Towards fully ab initio simulation of atmospheric aerosol nucleation
arXiv:2107.04802 · doi:10.1038/s41467-022-33783-y
Abstract
Atmospheric aerosol nucleation contributes to more than half of cloud condensation nuclei globally. The emissions, properties and concentrations of atmospheric aerosols or aerosol precursors could respond significantly to climate change. Despite the importance for climate, the detailed nucleation mechanisms are still poorly understood. The ultimate goal of theoretical understanding aerosol nucleation is to simulate nucleation in ambient condition, hindered by lack of accurate reactive force field. Here we propose the reactive force field for nucleation systems with good size scalability based on deep neural network. The huge computational costs from direct molecular dynamics in ambient conditions are surmounted by bridging the simulation in the limited box with cluster kinetics, facilitating the aerosol nucleation simulation to be fully ab initio. We found that the acid-base formation rates previously based on hard sphere collision rate constants tend to be underestimated up to several times. These findings show that the widely recognized acid-base nucleation observed in the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber experiments, pristine and polluted environments should be revisited to considering the contribution of collision enhancement. Besides, the framework here is transferable to other nucleation systems, potentially boosting the nucleation parameterizations accuracy generally to effectively advance the climate model predictions reliability.
References in corpus (18)
- Escaping free-energy minima
- On representing chemical environments
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- Machine Learning Force Fields
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
- DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
- Machine learning for molecular simulation
- Big Data meets Quantum Chemistry Approximations: The -Machine Learning Approach
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
- Neutral molecular cluster formation of sulfuric acid dimethylamine observed in real time under atmospheric conditions
- freud: A Software Suite for High Throughput Analysis of Particle Simulation Data
- Neural Network Based in Silico Simulation of Combustion Reactions
- A deep potential model with long-range electrostatic interactions
- Perspective on integrating machine learning into computational chemistry and materials science
- Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
- Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
- Towards fully ab initio simulation of atmospheric aerosol nucleation