Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics
Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics
复制标题
极化化学的机器学习：访问化学动力学
DOI：
--
复制
发表时间：
2023
影响因子：
15
通讯作者：
Paul Erhart
中科院分区：
文献类型：
--
作者：
C. Schäfer;J. Fojt;Eric Lindgren;Paul Erhart
在有限的光学环境中改变化学反应性和材料结构的研究正在兴起，然而，对微观机制的结论性理解仍然难以捉摸。这主要源于这样一个事实，即准确预测实际分子的已解集合的振动和反应动力学是一项不小的努力，加上（集体）强光-物质相互作用并不能简化问题。在这里，我们建立了一个基于机器学习（ML）模型组合的框架，使用密度泛函理论计算和分子动力学进行训练，以加速此类模拟。然后，我们应用该方法评估了1-苯基-2-三甲基硅基乙炔脱保护反应的强耦合、反应速率常数的变化及其对焓和熵的影响，这在之前的实验和从头算模拟中都有研究过。虽然我们发现与关键实验观察的定性一致，特别是关于动力学的变化，我们也发现与以前的理论预测相比的差异。机器学习加速和从头算模拟所符合的特征显示了实验估计的动力学行为。相互矛盾的特征表明，动态电子极化对反应过程的贡献比目前认为的更相关。我们的工作展示了机器学习在极化化学中的实际应用，讨论了常见近似的局限性，并为更全面地描述极化化学铺平了道路。
Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light–matter interaction does not simplify matters. Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using ab initio simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and ab initio simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant than currently believed. Our work demonstrates the practical use of ML for polaritonic chemistry, discusses limitations of common approximations, and paves the way for a more holistic description of polaritonic chemistry.
登录
查看更多内容
影响因子：
56.9
作者：
Chen, Teng-Teng;Du, Matthew;Xiong, Wei
通讯作者：
Xiong, Wei
影响因子：
--
作者：
J. Ponder;D. Case
通讯作者：
J. Ponder;D. Case
影响因子：
16.6
作者：
Lindoy, Lachlan P.;Mandal, Arkajit;Reichman, David R.
通讯作者：
Reichman, David R.
影响因子：
16.6
作者：
Campos-Gonzalez-Angulo, Jorge A.;Ribeiro, Raphael F.;Yuen-Zhou, Joel
通讯作者：
Yuen-Zhou, Joel
影响因子：
19.4
作者：
Arul, Rakesh;Grys, David-Benjamin;Chikkaraddy, Rohit;Mueller, Niclas S.;Xomalis, Angelos;Miele, Ermanno;Euser, Tijmen G.;Baumberg, Jeremy J.
通讯作者：
Baumberg, Jeremy J.