Machine learning for high performance organic solar cells: current scenario and future prospects
A Mahmood, JL Wang - Energy & environmental science, 2021 - pubs.rsc.org
Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …
automating solutions to complex problems that are hard to program using conventional …
Machine learning for organic photovoltaic polymers: a minireview
Abstract Machine learning is a powerful tool that can provide a way to revolutionize the
material science. Its use for the designing and screening of materials for polymer solar cells …
material science. Its use for the designing and screening of materials for polymer solar cells …
Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th …
Organic solar cells are the most promising candidates for future commercialization. This goal
can be quickly achieved by designing new materials and predicting their performance …
can be quickly achieved by designing new materials and predicting their performance …
A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent …
A Mahmood, JL Wang - Journal of Materials Chemistry A, 2021 - pubs.rsc.org
The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing
continuously, however, commercialization is far from being achieved due to the very high …
continuously, however, commercialization is far from being achieved due to the very high …
[HTML][HTML] Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications
Recent advancements and developments in artificial intelligence (AI) based approaches
have shifted the manufacturing practices towards the fourth industrial revolution, considered …
have shifted the manufacturing practices towards the fourth industrial revolution, considered …
Computational approaches for organic semiconductors: from chemical and physical understanding to predicting new materials
While a complete understanding of organic semiconductor (OSC) design principles remains
elusive, computational methods─ ranging from techniques based in classical and quantum …
elusive, computational methods─ ranging from techniques based in classical and quantum …
[HTML][HTML] Machine learning for advanced energy materials
The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …
structural-activity relationships has recently become one of the hot and trending topics in …
Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
In the process of finding high-performance materials for organic photovoltaics (OPVs), it is
meaningful if one can establish the relationship between chemical structures and …
meaningful if one can establish the relationship between chemical structures and …
Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells
Y Wu, J Guo, R Sun, J Min - npj Computational Materials, 2020 - nature.com
Integrating artificial intelligence (AI) and computer science together with current approaches
in material synthesis and optimization will act as an effective approach for speeding up the …
in material synthesis and optimization will act as an effective approach for speeding up the …
Predictions and strategies learned from machine learning to develop high‐performing perovskite solar cells
Perovskite solar cells (PSCs) have recently received considerable attention due to the high
energy conversion efficiency achieved within a few years of their inception. However, a …
energy conversion efficiency achieved within a few years of their inception. However, a …