Artificial intelligence helped design a new type of battery – Science News Explores
Come explore with us!
The process narrowed 32 million candidate materials to a few dozen in just 80 hours
A new type of battery is based on a material discovered with the help of artificial intelligence.
Dan DeLong/Microsoft
By
With more and more devices being powered by batteries, there’s a hunt to find new, safer and cheaper materials to use in those batteries. Doing that has traditionally involved tinkering in the lab — with lots of trial-and-error. But artificial intelligence (AI) could speed up that process, new research shows. And it hints that computers might help identify new materials for batteries to meet specific needs.
A team of 11 researchers in Washington started with a huge pool of potential materials for a new battery. Some of the researchers worked for Microsoft in Redmond. Others worked at the Energy Department’s Pacific Northwest National Laboratory (PNNL) in Richland. In all, the team came up with more than 32 million candidate materials. The group then used AI to help narrow that list down to just 23 promising options. From there, they picked one — and built a working battery.
It’s not the first time scientists have used AI to predict how materials might behave. But past work typically hasn’t led to making a new material.
“The nice thing about this paper is that it goes all the way from start to finish,” says Shyue Ping Ong. He is a materials scientist who did not take part in the new research. He works at the University of California, San Diego.
Batteries work by converting chemical energy to electrical energy. (That’s true for AA batteries and for the lithium-ion (Li-ion) batteries in your phone and laptop.) Batteries do this with the help of something called an electrolyte. Atoms that have an electric charge — ions — can flow through that electrolyte.
Electrolytes can be liquid or solid. Standard Li-ion batteries use a liquid type. But those liquids can pose risks, such as leaks or fires. So scientists have looked to design solid electrolytes. The Washington team hoped to make such an electrolyte for batteries.
To create their initial set of possible candidates, they looked at the structures of materials in known electrolytes. Then they asked a computer to swap different elements into those electrolyte recipes. It was like a game of chemical mix-and-match. That’s how the researchers came up with those 32 million candidate electrolyte recipes.
Sorting through this giant list with traditional methods would have taken decades, says Nathan Baker. He’s a chemist at Microsoft who led the new research. But with AI, the researchers were able to pare down the list in just 80 hours. They used a type of AI called machine learning. It can make quick predictions based on patterns learned from known materials.
Weekly updates to help you use Science News Explores in the learning environment
Thank you for signing up!
There was a problem signing you up.
The Washington team then used AI to drop from the list any potential ingredient too unstable to exist in the real world. That brought the list down to fewer than 600,000 candidates. Next, AI selected ones likely to have traits that would be good for batteries.
From there, the researchers used methods that are more accurate than AI. They applied tried-and-tested, physics-based methods. These complex calculations required a powerful supercomputer. It helped model what features the materials might have as an electrolyte. The team also weeded out what would be rare, toxic or expensive ingredients.
Now, only 23 candidates were left. Five were already known. The research team picked a new material that seemed promising. It was stable and conducted electricity well. And it was related to materials the researchers already knew how to make.
Back in the lab, they produced this novel electrolyte. Then they used it to make a prototype battery — which worked.
“That’s when we got very excited,” says Vijay Murugesan. He’s a materials scientist on the team at PNNL. From when they started trying to make the material to when they had a working battery took only about six months. “That is superfast,” he says.
The team shared its findings in a paper published January 8 on arXiv.org. (Studies on this site have not yet been vetted by other experts.)
Submit your question here, and we might answer it an upcoming issue of Science News Explores
The new electrolyte is similar to a known one. It contains lithium, yttrium and chlorine. But the new one swaps in sodium for some lithium. That’s an advantage, as lithium is costly and in high demand.
Mixing lithium and sodium is uncommon in electrolytes. Usually, “we would not mix these two,” says Yan Zeng. She works at Florida State University in Tallahassee. A materials scientist, she did not work on the new project. It’s more common to use either lithium or sodium ions, not both.
The two types of ions both conduct electricity. So they might be expected to compete with one another, lowering an electrolyte’s overall performance. But the new material highlights one of AI’s benefits, Zeng notes. It “can sort of step out of the box” to try things human minds might not.
artificial intelligence: A type of knowledge-based decision-making exhibited by machines or computers. The term also refers to the field of study in which scientists try to create machines or computer software capable of intelligent behavior.
atom: The basic unit of a chemical element. Atoms are made up of a dense nucleus that contains positively charged protons and uncharged neutrons. The nucleus is orbited by a cloud of negatively charged electrons.
battery: A device that can convert chemical energy into electrical energy.
chemical: A substance formed from two or more atoms that unite (bond) in a fixed proportion and structure. For example, water is a chemical made when two hydrogen atoms bond to one oxygen atom. Its chemical formula is H2O. Chemical also can be an adjective to describe properties of materials that are the result of various reactions between different compounds.
conductor: (in physics and engineering) A material through which an electrical current can flow.
current: (in electricity) The flow of electricity or the amount of charge moving through some material over a particular period of time.
electrolyte: A non-metallic liquid or solid that conducts ions — electrically charged atoms or molecules — to carry electrical charges. (Certain minerals in blood or other bodily fluids can serve as the ions that move to carry a charge.) Electrolytes also can serve as the ions that move positive charges within a battery or capacitor.
element: A building block of some larger structure. (in chemistry) Each of more than one hundred substances for which the smallest unit of each is a single atom. Examples include hydrogen, oxygen, carbon, lithium and uranium.
error: (In statistics) The non-deterministic (random) part of the relationship between two or more variables.
intelligence: The ability to collect and apply knowledge and skills.
ion: (adj. ionized) An atom or molecule with an electric charge due to the loss or gain of one or more electrons. An ionized gas, or plasma, is where all of the electrons have been separated from their parent atoms.
liquid: A material that flows freely but keeps a constant volume, like water or oil.
lithium: A soft, silvery metallic element. It’s the lightest of all metals and very reactive. It is used in batteries and ceramics.
machine learning: A technique in computer science that allows computers to learn from examples or experience. Machine learning is the basis of some forms of artificial intelligence (AI). For instance, a machine-learning system might compare X-rays of lung tissue in people with cancer and then compare these to whether and how long a patient survived after being given a particular treatment. In the future, that AI system might be able to look at a new patient’s lung scans and predict how well they will respond to a treatment.
materials scientist: A researcher who studies how the atomic and molecular structure of a material is related to its overall properties. Materials scientists can design new materials or analyze existing ones. Their analyses of a material’s overall properties (such as density, strength and melting point) can help engineers and other researchers select materials that are best suited to a new application.
prototype: A first or early model of some device, system or product that still needs to be perfected.
risk: The chance or mathematical likelihood that some bad thing might happen. For instance, exposure to radiation poses a risk of cancer. Or the hazard — or peril — itself. (For instance: Among cancer risks that the people faced were radiation and drinking water tainted with arsenic.)
sodium: A soft, silvery metallic element that will interact explosively when added to water. It is also a basic building block of table salt (a molecule of which consists of one atom of sodium and one atom of chlorine: NaCl). It is also found in sea salt.
solid: Firm and stable in shape; not liquid or gaseous.
toxic: Poisonous or able to harm or kill cells, tissues or whole organisms. The measure of risk posed by such a poison is its toxicity.
Preprint: C. Chen et al. Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation. arXiv. Submitted January 8, 2024. doi: 10.48550/arXiv. 2401.04070.
Journal: A. Merchant et al. Scaling deep learning for materials discovery. Nature. Vol. 624, December 7, 2023, p. 80. doi: 10.1038/s41586-023-06735-9.
Journal: N.J. Szymanski et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature. Vol. 624, December 7, 2023, p. 86. doi: 10.1038/s41586-023-06734-w.
Science News physics writer Emily Conover studied physics at the University of Chicago. She loves physics for its ability to reveal the secret rules about how stuff works, from tiny atoms to the vast cosmos.
Free educator resources are available for this article. Register to access:
Already Registered? Enter your e-mail address above.
Readability Score: 7.4
Founded in 2003, Science News Explores is a free, award-winning online publication dedicated to providing age-appropriate science news to learners, parents and educators. The publication, as well as Science News magazine, are published by the Society for Science, a nonprofit 501(c)(3) membership organization dedicated to public engagement in scientific research and education.
© Society for Science & the Public 2000–2024. All rights reserved.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
The process narrowed 32 million candidate materials to a few dozen in just 80 hours
A new type of battery is based on a material discovered with the help of artificial intelligence.
Dan DeLong/Microsoft
By
With more and more devices being powered by batteries, there’s a hunt to find new, safer and cheaper materials to use in those batteries. Doing that has traditionally involved tinkering in the lab — with lots of trial-and-error. But artificial intelligence (AI) could speed up that process, new research shows. And it hints that computers might help identify new materials for batteries to meet specific needs.
A team of 11 researchers in Washington started with a huge pool of potential materials for a new battery. Some of the researchers worked for Microsoft in Redmond. Others worked at the Energy Department’s Pacific Northwest National Laboratory (PNNL) in Richland. In all, the team came up with more than 32 million candidate materials. The group then used AI to help narrow that list down to just 23 promising options. From there, they picked one — and built a working battery.
It’s not the first time scientists have used AI to predict how materials might behave. But past work typically hasn’t led to making a new material.
“The nice thing about this paper is that it goes all the way from start to finish,” says Shyue Ping Ong. He is a materials scientist who did not take part in the new research. He works at the University of California, San Diego.
Batteries work by converting chemical energy to electrical energy. (That’s true for AA batteries and for the lithium-ion (Li-ion) batteries in your phone and laptop.) Batteries do this with the help of something called an electrolyte. Atoms that have an electric charge — ions — can flow through that electrolyte.
Electrolytes can be liquid or solid. Standard Li-ion batteries use a liquid type. But those liquids can pose risks, such as leaks or fires. So scientists have looked to design solid electrolytes. The Washington team hoped to make such an electrolyte for batteries.
To create their initial set of possible candidates, they looked at the structures of materials in known electrolytes. Then they asked a computer to swap different elements into those electrolyte recipes. It was like a game of chemical mix-and-match. That’s how the researchers came up with those 32 million candidate electrolyte recipes.
Sorting through this giant list with traditional methods would have taken decades, says Nathan Baker. He’s a chemist at Microsoft who led the new research. But with AI, the researchers were able to pare down the list in just 80 hours. They used a type of AI called machine learning. It can make quick predictions based on patterns learned from known materials.
Weekly updates to help you use Science News Explores in the learning environment
Thank you for signing up!
There was a problem signing you up.
The Washington team then used AI to drop from the list any potential ingredient too unstable to exist in the real world. That brought the list down to fewer than 600,000 candidates. Next, AI selected ones likely to have traits that would be good for batteries.
From there, the researchers used methods that are more accurate than AI. They applied tried-and-tested, physics-based methods. These complex calculations required a powerful supercomputer. It helped model what features the materials might have as an electrolyte. The team also weeded out what would be rare, toxic or expensive ingredients.
Now, only 23 candidates were left. Five were already known. The research team picked a new material that seemed promising. It was stable and conducted electricity well. And it was related to materials the researchers already knew how to make.
Back in the lab, they produced this novel electrolyte. Then they used it to make a prototype battery — which worked.
“That’s when we got very excited,” says Vijay Murugesan. He’s a materials scientist on the team at PNNL. From when they started trying to make the material to when they had a working battery took only about six months. “That is superfast,” he says.
The team shared its findings in a paper published January 8 on arXiv.org. (Studies on this site have not yet been vetted by other experts.)
Submit your question here, and we might answer it an upcoming issue of Science News Explores
The new electrolyte is similar to a known one. It contains lithium, yttrium and chlorine. But the new one swaps in sodium for some lithium. That’s an advantage, as lithium is costly and in high demand.
Mixing lithium and sodium is uncommon in electrolytes. Usually, “we would not mix these two,” says Yan Zeng. She works at Florida State University in Tallahassee. A materials scientist, she did not work on the new project. It’s more common to use either lithium or sodium ions, not both.
The two types of ions both conduct electricity. So they might be expected to compete with one another, lowering an electrolyte’s overall performance. But the new material highlights one of AI’s benefits, Zeng notes. It “can sort of step out of the box” to try things human minds might not.
artificial intelligence: A type of knowledge-based decision-making exhibited by machines or computers. The term also refers to the field of study in which scientists try to create machines or computer software capable of intelligent behavior.
atom: The basic unit of a chemical element. Atoms are made up of a dense nucleus that contains positively charged protons and uncharged neutrons. The nucleus is orbited by a cloud of negatively charged electrons.
battery: A device that can convert chemical energy into electrical energy.
chemical: A substance formed from two or more atoms that unite (bond) in a fixed proportion and structure. For example, water is a chemical made when two hydrogen atoms bond to one oxygen atom. Its chemical formula is H2O. Chemical also can be an adjective to describe properties of materials that are the result of various reactions between different compounds.
conductor: (in physics and engineering) A material through which an electrical current can flow.
current: (in electricity) The flow of electricity or the amount of charge moving through some material over a particular period of time.
electrolyte: A non-metallic liquid or solid that conducts ions — electrically charged atoms or molecules — to carry electrical charges. (Certain minerals in blood or other bodily fluids can serve as the ions that move to carry a charge.) Electrolytes also can serve as the ions that move positive charges within a battery or capacitor.
element: A building block of some larger structure. (in chemistry) Each of more than one hundred substances for which the smallest unit of each is a single atom. Examples include hydrogen, oxygen, carbon, lithium and uranium.
error: (In statistics) The non-deterministic (random) part of the relationship between two or more variables.
intelligence: The ability to collect and apply knowledge and skills.
ion: (adj. ionized) An atom or molecule with an electric charge due to the loss or gain of one or more electrons. An ionized gas, or plasma, is where all of the electrons have been separated from their parent atoms.
liquid: A material that flows freely but keeps a constant volume, like water or oil.
lithium: A soft, silvery metallic element. It’s the lightest of all metals and very reactive. It is used in batteries and ceramics.
machine learning: A technique in computer science that allows computers to learn from examples or experience. Machine learning is the basis of some forms of artificial intelligence (AI). For instance, a machine-learning system might compare X-rays of lung tissue in people with cancer and then compare these to whether and how long a patient survived after being given a particular treatment. In the future, that AI system might be able to look at a new patient’s lung scans and predict how well they will respond to a treatment.
materials scientist: A researcher who studies how the atomic and molecular structure of a material is related to its overall properties. Materials scientists can design new materials or analyze existing ones. Their analyses of a material’s overall properties (such as density, strength and melting point) can help engineers and other researchers select materials that are best suited to a new application.
prototype: A first or early model of some device, system or product that still needs to be perfected.
risk: The chance or mathematical likelihood that some bad thing might happen. For instance, exposure to radiation poses a risk of cancer. Or the hazard — or peril — itself. (For instance: Among cancer risks that the people faced were radiation and drinking water tainted with arsenic.)
sodium: A soft, silvery metallic element that will interact explosively when added to water. It is also a basic building block of table salt (a molecule of which consists of one atom of sodium and one atom of chlorine: NaCl). It is also found in sea salt.
solid: Firm and stable in shape; not liquid or gaseous.
toxic: Poisonous or able to harm or kill cells, tissues or whole organisms. The measure of risk posed by such a poison is its toxicity.
Preprint: C. Chen et al. Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation. arXiv. Submitted January 8, 2024. doi: 10.48550/arXiv. 2401.04070.
Journal: A. Merchant et al. Scaling deep learning for materials discovery. Nature. Vol. 624, December 7, 2023, p. 80. doi: 10.1038/s41586-023-06735-9.
Journal: N.J. Szymanski et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature. Vol. 624, December 7, 2023, p. 86. doi: 10.1038/s41586-023-06734-w.
Science News physics writer Emily Conover studied physics at the University of Chicago. She loves physics for its ability to reveal the secret rules about how stuff works, from tiny atoms to the vast cosmos.
Free educator resources are available for this article. Register to access:
Already Registered? Enter your e-mail address above.
Readability Score: 7.4
Founded in 2003, Science News Explores is a free, award-winning online publication dedicated to providing age-appropriate science news to learners, parents and educators. The publication, as well as Science News magazine, are published by the Society for Science, a nonprofit 501(c)(3) membership organization dedicated to public engagement in scientific research and education.
© Society for Science & the Public 2000–2024. All rights reserved.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
