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 …
Emerging two-dimensional organic semiconductor-incorporated perovskites─ A fascinating family of hybrid electronic materials
Halide perovskites have attracted a great amount of attention owing to their unique materials
chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional …
chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional …
Engineering ligand reactivity enables high-temperature operation of stable perovskite solar cells
Perovskite solar cells (PSCs) consisting of interfacial two-and three-dimensional
heterostructures that incorporate ammonium ligand intercalation have enabled rapid …
heterostructures that incorporate ammonium ligand intercalation have enabled rapid …
Synthesis-on-substrate of quantum dot solids
Perovskite light-emitting diodes (PeLEDs) with an external quantum efficiency exceeding
20% have been achieved in both green and red wavelengths,,,–; however, the performance …
20% have been achieved in both green and red wavelengths,,,–; however, the performance …
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 …
Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory
The past decade has witnessed the significant efforts in novel material discovery in the use
of data-driven techniques, in particular, machine learning (ML). However, since it needs to …
of data-driven techniques, in particular, machine learning (ML). However, since it needs to …
Advanced organic–inorganic hybrid materials for optoelectronic applications
K Zhou, B Qi, Z Liu, X Wang, Y Sun… - Advanced Functional …, 2024 - Wiley Online Library
Research on organic–inorganic hybrid materials (OIHMs) has experienced explosive growth
in the past decades. The diversity of organic components allows for the introduction of …
in the past decades. The diversity of organic components allows for the introduction of …
Machine learning in perovskite solar cells: recent developments and future perspectives
Within a short period of time, perovskite solar cells (PSC) have attracted paramount research
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
Challenges, opportunities, and prospects in metal halide perovskites from theoretical and machine learning perspectives
Metal halide perovskite (MHP) is a promising next generation energy material for various
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …
Designing Ruddlesden–Popper Layered Perovskites through Their Organic Cations
MP Arciniegas, L Manna - ACS Energy Letters, 2022 - ACS Publications
Organic–inorganic layered perovskites were initially envisioned as materials for solar cells
in view of their greater ambient stability compared to their 3D relatives. However, their major …
in view of their greater ambient stability compared to their 3D relatives. However, their major …