Uncertainty-Driven Deep-Ensemble Temporal Convolutional Networks for Predicting Chemical Reaction Dynamics

Chemical reaction dynamics, critical to advancing technologies in energy, environment, and materials, can in principle be captured via reactive molecular dynamics (RMD). Time-series machine learning algorithms enable prediction of species evolution information hidden in these simulations, but long-horizon autoregressive accuracy often degrades due to error accumulation, and generating sufficiently large and diverse RMD data sets across operating conditions is computationally expensive. Here, we propose DEAL-TCN (deep-ensemble active learning with temporal convolutional networks), an active-learning framework that effectively disentangles the high-dimensional complexity of reaction dynamics spanning species, time, and operating conditions. It adopts the query-by-committee strategy to select informative conditions and leverages one-dimensional temporal convolutions to capture interspecies and long timescale couplings, enabling efficient modeling and long-term prediction of species evolution. In a prototypical case study of Mo-O-S precursors, DEAL-TCN robustly identifies and accurately predicts the concentration evolution of all involved chemical species across orthogonally designed test sets spanning a broad parameter range (1100-1500 K, 2-6 atm, and a feed ratio of 1/25-1/15). Given only the first 50 ps of each trajectory as input, the model attains a mean prediction error of 4.8% at the picosecond level while maintaining a mean error of 18.2% over the subsequent 0.45 ns, representing improvements of ∼29% and 24% over baseline LSTM and Transformer architectures, respectively. Meanwhile, DEAL-TCN outperforms random sampling in 98.8% of active-learning iterations with the same labeling budget. These results underscore DEAL-TCN's potential as a scalable and generalizable approach for mechanistic discovery, reaction design, and optimization.