# Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing

arXiv: https://arxiv.org/abs/2402.19462 · HTML: https://mlsanity.com/paper/2402.19462/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 93 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation](https://mlsanity.com/paper/2205.14311/) (2022)
- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing](https://mlsanity.com/paper/2209.13136/) (2022)
- [Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels](https://mlsanity.com/paper/2006.02489/) (2020)
- [PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature](https://mlsanity.com/paper/2311.07715/) (2023)
- [Benchmarking Active Learning Strategies for Materials Optimization and Discovery](https://mlsanity.com/paper/2204.05838/) (2022)
- [Copolymer Informatics with Multi-Task Deep Neural Networks](https://mlsanity.com/paper/2103.14174/) (2021)

## Built on by (most cited first, 1 of 1)

- [AI-driven inverse design of materials: Past, present and future](https://mlsanity.com/paper/2411.09429/) (2024)

## Abstract (arXiv)

We present a simulation of various active learning strategies for the discovery of polymer solar cell donor/acceptor pairs using data extracted from the literature spanning ~20 years by a natural language processing pipeline. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches, their benefits have not been quantified for material discovery problems that can take decades. Our approach demonstrates a potential reduction in discovery time by approximately 75 %, equivalent to a 15 year acceleration in material innovation. Our pipeline enables us to extract data from greater than 3300 papers which is ~5 times larger and therefore more diverse than similar data sets reported by others. We also trained machine learning models to predict the power conversion efficiency and used our model to identify promising donor-acceptor combinations that are as yet unreported. We thus demonstrate a pipeline that goes from published literature to extracted material property data which in turn is used to obtain data-driven insights. Our insights include active learning strategies that can be used to train strong predictive models of material properties or be robust to the initial material system used. This work provides a valuable framework for data-driven research in materials science.