Polymer Property Prediction via an Automated Molecular Dynamics Pipeline and Transfer Learning
4 affiliations
- 1Renewable Resources and Enabling Sciences Center
- 2National Laboratory of the Rockies
- 3Department of Chemical and Biological Engineering
- 4University of Colorado Boulder
Abstract
For many existing and emerging applications, there is a great need to develop novel polymeric materials with highly tuned properties. In the quest to design materials from highly abundant natural resources or waste materials, there is a vast design space that has remained substantially unexplored. Machine learning approaches can help guide toward improved properties, but these approaches often suffer from a lack of high quality data, the acquisition of which can be tedious, time-consuming, and expensive. In silico atomistic simulations are a promising approach to generate data that are highly correlated to real polymer properties, but simulations of polymers present myriad challenges including accurate and automatic force field parameterization, initial configuration generation, and long relaxation times which make it extremely difficult to perform such simulations in a high-throughput manner. Here, we present a computational pipeline starting from SMILES strings of reactants to automatically generate force field parameters and initial system coordinates for subsequent molecular dynamics (MD) simulations of short oligomers that have much faster relaxation times than the corresponding polymer melts. Because most measurable properties of oligomeric fluids are not the same as those of the corresponding polymers of much higher degree of polymerization, and indeed many properties of polymer phases do not exist for such liquids, we employ a transfer learning scheme to predict polymer properties from the oligomer thermophysical data. Using this approach, we are able to achieve improved property predictions for a number of polymer properties, including gas permeabilities. These improvements are most dramatic when experimental data are sparse and when the experimental observables are most directly correlated to simulation properties.
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BibTeX
@misc{law2026polymer,
title = {{Polymer Property Prediction via an Automated Molecular Dynamics Pipeline and Transfer Learning}},
author = {Jeffrey N. Law and Daria Lazarenko and Timotej Bernat and Brandon C. Knott and Michael R. Shirts},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010097/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010097/v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Polymer Property Prediction via an Automated Molecular Dynamics Pipeline and Transfer Learning
AU - Jeffrey N. Law
AU - Daria Lazarenko
AU - Timotej Bernat
AU - Brandon C. Knott
AU - Michael R. Shirts
PY - 2026
DA - 2026/10/08
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010097/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010097/v1
AB - For many existing and emerging applications, there is a great need to develop novel polymeric materials with highly tuned properties. In the quest to design materials from highly abundant natural resources or waste materials, there is a vast design space that has remained substantially unexplored. Machine learning approaches can help guide toward improved properties, but these approaches often suffer from a lack of high quality data, the acquisition of which can be tedious, time-consuming, and expensive. In silico atomistic simulations are a promising approach to generate data that are highly correlated to real polymer properties, but simulations of polymers present myriad challenges including accurate and automatic force field parameterization, initial configuration generation, and long relaxation times which make it extremely difficult to perform such simulations in a high-throughput manner. Here, we present a computational pipeline starting from SMILES strings of reactants to automatically generate force field parameters and initial system coordinates for subsequent molecular dynamics (MD) simulations of short oligomers that have much faster relaxation times than the corresponding polymer melts. Because most measurable properties of oligomeric fluids are not the same as those of the corresponding polymers of much higher degree of polymerization, and indeed many properties of polymer phases do not exist for such liquids, we employ a transfer learning scheme to predict polymer properties from the oligomer thermophysical data. Using this approach, we are able to achieve improved property predictions for a number of polymer properties, including gas permeabilities. These improvements are most dramatic when experimental data are sparse and when the experimental observables are most directly correlated to simulation properties.
ER -