吸附能量,使得材料的发现速度更快.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 传统的模拟方法对于在低维表面上建模有机分子吸附是有限的.
- 数据驱动的方法为化学和材料研究提供了一个有希望的替代方案.
- 了解吸附对于设计新材料和预测化学相互作用至关重要.
研究的目的:
- 开发和应用数据驱动的方法来建模有机分子在低维的金属氧化物表面上的吸附.
- 为了比较不同的机器学习算法的性能来预测吸附能.
- 通过符号回归来识别关键的分子描述符并探索新的描述符.
主要方法:
- 密度函数理论 (DFT) 计算,以生成有机/金属氧化物接口的初始数据集.
- 机器学习算法,包括随机森林,用于预测吸附能量.
- 符号回归与基因编程相结合,用于自动描述器发现.
主要成果:
- 随机森林算法在预测吸附能量方面表现出高准确度.
- 有机吸附剂的极化性和键类型被确定为关键描述因素.
- 符号回归成功生成了新的混合描述符,提高了预测相关性.
结论:
- 建立了一个全面的数据驱动框架,用于建模和分析有机分子在低维表面的吸附.
- 机器学习和符号回归可以有效地补充材料发现的传统方法.
- 已识别的描述符为控制吸附过程的因素提供了洞察力.
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