机器学习金属表面反应性动力学的原子间潜力,基于反应概率的代精细化
Wojciech G Stark1, Julia Westermayr1, Oscar A Douglas-Gallardo1
1Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
The journal of physical chemistry. C, Nanomaterials and interfaces
|December 27, 2023
概括
这项研究使用集体学习和不确定性量化来改进机器学习模型来预测铜表面的反应,揭示了当前表面动态模型的局限性.
科学领域:
- 表面科学是一门科学.
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
背景情况:
- 分子的表面反应对于储能和燃料电池至关重要.
- 准确的理论预测这些反应是计算要求很高.
- 机器学习潜力是有前途的,但需要强大的数据生成和不确定性评估.
研究的目的:
- 开发和应用一个集体学习方法,对气体表面动态进行不确定性量化.
- 调查SchNet和PaiNN模型对铜上的散射的性能.
- 评估模型不确定性对预测反应概率的影响.
主要方法:
- 通过集体学习生成适应性培训数据.
- 对于反应概率的完全不确定性量化 (UQ).
- 应用和比较SchNet和PaiNN传递信息的神经网络.
主要成果:
- 基于集群的UQ确定了SchNet对气体表面动态的不变特征表示的局限性.
- 训练数据的代改进提高了模型的可靠性.
- 这项研究为在表面反应动态中强大的机器学习提供了一个框架.
结论:
- 与UQ一起学习对于在表面化学中可靠的机器学习至关重要.
- 在这个特定的气体表面动态问题上,PaiNN表现比SchNet更好.
- 对于表面反应的机器学习潜力的特征表示需要进一步开发.
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