Recurrent Neural Network/Machine Learning Predictions of Reactive Channels in H+ + C2H4 at ELab = 30 eV: A Prototype of Ion Cancer Therapy Reactions

Abstract

We present a simplest-level electron nuclear dynamics/machine learning (SLEND/ML) approach to predict chemical properties in ion cancer therapy (ICT) reactions. SLEND is a time-dependent, variational, on-the-fly, and nonadiabatic method. In SLEND, nuclear and electronic parameters determine reactants-to-products trajectories in a quantum phase space; this establishes a mapping between reactants' initial conditions and products' properties. To accelerate simulations, SLEND/ML utilizes a modicum of SLEND trajectories to train ML methods on the aforesaid mapping and employs them to predict chemical properties. We employ SLEND/ML to predict reaction types and products' charges in H⁺ + C₂H₄ at E_Lab = 30 eV, a prototype of ICT reactions involving double-bonded compounds. For reaction predictions, a recurrent neural network (RNN) and k-nearest neighbor method are the best models with 98.23% and 95.13% accuracy. RNN correctly predicts frequent and infrequent reaction types and generalizes over data sets. For charge predictions, the RNN exhibits low mean absolute errors of 0.02–0.07.