Will AI Help Us Build Better Batteries?

Global Head of Research

We have written a series of blog articles about different things artificial intelligence (AI) is being used for to help advance other megatrends: 

We believe in continuing to not only talk about AI but also find ways to connect it back to how it is catalyzing advances in other megatrends. This way, it can be viewed less as a black box of algorithmic complexity and more as something that is focused on helping solve concrete problems in the world. 

A Brief Primer on Electrochemical Batteries1 

What we know today as “lithium-ion” batteries fall into the class of “electrochemical batteries.” For the battery to generate power, the chemical process has to generate electrons, and for the battery to be “recharged,” it has to store electrons. 

The structure of the battery involves the anode (negative side), electrolyte and cathode (positive side). The current that the battery can generate relates to the number of electrons flowing across from negative to positive, and the voltage relates to the force with which the electrons are traveling. 

Using the battery—i.e., using your smartphone or driving your electric car—means that the electrons are flowing from the anode through the electrolyte to the cathode. Charging your devices means that you are forcing the process to occur in reverse, where the electrons are leaving the cathode, going back across the electrolyte and ending up in the anode. 

Why Do We Have to Know All of That?

Some of you might be like me and think, “my last chemistry class was more than 20 years ago.” The reason we set that foundation, however, is that it now allows us to think in terms of the following:

  • The different parts of the battery can be fashioned out of different elements. 
  • Changing the mix of metals in the cathode, for example, may impact the energy density, speed of charging, heat dispersion or other battery characteristics. 
  • Researchers can experiment with all sorts of different anodes, cathodes and electrolytes as they seek to optimize the characteristics of a given battery to its use case. 

Now, we can better understand the ways in which an artificial intelligence process can be utilized to seek to improve the different characteristics of the batteries that we use. 

Who Wants Electric Vehicles to Charge Faster?

One of the many obstacles to the wider usage of electric vehicles is how people compare the time it takes to fill a tank with gasoline or diesel to the time it takes to charge a battery to the appropriate level. Since filling the tank is much faster, they opt for the vehicle with the internal combustion engine over the vehicle with the electric battery. 

There is huge marketability for automobile manufacturers and battery-makers for every unit of time they can shave off charging times. 

Researchers at Carnegie Mellon used a robotic system to run dozens of experiments designed to generate different electrolytes that could enable lithium-ion batteries to charge faster. The system is known as Clio, and it was able to mix different solutions together as well as measure performance against critical battery benchmarks. These results were then fed into a machine-learning system known as Dragonfly.2  

Dragonfly is where the process starts to get exciting—the system is designed to propose possible combinations of chemicals to be used in the electrolytes that could possibly work even better. Using this process during this particular period led to six different electrolyte solutions that outperformed a standard one when they were placed into standard battery test cells. The best option showed a 13% improvement relative to the top-performing battery baseline.3  

In reality, electrolyte ingredients can be mixed and matched in billions of different ways, but the benefit of using the system of Clio and Dragonfly working together is that it can get through a wider array of possibilities faster than humans alone. Dragonfly also isn’t equipped with information about chemistry or batteries, so it doesn’t bring the “bias of previous knowledge or experience” to the process. 

Using AI to Help the Progress of Solid-State Batteries

While the aforementioned path involves improving liquid electrolytes, it is not the only critical area of battery research today.

If the flammable liquid electrolyte is replaced by a stable solid, it’s possible that there would be improvements in battery safety, lifetime and energy density. However, finding the appropriate materials to facilitate building solid-state batteries that fit all specifications and can be produced at scale is not a simple matter. 

Researchers at Stanford have noted a particular process where they compile data on 40 materials with both good and bad measured room temperature lithium conductivity values. This particular characteristic is thought to be the most restrictive of all the different constraints on candidate materials. The 40 examples are “shown” to a logistic regression classifier, which can “learn” to predict whether the material performed well or not based on the atomistic structure. After the training phase, the model can then evaluate more than 12,000 lithium-containing solids and find around 1,000 of them that have a better than 50% chance of exhibiting fast lithium conduction. 

Progressing solid-state batteries along the development path is, therefore, another clear use case for artificial intelligence. 

Conclusion: Energy Storage Is One of the Most Important Considerations for the Coming Decades

Having better energy storage solutions will help global society in myriad different ways. The classic case: there are intermittent power generation sources like solar and wind that can use batteries to equilibrate the flows of energy across time. However, I think we’d all love smartphones that don’t need a charge for a week or two or electric vehicle batteries with a long range that can charge in similar times to what it previously took to fill up at a gas station. Those interested in energy storage solutions and possible investments would do well to look more deeply at the WisdomTree Battery Value Chain and Innovation Fund (WBAT). On the other hand, those interested in how artificial intelligence can supercharge many different megatrends may want to look more closely at the WisdomTree Artificial Intelligence and Innovation Fund (WTAI).



1 Source: https://www.volts.wtf/p/a-primer-on-lithium-ion-batteries#details.
2 Source: James Temple, “How robots and AI are helping develop better batteries,” MIT Technology Review, 9/27/22. 
3 Source: Temple, 9/27/22. 
4 Source: https://reedgroup.stanford.edu/research/eletrolyte.html.


Christopher Gannatti is an employee of WisdomTree UK Limited, a European subsidiary of WisdomTree Asset Management Inc.’s parent company, WisdomTree Investments, Inc.

Important Risks Related to this Article

WBAT: There are risks associated with investing, including the possible loss of principal. The Fund invests in the equity securities of exchange-listed companies globally involved in the investment themes of battery and energy storage solutions (“BESS”) and innovation. The value chain of BESS companies is divided into four categories: raw materials, manufacturing, enablers and emerging technologies. Innovation companies are those that introduce a new, creative or different technologically enabled product or service in seeking to potentially change an industry landscape, as well as companies that service those innovative technologies. The Fund invests in the securities included in, or representative of, its Index regardless of their investment merit. The Fund does not attempt to outperform its Index or take defensive positions in declining markets, and the Index may not perform as intended. Please read the Fund’s prospectus for specific details regarding the Fund’s risk profile.

WTAI: There are risks associated with investing, including the possible loss of principal. The Fund invests in companies primarily involved in the investment theme of artificial intelligence (AI) and innovation. Companies engaged in AI typically face intense competition and potentially rapid product obsolescence. These companies are also heavily dependent on intellectual property rights and may be adversely affected by the loss or impairment of those rights. Additionally, AI companies typically invest significant amounts of spending in research and development, and there is no guarantee that the products or services produced by these companies will be successful. Companies that are capitalizing on innovation and developing technologies to displace older technologies or create new markets may not be successful. The Fund invests in the securities included in, or representative of, its Index regardless of their investment merit, and the Fund does not attempt to outperform its Index or take defensive positions in declining markets. The composition of the Index is governed by an Index Committee, and the Index may not perform as intended. Please read the Fund’s prospectus for specific details regarding the Fund’s risk profile.


Related Blogs

AI Continues to Build the Foundation for a Remarkable Future in Biology

Can AI Replace People? The Truck-Driving Case Study

The World Needs More Metals—Maybe AI Can Find Them

Related Funds

WisdomTree Artificial Intelligence and Innovation Fund

WisdomTree Battery Value Chain and Innovation Fund


About the Contributor
Global Head of Research

Christopher Gannatti began at WisdomTree as a Research Analyst in December 2010, working directly with Jeremy Schwartz, CFA®, Director of Research. In January of 2014, he was promoted to Associate Director of Research where he was responsible to lead different groups of analysts and strategists within the broader Research team at WisdomTree. In February of 2018, Christopher was promoted to Head of Research, Europe, where he was based out of WisdomTree’s London office and was responsible for the full WisdomTree research effort within the European market, as well as supporting the UCITs platform globally. In November 2021, Christopher was promoted to Global Head of Research, now responsible for numerous communications on investment strategy globally, particularly in the thematic equity space. Christopher came to WisdomTree from Lord Abbett, where he worked for four and a half years as a Regional Consultant. He received his MBA in Quantitative Finance, Accounting, and Economics from NYU’s Stern School of Business in 2010, and he received his bachelor’s degree from Colgate University in Economics in 2006. Christopher is a holder of the Chartered Financial Analyst Designation.