TY - JOUR
T1 - Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells
AU - Malhotra, Prateek
AU - Khandelwal, Kanupriya
AU - Biswas, Subhayan
AU - Chen, Fang Chung
AU - Sharma, Ganesh D.
N1 - Publisher Copyright:
© 2022 The Royal Society of Chemistry.
PY - 2022/10/19
Y1 - 2022/10/19
N2 - Organic solar cells (OSCs) have witnessed significant improvement in power conversion efficiency (PCE) in the last decade. The structural flexibility of organic semiconductors provides vast search space for potential candidates of OSCs, but discovering new materials from search space with traditional approaches such as DFT is computationally expensive and time-consuming. Machine learning (ML) is extensively used in OSCs to accelerate productivity and materials discovery. ML is gaining more attention due to the availability of large datasets, improved algorithms, and exponentially growing computational power. In this review, current progress, opportunity, and challenges for ML in OSCs have been identified. Given the rapid advances in this field, impactful techniques that have been useful in extracting meaningful insights are discussed. Finally, we elaborate upon the bottlenecks of the ML workflow with respect to data size, model interpretability, and extrapolation.
AB - Organic solar cells (OSCs) have witnessed significant improvement in power conversion efficiency (PCE) in the last decade. The structural flexibility of organic semiconductors provides vast search space for potential candidates of OSCs, but discovering new materials from search space with traditional approaches such as DFT is computationally expensive and time-consuming. Machine learning (ML) is extensively used in OSCs to accelerate productivity and materials discovery. ML is gaining more attention due to the availability of large datasets, improved algorithms, and exponentially growing computational power. In this review, current progress, opportunity, and challenges for ML in OSCs have been identified. Given the rapid advances in this field, impactful techniques that have been useful in extracting meaningful insights are discussed. Finally, we elaborate upon the bottlenecks of the ML workflow with respect to data size, model interpretability, and extrapolation.
UR - http://www.scopus.com/inward/record.url?scp=85142355381&partnerID=8YFLogxK
U2 - 10.1039/d2tc03276g
DO - 10.1039/d2tc03276g
M3 - Review article
AN - SCOPUS:85142355381
SN - 2050-7526
VL - 10
SP - 17781
EP - 17811
JO - Journal of Materials Chemistry C
JF - Journal of Materials Chemistry C
IS - 47
ER -