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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >A Machine Learning Approach for Rate Constants. II. Clustering, Training, and Predictions for the O(P-3) + HCl -> OH plus Cl Reaction
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A Machine Learning Approach for Rate Constants. II. Clustering, Training, and Predictions for the O(P-3) + HCl -> OH plus Cl Reaction

机译：速率常数的机器学习方法。 II。 O（P-3）+ HCl - > OH加仑反应的聚类，培训和预测

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Following up on our recent paper, which reported a machine learning approach to train on and predict thermal rate constants over a large temperature range, we present new results by using clustering and new Gaussian process regression on each cluster. Each cluster is defined by the magnitude of the correction to the Eckart transmission coefficient. Instead of the usual protocol of training and testing, which is a challenge for present small database of exact rate constants, training is done on the full data set for each cluster. Testing is done by inputing hundreds of random values of the descriptors (within reasonable bounds). The new training strategy is applied to predict the rate constants of the O(P-3) + HCl reaction on the (3)A' and (3)A '' potential energy surfaces. This reaction was recently focused on as a "stress test" for the ring polymer molecular dynamics method. Finally, this reaction is added to the databases and training is done with this addition. The freely available database and new Python software that evaluates the correction to the Eckart transmission coefficient for any reaction are briefly described.
机译：在我们最近的论文上，报告了一种机器学习方法，可以在大温度范围内训练和预测热速率常数，我们通过在每个群集中使用聚类和新的高斯进程回归来呈现新的结果。每个集群由对ECKART传输系数的校正的大小定义。而不是通常的培训和测试协议，这是对当前精确速率常量的小数据库的挑战，在每个群集的完整数据集上完成培训。通过输入数百个描述符的随机值（在合理的边界内）来完成测试。应用新的训练策略以预测（3）'和（3）潜在能量表面上的O（P-3）+ HCl反应的速率常数。最近将该反应聚焦为环聚合物分子动力学方法的“应力测试”。最后，将该反应添加到数据库中，并通过此添加完成培训。简要描述了可自由的数据库和新的Python软件，用于评估对ECKART传输系数进行任何反应的校正。

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