Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
AI can try millions of different chemical permutations a day, and dramatically accelerate product development.
Scientists and researchers around the world are increasingly turning to Artificial Intelligence (AI)and so called Robo-Scientists to help them discover everything from new anti-biotics and drugs, through to new spray-on solar panel materials and vaccines, so it shouldn’t come as much of a surprise that AI is now being used to help develop new battery tech.
“In battery testing, you have to try a massive number of things, because the performance you get will vary drastically,” said Ermon, an assistant professor of computer science at MIT, referring to how scientists traditionally hunt for new battery breakthroughs, and who led the new project to use AI to help them develop new promising batteries for Electric Vehicles. “With AI, we’re able to quickly identify the most promising approaches and cut out a lot of unnecessary experiments.”
The study, published by Nature was part of a larger collaboration among scientists from Stanford University, MIT and the Toyota Research Institute that bridges foundational academic research and real-world industry applications, and their goal: to find the best method for charging an EV battery in 10 minutes that maximizes the battery’s overall lifetime.
The researchers wrote an AI program that, based on only a few charging cycles, predicted how batteries would respond to different charging approaches. The software also decided in real time what charging approaches to focus on or ignore. By reducing both the length and number of trials, the researchers cut the testing process from almost two years to just 16 days.
“We figured out how to greatly accelerate the testing process for extreme fast charging,” said Peter Attia, who co-led the study while he was a graduate student. “What’s really exciting, though, is the method. We can apply this approach to many other problems that, right now, are holding back battery development for months or years.”
Designing ultra-fast-charging batteries is a major challenge, mainly because it is difficult to make them last. The intensity of the faster charge puts greater strain on the battery, which often causes it to fail early. To prevent this damage to the battery pack, a component that accounts for a large chunk of an electric car’s total cost, battery engineers must test an exhaustive series of charging methods to find the ones that work best.
The new research sought to optimise this process. At the outset, the team saw that fast-charging optimisation amounted to many trial-and-error tests – something that is inefficient for humans, but the perfect problem for a machine.
“Machine learning is trial-and-error, but in a smarter way,” said Aditya Grover, a graduate student in computer science who also co-led the study. “Computers are far better than us at figuring out when to explore – try new and different approaches – and when to exploit, or zero in, on the most promising ones.”
The team used this power to their advantage in two key ways. First, they used it to reduce the time per cycling experiment. In a previous study, the researchers found that instead of charging and recharging every battery until it failed – the usual way of testing a battery’s lifetime –they could predict how long a battery would last after only its first 100 charging cycles. This is because the machine learning system, after being trained on a few batteries cycled to failure, could find patterns in the early data that presaged how long a battery would last.
Second, machine learning reduced the number of methods they had to test. Instead of testing every possible charging method equally, or relying on intuition, the computer learned from its experiences to quickly find the best protocols to test.
By testing fewer methods for fewer cycles, the study’s authors quickly found an optimal ultra-fast-charging protocol for their battery. In addition to dramatically speeding up the testing process, the computer’s solution was also better – and much more unusual – than what a battery scientist would likely have devised, said Ermon.
“It gave us this surprisingly simple charging protocol – something we didn’t expect,” Ermon said. “That’s the difference between a human and a machine: The machine is not biased by human intuition, which is powerful but sometimes misleading.”
And as for what’s next and wider applications of the study, well, the researchers said their approach could accelerate nearly every piece of the battery development pipeline: from designing the chemistry of a battery to determining its size and shape, to finding better systems for manufacturing and storage which would have broad implications not only for electric vehicles but for other types of energy storage too.
“This is a new way of doing battery development,” said Patrick Herring, co-author of the study and a scientist at the Toyota Research Institute. “Having data that you can share among a large number of people in academia and industry, and that is automatically analysed, enables much faster innovation.”
The study’s machine learning and data collection system will be made available for future battery scientists to freely use, Herring added. By using this system to optimize other parts of the process with machine learning, battery development – and the arrival of newer, better technologies – could be accelerated by an order of magnitude or more, he said.
The potential of the study’s method extends even beyond the world of batteries, Ermon said. Other big data testing problems, from drug development to optimizing the performance of X-rays and lasers, could also be revolutionised by the use of machine learning optimisation. And ultimately, he said, it could even help to optimise one of the most fundamental processes of all.
“The bigger hope is to help the process of scientific discovery itself,” Ermon said. “We’re asking: Can we design these methods to come up with hypotheses automatically? Can they help us extract knowledge that humans could not? As we get better and better algorithms, we hope the whole scientific discovery process may drastically speed up.” And as these robo-scientists get faster and smarter hopefully everyone will be able to benefit as they help accelerate the global rate of innovation.
Source: DOI 10.1038/s41586-020-1994-5