RMG Database for Chemical Property Prediction
RMG Database for Chemical Property Prediction
复制标题
DOI：
10.1021/acs.jcim.2c00965
复制
发表时间：
2022-10-12
影响因子：
5.6
通讯作者：
Green，William H.
中科院分区：
文献类型：
--
作者：
Johnson，Matthew S.;Dong，Xiaorui;Green，William H.
介绍了用于化学性质预测的反应机理生成器（RMG）数据库。RMG数据库由精心策划的数据集和估计器组成，用于准确预测构建各种化学动力学机制所需的参数。这些数据集和估计器大多已发布，并能够预测热力学，动力学，溶剂化效应和传输特性。对于热化学预测，RMG数据库包含45个热化学参数库，其组合为4564个条目和具有9种类型校正的基团加和方案，包括自由基，多环，以及具有1580个总策划组和参数的表面吸收校正，用于使用来自一组> 130 000 DFT计算到10 000个高质量值。修正方案的溶剂溶质效应，重要的热化学在液相中，是可用的。它们包括195种纯溶剂和152种常见溶质的列表值，以及用于预测任意溶质性质的组加和性方案。对于动力学估计，该数据库包含92个动力学参数库，其中包含组合的21000个反应，并包含在8655个策划的训练反应上训练的87个反应类的速率规则方案。其他库和估计器可用于传输属性。所有这些信息都可以通过https://rmg.mit.edu上的图形用户界面轻松访问。批量或动态使用可以通过直接与可以从Anaconda安装的RMG Python包接口来促进。RMG数据库为动力学家提供了方便的访问，以估计他们需要建模和分析动力学系统的许多参数。这有助于加速和促进动力学分析，通过实现对途径的简单假设检验，通过提供用于模型构建的参数，以及通过提供对来自其他来源的动力学参数的检查。
The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent–solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.