Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an
integrated infrastructure to accelerate materials discovery and design using density …
integrated infrastructure to accelerate materials discovery and design using density …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
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 …
Sustainable and efficient technologies for removal and recovery of toxic and valuable metals from wastewater: Recent progress, challenges, and future perspectives
Due to their numerous effects on human health and the natural environment, water
contamination with heavy metals and metalloids, caused by their extensive use in various …
contamination with heavy metals and metalloids, caused by their extensive use in various …
[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 …
[HTML][HTML] Application of nanogenerators in acoustics based on artificial intelligence and machine learning
X Yu, T Ai, K Wang - APL Materials, 2024 - pubs.aip.org
As artificial intelligence (AI) advances, it is critical to give conventional electronics the
capacity to “think,”“analyze,” and “advise.” The need for intelligent, self-powered devices has …
capacity to “think,”“analyze,” and “advise.” The need for intelligent, self-powered devices has …
Machine-learning and high-throughput studies for high-entropy materials
The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …