Lithium-ion batteries are the leading choice in today’s electric vehicle and battery energy storage system industries, but they contain a number of critical minerals — including lithium, cobalt, nickel, and graphite — that are considered essential for economic and national security reasons, and therefore vulnerable to supply chain disruptions. As renewable energy, electrified infrastructure, and high-power digital technologies continue to grow, there is an increasing need for energy storage systems that are low-cost, resource-abundant, and capable of fast charging and discharging. 

That need, among other reasons, has motivated a group of researchers — based at MIT and led by Ju Li, the Carl Richard Soderberg Professor of Power Engineering in the departments of Nuclear Science and Engineering (NSE) and Materials Science and Engineering — to develop complementary energy storage solutions. 

The team is looking, in particular, at sodium-metal batteries, which offer several attractive features. Sodium is about 1,000 times more abundant than lithium and, pound for pound, about one-hundredth the cost. A key challenge, however, is that sodium metal is highly reactive, making it difficult for these batteries to achieve both long-term stability and fast cycling. 

A new paper in the journal Joule — written by 15 members of the MIT team and published online this week — shows how this dilemma can be addressed by finding the right electrolyte for this battery system.

Electrolytes behaving badly

An electrolyte is one of three main components of a battery, along with the negative electrode (the anode) and the positive electrode (the cathode). The electrolyte acts like the “blood” of the battery, allowing electrically charged ions to move between the two electrodes. “The electrolyte is supposed to just transmit those ions,” explains Li. “It’s supposed to be an ion conductor.” But unfortunately, most electrolytes get involved in unwanted chemical reactions with the electrodes, which can greatly undermine battery stability.

The consequences of these “side reactions” can be severe, says Weiyin Chen, a postdoc in NSE and one of four lead authors of the Joule paper. Insoluble compounds produced during the reactions can build up on the electrodes, creating a barrier that blocks ion transport and can eventually cause the battery to fail. 

Until recently, Chen says, no electrolyte used in sodium-metal batteries was fully stable against these unwanted reactions at both the anode and cathode, even though such stability is essential for rechargeable batteries to achieve a long cycle life. An initial breakthrough occurred in 2021, when the Li group and their collaborators identified a “sulfonamide” molecule — consisting of sulfur, oxygen, and nitrogen atoms — that, when used as a solvent, “is magically stable at both electrodes in lithium batteries,” according to Li. This molecule is known as DMTMSA. 

Building on that discovery, Li and his colleagues set out to see if related molecules could improve sodium batteries. The goal was not only to maintain stability, but also to enable fast charging and discharging. If charging is too slow, it could take all night to recharge, and if discharging is too slow, the battery cannot deliver much power when needed.

How did the solvent cross the road?

Chen explains the idea with an analogy: Suppose you need to cross a street jam-packed with pedestrians, much like ions traveling from one electrode to another. “You can move more quickly through the crowd with a small backpack that is snug against your body, rather than dragging a bulky suitcase on wheels,” Chen says. 

A similar situation occurs in batteries: When sodium ions are surrounded by smaller solvents, they can move faster than when they are surrounded by larger, bulkier solvents. Faster ion transport enables more-rapid charging and discharging. The team’s goal, accordingly, was to identify solvent molecules that are small enough to improve ion transport while still maintaining electrolyte stability.

There is, however, a complicating factor — a trade-off to be addressed: Faster ion transport often comes at the expense of electrolyte stability. Many highly conductive electrolytes react more easily with the electrodes, shortening battery life. Fortunately for their plan, Li says, “reducing the size of solvents provides a new pathway to overcome this trade-off.” 

The question then becomes how to find a smaller solvent that has other desirable properties. The idea they adopted is to look for molecules that are “congeneric,” says Li, “meaning that they belong to a similar family and are molecularly similar.” In particular, they searched for molecules related to DMTMSA, hoping to find candidates that were smaller but could retain the stability that made DMTMSA so promising.

Chia-Wei Hsu, an MIT PhD student in materials science and engineering, created an AI-guided algorithm, which designed 100,000 candidate molecules on his computer within 24 hours. Hsu then narrowed down the pool to 200 candidates by applying a set of technical criteria — including similarity in shape to DMTMSA and comparable electronic properties. Twenty-seven representative candidates covering the full range of possibilities were selected for experimental tests. 

“We tested them all under the same conditions to make it a fair, head-to-head competition,” Chen says. A clear winner emerged, a solvent called DMFSA, which was both the smallest and the best.

Small is beautiful

This work, claims Jinhyuk Lee, an associate professor of materials engineering at McGill University who is not part of the study, “addresses one of the most persistent challenges in battery research: improving battery performance at high charging and discharging rates without sacrificing long-term stability. By carefully tailoring the size of solvent molecules, the authors demonstrate a new design strategy that could enable lower-cost, higher performance batteries.” 

The group is not done. A new search is underway to find an even better solvent. This time, the approach is similar, but DMFSA (rather than the larger DMTMSA molecule) serves as the starting point. Chen believes the new solvents they are uncovering could eventually lead to rechargeable sodium-metal batteries that combine low-cost, abundant materials with fast charging and high-power performance, opening the door to broader energy storage applications.

The overriding goal of this work, the authors emphasize, is not only to advance sodium batteries. It’s also to introduce a new approach to electrolyte design that uses solvent size and molecular similarity as the key guideposts. Viewing the research in this light, sodium-metal batteries serve as a model system for demonstrating a more general design principle.

“Because the concept is broadly applicable,” Lee comments, “its impact could extend well beyond sodium batteries and influence the design of a wide range of future energy storage technologies.”

This work was supported, in part, by a National Research Foundation of Korea grant funded by the government of Korea government, as well as U.S. National Science Foundation graduate research fellowship. The characterization equipment used in this project is partly from the MIT.nano Characterization Facilities. 

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.