Rare event schemes require an approximation of the probability of the rare event as a function of system state. Finding an appropriate reaction coordinate is typically the most challenging aspect of applying a rare event scheme. Here we develop an artificial intelligence (AI) based reaction coordinate that effectively predicts which of a limited number of simulations of the Solar System will go unstable using a convolutional neural network classifier. The performance of the algorithm does not degrade significantly even 3.5 billion years before the instability. We overcome the class imbalance intrinsic to rare event problems using a combination of minority class oversampling, increased minority class weighting, and pulling multiple non-overlapping training sequences from simulations. Our success suggests that AI may provide a promising avenue for developing reaction coordinates without detailed theoretical knowledge of the system.
@article{arxiv.2401.12180,
title = {AI can identify Solar System instability billions of years in advance},
author = {Dorian S. Abbot and J. D. Laurence-Chasen and Robert J. Webber and David M. Hernandez and Jonathan Weare},
journal= {arXiv preprint arXiv:2401.12180},
year = {2024}
}