Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics

@article{Yang2017LearningRK,
  title={Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics},
  author={Qian Yang and Carlos A. Sing-Long and Evan J Reed},
  journal={Chemical Science},
  year={2017},
  volume={8},
  pages={5781 - 5796}
}
We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). 
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15 Citations
Background Citations
2
Methods Citations
4

15 Citations

Data-Driven Methods for Building Reduced Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations
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Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events.
Atomic-level features for statistical learning of the chemical kinetics from molecular dynamics simulations
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Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events and it is found that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.
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Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ1-regularization.
TLDR
A new data-driven method utilizing ℓ1-regularization for model reduction of nonlinear dynamical systems, which involves minimal parameterization and has polynomial-time complexity, allowing it to easily handle large-scale systems with as many as thousands of components in a matter of minutes.
Heuristics for chemical species identification in dense systems.
A new approach to identify chemical species from molecular dynamics (MD) simulations of reacting materials under extreme temperatures and pressures is presented. The approach is based on…
Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory
Hydrocarbon pyrolysis is a complex process involving large numbers of chemical species and types of chemical reactions. Its quantitative description is important for planetary sciences, in…
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming…
Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics
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This paper advocates the paradigm of data-driven discovery for extract- ing governing equations by employing fine-scale simulation data and advocates a sparse Bayesian learning perspective which avoids overfitting and reveals the most salient features in the CG evolution law.
Quantum Material Synthesis by Reinforcement Learning
Designing conditions for experimental synthesis is the primary bottleneck for the realization of new functional quantum materials. Current strategies to synthesize new promising materials with…
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