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Dye-sensitized solar cells strike back
Dye-sensitized solar cells (DSCs) are celebrating their 30th birthday and they are attracting
a wealth of research efforts aimed at unleashing their full potential. In recent years, DSCs …
a wealth of research efforts aimed at unleashing their full potential. In recent years, DSCs …
Artificial intelligence in physical sciences: Symbolic regression trends and perspectives
Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …
programming principles that integrates techniques and processes from heterogeneous …
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …
manufacturing sectors that have a considerable impact on sustainability and the …
Machine learning: accelerating materials development for energy storage and conversion
With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …
increasingly important on a global scale. Therefore, the exploration of novel materials for …
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 …
Paths towards high perovskite solar cells stability using machine learning techniques
This work aims to analyze the stability of Perovskite solar cells PSCs using machine learning
(ML) techniques. An extremely randomized trees technique, trained with a dataset …
(ML) techniques. An extremely randomized trees technique, trained with a dataset …
[HTML][HTML] Machine learning for advanced energy materials
The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …
structural-activity relationships has recently become one of the hot and trending topics in …
MLMD: a programming-free AI platform to predict and design materials
Accelerating the discovery of advanced materials is crucial for modern industries,
aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are …
aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are …
Novel materials for urban farming
Scarcity of natural resources, shifting demographics, climate change, and increasing waste
are four major challenges in the quest to feed the exploding world population. These …
are four major challenges in the quest to feed the exploding world population. These …
Artificial intelligence-based, wavelet-aided prediction of long-term outdoor performance of perovskite solar cells
The commercial development of perovskite solar cells (PSCs) has been significantly
delayed by the constraint of performing time-consuming degradation studies under real …
delayed by the constraint of performing time-consuming degradation studies under real …