SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
Abstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the li...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01492-3 |
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| Summary: | Abstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that incorporates RadonPy, a Python library for fully automated polymer physical property calculations based on all-atom classical molecular dynamics, into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number. |
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| ISSN: | 2057-3960 |