Scientists from the School of Energy and Power Engineering, Chongqing University, China, have discovered a highly efficient, time saving as well as a reliable machine learning (ML) method for the research and development of novel organic photovoltaic (OPV) materials. During the development of high performing OPV materials, if one can pre-establish the correlation between the structure of the designed material and its photovoltaic property, it becomes highly meaningful and time saving. The research is reported in the journal Science Advances. OPV cells are an easy and highly economical method for transforming the solar energy into electrical energy. Until now, the typical OPV materials-based research has focused on building a relationship between the newly developed OPV molecular material and its organic photovoltaic material properties.
The conjunction of psyche and matter, of digital and physical advances, lies at the core of the fourth industrial revolution. The marriage of Artificial Intelligence (AI) and materials science speaks as one of the clearest models. Unadulterated digital development has pulled in the best consideration and a large offer of financial investment in the course of the most recent years. Be that as it may, we live in a material world, where the nature of our lives relies upon enhancements in physical products and services: nourishment and asylum, social insurance, transportation, energy etc. It is quite true that we invest much more energy in our online virtual universes, yet this is reflected by a developing number of Amazon bundles at our doorsteps.
In recent years, research efforts such as the Materials Genome Initiative and the Materials Project have produced a wealth of computational tools for designing new materials useful for a range of applications, from energy and electronics to aeronautics and civil engineering. But developing processes for producing those materials has continued to depend on a combination of experience, intuition, and manual literature reviews. A team of researchers at MIT, the University of Massachusetts at Amherst, and the University of California at Berkeley hope to close that materials-science automation gap, with a new artificial-intelligence system that would pore through research papers to deduce "recipes" for producing particular materials. "Computational materials scientists have made a lot of progress in the'what' to make -- what material to design based on desired properties," says Elsa Olivetti, the Atlantic Richfield Assistant Professor of Energy Studies in MIT's Department of Materials Science and Engineering (DMSE). "But because of that success, the bottleneck has shifted to, 'Okay, now how do I make it?'"
Most of that sediment then gets dumped right back in the water: either in the Gulf of Mexico or in deeper parts of the same waterway, where the current is expected to carry it downstream and away from the dredged area. By law, the Corps is required to do the most cost-effective thing with the dredged material, which limits the options on its use.
Researchers at Washington State University have created materials that react to temperature and ultraviolet light. It's a look into what mass-produced smart materials could be. Smart materials are a growing area of interest for researchers, with potential for instantly forming casts and self-healing phone screens. But this is the first time memory, light-activated movement, and self-healing have been combined. One of the reasons smart materials aren't widespread yet is their single-use capability.