Integrating machine learning and molecular dynamics for accelerated discovery of polymers with high thermal conductivity

YG Wu and B Yao and X Huang and YQ Chen, JOURNAL OF APPLIED PHYSICS, 138, 175101 (2025).

DOI: 10.1063/5.0296040

The vast chemical diversity of polymers, compounded with sparse reliable characterization data, fundamentally constrains machine learning (ML)-driven exploration of advanced polymeric materials. To overcome this, we establish an integrated computational framework combining a deep neural network (DNN), Bayesian optimization (BO), and molecular dynamics (MD) simulations for the targeted design of backbone polymers with high thermal conductivity (TC >= 0.40 W m(-1) K-1). Our workflow begins with a data set of 501 backbone polymers, whose thermal conductivities (TCs) are labeled by MD simulations. We then established a mapping between their force-field-inspired descriptors and TCs using a DNN. While sparse high-TC data limited the DNN's predictive accuracy for TC >= 0.40 W m(-1) K-1, we leveraged it to screen 2209 high-potential candidates from a 14 706-polymer virtual library generated by small molecules according to chemical reaction rules. Subsequent closed-loop BO-MD validation enabled efficient navigation of this subspace, and 11 synthesizable polymers with high TC were identified in 500 evaluations. Crucially, mechanistic analysis reveals that intra-chain interactions dominate thermal transport, with intra-chain contributions of 79.1%-87.5% on TC for the first six polymers. This work delivers a robust paradigm for ML-driven material discovery under data constraints.

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