Machine Learning Quantum Reaction Rate Constants for Single and Double Barrier Reactions

1,517,419 quantum reaction rate constant products computed from transmission coefficients for model single and double barrier minimum energy paths. The dataset was created by Evan Komp and Stacey Valleau at the University of Washington for a 2020 publication to train a deep neural network. It includes features like barrier widths, heights, symmetry constants, and temperature.

Use Cases

• Training deep neural networks to predict quantum reaction rate constants based on potential energy barrier parameters.
• Benchmarking machine learning models for quantum dynamics calculations using the provided log_kQ_rate target variable.
• Studying the relationship between reaction barrier properties (width, height, symmetry) and quantum rate constants across a range of temperatures.

Strengths

• Contains 1,517,419 data points, providing a substantial basis for model training.
• Includes specific physical parameters like barrier widths, heights, and symmetry constants, allowing for detailed feature analysis.
• Target variable log_kQ_rate is explicitly defined for machine learning tasks.

Limitations

• Row count for the dataset file is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.
• Description metadata is limited; actual data quality and completeness require manual inspection after download.

Provenance

Source

University of Washington, created for the publication 'Machine Learning Quantum Reaction Rate Constants'.

Collection Method

Computationally generated from transmission coefficient calculations for model potential energy paths.