Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Tailoring passivators for highly efficient and stable perovskite solar cells
There is an ongoing global effort to advance emerging perovskite solar cells (PSCs), and
many of these endeavours are focused on develo** new compositions, processing …
many of these endeavours are focused on develo** new compositions, processing …
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Trapped by time-consuming traditional trial-and-error methods and vast untapped
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …
Passivation and process engineering approaches of halide perovskite films for high efficiency and stability perovskite solar cells
AR bin Mohd Yusoff, M Vasilopoulou… - Energy & …, 2021 - pubs.rsc.org
The surface, interfaces and grain boundaries of a halide perovskite film carry critical tasks in
achieving as well as maintaining high solar cell performance due to the inherently defective …
achieving as well as maintaining high solar cell performance due to the inherently defective …
[HTML][HTML] Roadmap on organic–inorganic hybrid perovskite semiconductors and devices
Metal halide perovskites are the first solution processed semiconductors that can compete in
their functionality with conventional semiconductors, such as silicon. Over the past several …
their functionality with conventional semiconductors, such as silicon. Over the past several …
Employing 2D‐perovskite as an electron blocking layer in highly efficient (18.5%) perovskite solar cells with printable low temperature carbon electrode
Interface engineering and passivating contacts are key enablers to reach the highest
efficiencies in photovoltaic devices. While printed carbon–graphite back electrodes for hole …
efficiencies in photovoltaic devices. While printed carbon–graphite back electrodes for hole …
Interpretable and explainable machine learning for materials science and chemistry
Conspectus Machine learning has become a common and powerful tool in materials
research. As more data become available, with the use of high-performance computing and …
research. As more data become available, with the use of high-performance computing and …
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 …
Material machine learning for alloys: Applications, challenges and perspectives
X Liu, P Xu, J Zhao, W Lu, M Li, G Wang - Journal of Alloys and Compounds, 2022 - Elsevier
Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to
efficiently design novel materials with superior performance. Here we reviewed the recent …
efficiently design novel materials with superior performance. Here we reviewed the recent …
Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning
Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation
engineering influences perovskite stability, with the present-day understanding of the impact …
engineering influences perovskite stability, with the present-day understanding of the impact …