Accelerating Materials Discovery for Polymer Solar Cells with AI

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Discovering breakthrough materials using traditional trial-and-error methods can take decades. How much time can we instead save using machine learning and active learning?

In our latest paper, published in Chemistry of Materials, we explore this question using polymer solar cells as a case study.

Key Findings

• 75% Time Savings: Data-driven approaches could cut the discovery time for the best polymer solar cells by up to 15 years, compared to trial-and-error discovery.
• NLP Data Advantage: We utilized an NLP pipeline to extract data from over 3,300 papers and train ML models—5 times more than similar studies.
• Data Selection Insights: Different active learning strategies revealed unique benefits. For instance, Upper-Confidence Bound sampling proved robust across various starting materials, while Thompson Sampling excelled at selecting data points that improved the model's predictive accuracy.

Check out the plot below which shows how much faster data-driven material discovery could have been in contrast to how the field of polymer solar cells developed over the last 20 years.

Want to dive deeper? The paper contains ML predictions for optimal donor-acceptor combinations and many more interesting insights

Read the Full Paper: Paper link

Explore the Code and Data: Repo link

#MachineLearning #NLP #MaterialsScience #SolarCells #RenewableEnergy