Researchers at Osaka University are using ML to design and simulate molecules for organic solar cells
With the implementation of Machine Learning, technology is increasingly evolving and revolutionizing the environment. Machine learning (ML) and Artificial Intelligence (AI) may tend to be synonymous, but ML is an AI application that allows a program to understand automatically from data input. In a variety of industries, ML’s functional capabilities accelerate operating performance and power automation. ML is progressing at a breakneck pace, fueled primarily by new technological innovations.
Machine Learning in the Solar Energy Industry
Solar energy is a significant renewable energy source, and its demand has been growing rapidly in recent years. Last year, the global solar energy market was worth $52.5 billion, and by 2026, it is expected to be worth $223.3 billion. The possibilities that machine learning approaches are unveiling in the industry are part of the reason for this exponential development. Machine learning technology utilizes complex algorithms to assist in the analysis of the future, enabling businesses to develop more successful strategies. Solar energy companies can significantly boost their profit margins by shifting their market strategy from a conventional approach to modern data-driven competencies.
Osaka University Experiments on Virtually Unlimited Solar Cell
Machine learning is being used by researchers at Osaka University to design and simulate molecules for organic solar cells, which could lead to more efficient usable materials for renewable energy applications.
As per report of EurekAlert, Osaka University researchers employed machine learning to design new polymers for use in photovoltaic devices. After virtually screening over 200,000 candidate materials, they synthesized one of the most promising and found its properties were consistent with their predictions. This work may lead to a revolution in the way functional materials are discovered.
Machine learning is a powerful tool that allows computers to make predictions about even complex situations, as long as the algorithms are supplied with sufficient example data. This is especially useful for complicated problems in material science, such as designing molecules for organic solar cells, which can depend on a vast array of factors and unknown molecular structures. It would take humans years to sift through the data to find the underlying patterns–and even longer to test all of the possible candidate combinations of donor polymers and acceptor molecules that make up an organic solar cell. Thus, progress in improving the efficiency of solar cells to be competitive in the renewable energy space has been slow.
EurekAlert also added that, “This project may contribute not only to the development of highly efficient organic solar cells, but also can be adapted to material informatics of other functional materials,” senior author Akinori Saeki says.
We may see this type of machine learning, in which an algorithm can rapidly screen thousands or perhaps even millions of candidate molecules based on machine learning predictions, applied to other areas, such as catalysts and functional polymers.
Credit: Google News