Reaction-Depth Learning Enables Apparent Yield Extrapolation in Catalyst Informatics.
TL;DR
A reaction-depth learning framework for the interpretable extrapolative prediction of catalytic performance that predicts latent reaction-depth variables associated with a predefined stoichiometric reaction network from catalyst and reaction condition inputs.
Abstract
Extrapolative prediction of high-yield catalyst performance remains challenging because conventional direct-yield machine-learning models do not explicitly represent the target, side, and competing reactions that determine the final yield. Here, we propose a reaction-depth learning framework for the interpretable extrapolative prediction of catalytic performance. Instead of directly predicting product yield, the model predicts latent reaction-depth variables associated with a predefined stoichiometric reaction network from catalyst and reaction condition inputs. Outlet flows are reconstructed through the stoichiometric matrix, expressing predictions as pathway-specific reaction progress. Applied to an oxidative coupling of methane data set, this representation transformed apparent extrapolation in C2-yield space into interpolation or near-interpolation in reaction-depth space. The reaction-depth model achieved a Spearman rank correlation of 0.51 in the extrapolation region, whereas conventional direct-yield models showed near-zero correlations. Reaction networks inconsistent with the feed environment frequently produced negative reconstructed outlet flows, indicating that such violations can diagnose reaction-network validity.