Berkeley Lab’s COSMIC X-ray instrument reveals key information about individual battery particles

An X-ray instrument at Berkeley Lab contributed to a battery study that used an innovative approach to machine learning to speed up the learning curve about a process that shortens the life of fast-charging lithium batteries.

Researchers used Berkeley Lab’s Advanced Light Source, a synchrotron that produces light ranging from the infrared to X-rays for dozens of simultaneous experiments, to perform a chemical imaging technique known as scanning transmission X-ray microscopy, or STXM, at a state-of-the-art ALS beamline dubbed COSMIC. 

Researchers also employed “in situ” X-ray diffraction at another synchrotron – SLAC’s Stanford Synchrotron Radiation Lightsource – which attempted to recreate the conditions present in a battery, and additionally provided a many-particle battery model. All three forms of data were combined in a format to help the machine-learning algorithms learn the physics at work in the battery.

While typical machine-learning algorithms seek out images that either do or don’t match a training set of images, in this study the researchers applied a deeper set of data from experiments and other sources to enable more refined results. It represents the first time this brand of “scientific machine learning” was applied to battery cycling, researchers noted. The study was published recently in Nature Materials.

The study benefited from an ability at the COSMIC beamline to single out the chemical states of about 100 individual particles, which was enabled by COSMIC’s high-speed, high-resolution imaging capabilities. Young-Sang Yu, a research scientist at the ALS who participated in the study, noted that each selected particle was imaged at about 50 different energy steps during the cycling process, for a total of 5,000 images. 

The data from ALS experiments and other experiments were combined with data from fast-charging mathematical models, and with information about the chemistry and physics of fast charging, and then incorporated into the machine-learning algorithms.

“Rather than having the computer directly figure out the model by simply feeding it data, as we did in the two previous studies, we taught the computer how to choose or learn the right equations, and thus the right physics,” said Stanford postdoctoral researcher Stephen Dongmin Kang, a study co-author.

Patrick Herring, senior research scientist for Toyota Research Institute, which supported the work through its Accelerated Materials Design and Discovery program, said, “By understanding the fundamental reactions that occur within the battery, we can extend its life, enable faster charging, and ultimately design better battery materials.”

The post X-Ray Experiments, Machine Learning Could Trim Years Off Battery R&D appeared first on Artificial Intelligence.

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.

The post MACHINE LEARNING TO DESIGN VIRTUALLY UNLIMITED SOLAR CELL EXPERIMENTS appeared first on Artificial Intelligence.

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.

The post MACHINE LEARNING TO DESIGN VIRTUALLY UNLIMITED SOLAR CELL EXPERIMENTS appeared first on Artificial Intelligence.