AI for nanomaterials development in clean energy and carbon capture, utilization and storage (CCUS)
Zero-carbon energy and negative emission technologies are crucial for achieving a carbon
neutral future, and nanomaterials have played critical roles in advancing such technologies …
neutral future, and nanomaterials have played critical roles in advancing such technologies …
Methods, progresses, and opportunities of materials informatics
C Li, K Zheng - InfoMat, 2023 - Wiley Online Library
As an implementation tool of data intensive scientific research methods, machine learning
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …
Statistical analysis and visualization of data of non-fullerene small molecule acceptors from Harvard organic photovoltaic database. Structural similarity analysis with …
Data-driven material design has gained the position of “fourth paradigm” with the first three
being experiments, theory, and simulation. The statistical analysis and visualization of data …
being experiments, theory, and simulation. The statistical analysis and visualization of data …
Machine learning assisted designing of organic semiconductors for organic solar cells: High-throughput screening and reorganization energy prediction
Organic solar cells (OSCs) are ecofriendly and an inexpensive source of electricity
production. However, high-throughput screening and designing new materials without …
production. However, high-throughput screening and designing new materials without …
Energy level prediction of organic semiconductors for photodetectors and mining of a photovoltaic database to search for new building units
Due to the large versatility in organic semiconductors, selecting a suitable (organic
semiconductor) material for photodetectors is a challenging task. Integrating computer …
semiconductor) material for photodetectors is a challenging task. Integrating computer …
Designing of near-IR organic semiconductors for photodetectors: Machine learning and data mining assisted efficient pipeline
Near-infrared organic semiconductors are attractive candidates for photodetector
applications due to their inherent characteristics such as room temperature operating …
applications due to their inherent characteristics such as room temperature operating …
Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline
Abstract Machine learning (ML) analysis has gained huge importance among researchers
for predicting multiple parameters and designing efficient donor and acceptor materials …
for predicting multiple parameters and designing efficient donor and acceptor materials …
Virtual screening of efficient building blocks and designing of new polymers for organic solar cells
FMA Alzahrani, M Saqib, M Arooj, T Mubashir… - Journal of Physics and …, 2023 - Elsevier
Designing effective materials for organic solar cells (OSCs) is a challenging and time-
consuming process. To achieve high performance OSCs, efficient designing/screening of …
consuming process. To achieve high performance OSCs, efficient designing/screening of …
Virtual mining of polymer monomers for photodetectors application and regression-aided reorganization energy prediction
Predicting and understanding the charge transport properties of organic semiconductors is a
crucial target for constructing efficient electronic devices such as photodetectors. To this end …
crucial target for constructing efficient electronic devices such as photodetectors. To this end …
Machine learning assisted designing of Indacenodithiophene (IDT)-based polymers for future application of photoacoustic imaging
Non-invasive imaging tools are essential for diagnosis of complex disease. Photoacoustic
(PA) imaging is a multiscale noninvasive imaging modality with high resolution and …
(PA) imaging is a multiscale noninvasive imaging modality with high resolution and …