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.”

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LINE Corporation has selected Cloudera to develop its AI technology based business and further empower its Data Science and Engineering Centre (DSEC), thus strengthening its data-driven business objectives.

According to Cloudera, LINE will utilise Cloudera’s open source technologies to manage data lifecycles and security. LINE’s DSEC is a research and development (R&D) arm of the company that focuses on data platform development, data analysis, machine learning, AI technology development and fundamental research.

The department has made usage statistics and other data more accessible across other service areas of the business, allowing for more precise data analysis, information filtering and more effective data and information application, the company states.

The DSEC has to handle a large volume and variety of data in order to deliver insights to multiple lines of business and provide services for 186 million monthly active users around the world.

Cloudera’s Data Platform, with enterprise-grade centralised security and governance capabilities, proposes to enable the DSEC team to manage and govern data more effectively and securely.

It will allows operators to set and maintain metadata parameters for security, regulatory compliance and data analytics, the company states.

By unifying the data warehouses from LINE’s various services, LINE envisions that its data platform will be enhanced and users across the organisation will be provided with governed, self-service analytics without compromising the safety of their data, the company states.

This is also aligned with LINE’s restructuring of its organisation to promote data use across businesses and departments, utilising its sizable database more actively to improve usability of its existing services as well as create new AI-powered services and functionality.

LINE Corporation engineering director of data platform and fellow of data engineering Cheolho Choi says, “In order to be at the forefront of innovation, it is crucial for us to be able to manage, harness, and secure big data while democratising it.

“Given Cloudera’s expertise in architecting, designing, and securing mission-critical big data platforms, we are confident that they can improve on the robust LINE data platform to speed time to value, reinforce our data security, and reduce operational costs.”

Cloudera states that from initial phases of architecture through to scale-out operations, Cloudera will provide ongoing technical domain expertise and guidance via its team of professional services consultants.

In addition, Cloudera’s customised training will equip DSEC’s engineers with the skills to manage and secure big data effectively, the company states, with an overall focus on promoting research and development of the AI technology powering its business.

Cloudera vice president Asia Pacific and Japan Mark Micallef says, “Consumers today expect businesses to offer personalised services and innovate quickly while keeping their data safe.

“We are pleased to help LINE address those demands by empowering its users across the organisation to transform its massive and complex data into clear and actionable insights using an integrated data platform that is scalable and delivers multi-function analytics with consistent security and governance.”

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Trying to get a handle on the progress of artificial intelligence is a daunting task, even for those enmeshed in the AI community. But the latest edition of the AI Index report — an annual rundown of machine learning data points now in its third year — does a good job confirming what you probably already suspected: the AI world is booming in a range of metrics covering research, education, and technical achievements.

The AI Index covers a lot of ground — so much so that its creators, which include institutions like Harvard, Stanford, and OpenAI, have also released two new tools just to sift through the information they sourced from. One tool is for searching AI research papers and the other is for investigating country-level data on research and investment.

Most of the 2019 report basically confirms the continuation of trends we’ve highlighted in previous years. But to save you from having to trudge through its 290 pages, here are some of the more interesting and pertinent points:

• AI research is rocketing. Between 1998 and 2018, there’s been a 300 percent increase in the publication of peer-reviewed papers on AI. Attendance at conferences has also surged; the biggest, NeurIPS, is expecting 13,500 attendees this year, up 800 percent from 2012.
• AI education is equally popular. Enrollment in machine learning courses in universities and online continues to rise. Numbers are hard to summarize, but one good indicator is that AI is now the most popular specialization for computer science graduates in North America. Over 21 percent of CS PhDs choose to specialize in AI, which is more than double the second-most popular discipline: security / information assurance.
• The US is still the global leader in AI by most metrics. Although China publishes more AI papers than any other nation, work produced in the US has a greater impact, with US authors cited 40 percent more than the global average. The US also puts the most money into private AI investment (a shade under $12 billion compared to China in second place globally with $6.8 billion) and files many more AI patents than any other country (with three times more than the number two nation, Japan).
• AI algorithms are becoming faster and cheaper to train. Research means nothing unless it’s accessible, so this data point is particularly welcome. The AI Index team noted that the time needed to train a machine vision algorithm on a popular dataset (ImageNet) fell from around three hours in October 2017 to just 88 seconds in July 2019. Costs also fell, from thousands of dollars to double-digit figures.
• Self-driving cars received more private investment than any AI field. Just under 10 percent of global private investment went into autonomous vehicles, around $7.7 billion. That was followed by medical research and facial recognition (both attracting $4.7 billion), while the fastest-growing industrial AI fields were less flashy: robot process automation ($1 billion investment in 2018) and supply chain management (over $500 million).

All this is impressive, but one big caveat applies: no matter how fast AI improves, it’s never going to match the achievements accorded to it by pop culture and hyped headlines. This may seem pedantic or even obvious, but it’s worth remembering that, while the world of artificial intelligence is booming, AI itself is still limited in some important ways.

The best demonstration of this comes from a timeline of “human-level performance milestones” featured in the AI Index report; a history of moments when AI has matched or surpassed human-level expertise.

The timeline starts in the 1990s when programs first beat humans at checkers and chess, and accelerates with the recent machine learning boom, listing video games and board games where AI has came, saw, and conquered (Go in 2016, Dota 2 in 2018, etc.). This is mixed with miscellaneous tasks like human-level classification of skin cancer images in 2017 and in Chinese to English translation in 2018. (Many experts would take issue with that last achievement being included at all, and note that AI translation is still way behind humans.)

And while this list is impressive, it shouldn’t lead you to believe that AI superintelligence is nigh.

For a start, the majority of these milestones come from defeating humans in video games and board games — domains that, because of their clear rules and easy simulation, are particularly amenable to AI training. Such training usually relies on AI agents sinking many lifetimes’ worth of work into a single game, training hundreds of years in a solar day: a fact that highlights how quickly humans learn compared to computers.

Similarly, each achievements was set in a single domain. With very few exceptions, AI systems trained at one task can’t transfer what they’ve learned to another. A superhuman StarCraft II bot would lose to a five-year-old playing chess. And while an AI might be able to spot breast cancer tumors as accurately as an oncologist, it can’t do the same for lung cancer (let alone write a prescription or deliver a diagnosis). In other words: AI systems are single-use tools, not flexible intelligences that are stand-ins for humans.

But — and yes, there’s another but — that doesn’t mean AI isn’t incredibly useful. As this report shows, despite the limitations of machine learning, it continues to accelerate in terms of funding, interest, and technical achievements.

When thinking about AI limitations and promises, it’s good to remember the words of machine learning pioneer Andrew Ng: “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” We’re just beginning to find out what happens when those seconds are added up.

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