Work

Interpretable Machine Learning of Hydrogen Production from Reactive Molecular Dynamics Simulations of Polyethylene Oxidative Gasification

Year: 2026

Type: article

Abstract: Reactive molecular dynamics (ReaxFF MD) simulations of polyethylene (PE) oxidative gasification generate large trajectory datasets suited to machine learning (ML). We recast H2 production monitoring as a leakage-free forecasting task: given the simulated thermochemical state at time t, predict the time-weighted cumulative H2 production cumH2(t+h) a fixed horizon h=25 ps ahead. Data from 48 trajectories spanning six O2 loadings (1–900 molecules per 50 PE chains) and eight temperatures (1500–2900 K) yield 72,117 forecast pairs (45 simulations; three truncated trajectories excluded). Eight tabular regression models are benchmarked against persistence and physics-informed baselines under simulation-grouped nested cross-validation, a held-out test partition, temperature- and loading-holdout stress tests, and a paired ablation removing all H2-derived inputs. With the H2 history available, the best test model (random forest) attains R2=0.9999 at RMSE 121.8 molecules·ps, a 93.0% RMSE reduction relative to persistence, robust to unseen O2 loadings and largely robust to unseen temperatures. Without it, no model beats persistence in any evaluation: the best ML model (an artificial neural network) is 26.0% worse, and accuracy decreases substantially under temperature extrapolation. The ablation attributes 93.3–98.3% of the tree and kernel models’ RMSE reduction to the H2-derived features, quantifying how much forecasting skill derives from the target history rather than instantaneous observables.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms27219435

Authors: Thi Be Ta Truong, Thuat T. Trinh

Institutions: Ton Duc Thang University, Norwegian University of Science and Technology, PoreLab

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,499