两步机器学习方法用于与结构多样化的数据进行电荷转移合
Hung-Hsuan Lin1,2, Chun-I Wang1,3, Chou-Hsun Yang1
1Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan.
The journal of physical chemistry. A
|December 29, 2023
概括
我们开发了一种新的机器学习方法,可以准确预测分子对中的电子合. 这种方法改善了电荷转移速率的预测,特别是在复杂的无形系统中.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 电子合决定了电荷转移速率,受到分子间和分子内因素的影响.
- 准确的预测是很困难的,特别是对于具有不同配置的无形材料.
研究的目的:
- 开发一种强大的机器学习算法,用于预测分子对中的电子合.
- 为了提高电荷转移动态建模的准确性.
主要方法:
- 采用了一种两步的内核回归模型 (KRR).
- 第一个KRR模型从结构变化中预测分子轨道 (MO).
- 第二个KRR模型使用MO重叠积分来预测合强度.
主要成果:
- 获得的平均绝对误差为0.27meV的乙烯二次体和1.99meV的乙烯对.
- 证明了显著的错误减少 (分别是14倍和3倍).
- 预测的MO可以作为其他量子化学属性计算的输入.
结论:
- 新的两步KRR方法显著提高了电子合预测的准确性.
- 这种方法提供了一种更可靠的方式来建模电荷转移动态,特别是在无形系统中.
- 该方法具有适应性,并且与其他准确的MO预测器兼容.
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