Machine learning for perovskite materials design and discovery
Q Tao, P Xu, M Li, W Lu - Npj computational materials, 2021 - nature.com
The development of materials is one of the driving forces to accelerate modern scientific
progress and technological innovation. Machine learning (ML) technology is rapidly …
progress and technological innovation. Machine learning (ML) technology is rapidly …
Treasure trove for efficient hydrogen evolution through water splitting using diverse perovskite photocatalysts
Photocatalytic water splitting is the environmentally benign artificial photosynthetic pathway
to dissociate water into hydrogen and oxygen. It provides a great platform for bridging the …
to dissociate water into hydrogen and oxygen. It provides a great platform for bridging the …
Applications of machine learning in perovskite materials
Z Wang, M Yang, X Xie, 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 …
Machine learning aided design of perovskite oxide materials for photocatalytic water splitting
Q Tao, T Lu, Y Sheng, L Li, W Lu, M Li - Journal of Energy Chemistry, 2021 - Elsevier
Suffering from the inefficient traditional trial-and-error methods and the huge searching
space filled by millions of candidates, discovering new perovskite visible photocatalysts with …
space filled by millions of candidates, discovering new perovskite visible photocatalysts with …
Machine learning for halide perovskite materials
L Zhang, M He, S Shao - Nano Energy, 2020 - Elsevier
Halide perovskite materials serve as excellent candidates for solar cell and optoelectronic
devices. Recently, the design of the halide perovskite materials is greatly facilitated by …
devices. Recently, the design of the halide perovskite materials is greatly facilitated by …
Machine Learning for Predicting the Band Gaps of ABX3 Perovskites from Elemental Properties
The band gap is an important parameter that determines light-harvesting capability of
perovskite materials. It governs the performance of various optoelectronic devices such as …
perovskite materials. It governs the performance of various optoelectronic devices such as …
Bandgap prediction of metal halide perovskites using regression machine learning models
Organometal halide perovskites represent a type of nanomaterials, which are extensively
used in solar cells, light-emitting diodes, detectors and memristors due to their outstanding …
used in solar cells, light-emitting diodes, detectors and memristors due to their outstanding …
Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites
Crystallographic stability is an important factor that affects the stability of perovskites. The
stability dictates the commercial applications of lead-based organometal halide perovskites …
stability dictates the commercial applications of lead-based organometal halide perovskites …
AI for dielectric capacitors
Dielectric capacitors, characterized by ultra-high power densities, have been widely used in
Internet of Everything terminals and vigorously developed to improve their energy storage …
Internet of Everything terminals and vigorously developed to improve their energy storage …
Comparative analysis of machine learning approaches on the prediction of the electronic properties of perovskites: A case study of ABX3 and A2BB'X6
Abstract Machine learning (ML) methods have recently been widely employed to tackle
several problems in quantum mechanics and materials science. Their main objective is to …
several problems in quantum mechanics and materials science. Their main objective is to …