Rapid Neural Network Prediction of Linear Block Copolymer Free Energies
Free energies are fundamental quantities governing phase behavior and thermodynamic stability in polymer systems, yet their accurate computation often requires extensive simulations and post-processing techniques such as the Bennett Acceptance Ratio (BAR). While BAR provides reliable estimates when applied between closely related thermodynamic states, evaluating free energies across large changes in interaction strength typically requires a sequence of intermediate simulations to maintain sufficient phase-space overlap, substantially increasing computational cost. In this work we develop a machine learning framework for rapidly predicting excess free energies of linear diblock copolymer s...
| Authors | Ian Chen, Alfredo Alexander-Katz |
|---|---|
| Primary category | cond-mat.soft |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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