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Interatomic Potentials for Small Molecule Radical Reactions

小分子自由基反应的原子间势

基本信息

批准号：
2276986
负责人：
金额：
--
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2276986
关键词：
Interatomic Potentials Small Molecule Radical

项目摘要

Year 1: Generic training activities for all first-year student members of the CDT.Year 2-4: Machine-learned interatomic potentials are emerging tools in atomistic modelling. They bridge the gap between the accurate yet slow quantum-mechanical (QM) methods and computationally cheap, but not satisfyingly accurate classical force fields. Although they have been applied to modelling a range of materials and organic molecules, their use in modelling reactions is yet to be explored.ML potentials are built on reference databases consisting of three-dimensional structures and energies and forces estimated with a highly accurate QM methods. As a result they are only applicable to structures not too dissimilar to ones the force fields were fitted to. Consequently, a lot of attention is devoted to composing the training data sets. A recent development is to use an imperfect potential to explore the chemical space and improve the potential with collected data. The challenge in our case is to determine how to similarly gather data for small organic molecules and radicals for the relatively constrained problem we are trying to solve.We will narrow the scope of "modelling reactions of small molecules and radicals" by focusing on hydrogen abstraction reactions of methoxy radical and drug-like molecules, relevant to Cytochromes P450 metabolism. To model reactions, one would need to gather representative data for the equilibrium geometries of reactants and products as well as data sampled from the reaction path between them.

第一年：为CDT的所有一年级学生成员举办的一般培训活动。第2-4年：机器学习的原子间势是原子建模中的新兴工具。它们弥补了精确但速度慢的量子力学（QM）方法和计算量小但精确度不高的经典力场之间的差距。虽然它们已被应用于模拟一系列的材料和有机分子，它们在模拟反应中的使用还有待探索。ML潜力是建立在参考数据库，包括三维结构和能量和力估计与一个高度准确的QM方法。因此，它们只适用于与力场拟合的结构没有太大不同的结构。因此，大量的注意力都集中在训练数据集的组成上。最近的发展是使用不完美的潜力，探索化学空间和提高潜力与收集的数据。在我们的案例中，挑战是确定如何为我们试图解决的相对受限的问题类似地收集有机小分子和自由基的数据。我们将通过关注与细胞色素P450代谢相关的甲氧基自由基和药物样分子的夺氢反应来缩小“小分子和自由基的建模反应”的范围。为了模拟反应，需要收集反应物和产物的平衡几何结构的代表性数据，以及从它们之间的反应路径采样的数据。