Passivation strategies for enhancing device performance of perovskite solar cells
Because of high efficiencies and low-cost fabrication, perovskite solar cells (PSCs) have
drawn great attention. Although an impressive power conversion efficiency (PCE) of 26 …
drawn great attention. Although an impressive power conversion efficiency (PCE) of 26 …
Recent advances, practical challenges, and perspectives of intermediate temperature solid oxide fuel cell cathodes
As a highly efficient clean power generation technology, intermediate temperature (600–
800° C) solid oxide fuel cells (IT-SOFCs) have gained much interest due to their rapid start …
800° C) solid oxide fuel cells (IT-SOFCs) have gained much interest due to their rapid start …
Machine learning for perovskite solar cells and component materials: key technologies and prospects
Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
Machine learning in materials science: From explainable predictions to autonomous design
G Pilania - Computational Materials Science, 2021 - Elsevier
The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …
Applications of machine learning in perovskite materials
Z Wang, M Yang, X **e, C Yu, Q Jiang… - … Composites and Hybrid …, 2022 - Springer
Abstract Machine learning (ML) offers the opportunities to discover certain unique properties
for typical material. Taking perovskite materials as an example, this review summarizes the …
for typical material. Taking perovskite materials as an example, this review summarizes the …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Half-metallic double perovskite oxides: recent developments and future perspectives
Q Tang, X Zhu - Journal of Materials Chemistry C, 2022 - pubs.rsc.org
The continuous miniaturization of charge-based electronic devices and overcoming the
bottleneck of Moore's law have driven the rapid growth of spintronics, spintronics operates …
bottleneck of Moore's law have driven the rapid growth of spintronics, spintronics operates …
MatGPT: A vane of materials informatics from past, present, to future
Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …
disciplines, materials informatics is continuously accelerating the vigorous development of …
Machine-learning-assisted design of highly tough thermosetting polymers
Y Hu, W Zhao, L Wang, J Lin, L Du - ACS Applied Materials & …, 2022 - ACS Publications
Despite advances in machine learning for accurately predicting material properties,
forecasting the performance of thermosetting polymers remains a challenge due to the …
forecasting the performance of thermosetting polymers remains a challenge due to the …
Discovery of energy storage molecular materials using quantum chemistry-guided multiobjective bayesian optimization
Redox flow batteries (RFBs) are a promising technology for stationary energy storage
applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried …
applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried …