How Ford models EV battery materials using qubits | Daily News Byte

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Quantum researchers at Ford have just published a new preprint study modeling important electric vehicle (EV) battery materials using a quantum computer. While the results don’t reveal anything new about lithium-ion batteries, they do show how more powerful quantum computers could be used to accurately simulate complex chemical reactions in the future.

To discover and test new materials using computers, researchers have to break the process into many separate calculations: One set for all the relevant properties of each single molecule, another for how those properties are affected this of the smallest changes in the environment such as fluctuating temperatures, another for all the possible ways any two molecules can interact together, and on and on. Even something as simple as two hydrogen molecules bonding requires incredibly deep calculations.

But developing materials using computers has a big advantage: researchers don’t have to do every possible physical experiment, which can be incredibly time-consuming. Tools like AI and machine learning have accelerated the research process for developing novel materials, but quantum computing offers the potential to make it even faster. For EVs, finding better materials could lead to longer-lasting, faster-charging, and more powerful batteries.

Traditional computers use binary bits—which can be either zero or one—to do all their calculations. While they’re capable of incredible things, there are some problems like highly accurate molecular modeling that they simply don’t have the power to handle—and given the kinds of calculations involved, possibly never will. When researchers model more than a few atoms, the calculations become so large and time-consuming that they have to rely on approximations that reduce the accuracy of the simulation.

Instead of regular bits, quantum computers use qubits that can be zero, one, or both at the same time. Qubits can also be shocked, spun, and manipulated in other wild quantum ways to carry more information. This gives them the power to solve problems beyond the reach of traditional computers—including the precise modeling of molecular reactions. Further, molecules are quantum in nature, and therefore map more precisely to qubits, which are represented as waveforms.

Unfortunately, much of this is still theoretical. Quantum computers are not yet powerful enough or reliable enough to be widely commercially viable. There’s also a knowledge gap—because quantum computers work in completely different ways than traditional computers, researchers still have to learn how to best use them.

[Related: Scientists use quantum computing to create glass that cuts the need for AC by a third]

This is where Ford’s research comes in. Ford is interested in making batteries that are safer, have more energy and are thicker, and easier to recycle. To do that, they need to understand the chemical properties of potential new materials such as charge and discharge mechanisms, as well as electrochemical and thermal stability.

The team wanted to calculate the ground-state energy (or the normal atomic energy state) of LiCoO2, a material that could potentially be used in lithium ion batteries. They did this using an algorithm called the variational quantum eigensolver (VQE) to simulate the Li2Co2O4 and Co2O4 gas-phase models (basically, the simplest form of chemical reaction possible) that represent the charge and discharge of the battery. . VQE uses a hybrid quantum-classical approach on a quantum computer (in this case, 20 qubits in an IBM statevector simulator) used only to solve parts of the molecular simulation that take advantage of its unique properties. Everything else is handled by traditional computers.

As this is a proof-of-concept for quantum computing, the team tested three approaches to VQE: unitary coupled-cluster singles and doubles (UCCSD), unitary coupled-cluster generalized singles and doubles (UCCGSD) and k-unitary pair coupled – cluster generalized singles and doubles (k-UpCCGSD). As well as comparing the quantitative results, they compared the quantitative resources required to perform the calculations accurately with methods based on the classical wavefunction. They found that k-UpCCGSD produced similar results to UCCSD at lower cost, and results from VQE methods agreed with those obtained using classical methods—such as coupled-cluster singles and doubles (CCSD) and complete active space configuration interaction (CASCI) .

Although not there yet, the researchers concluded that quantum-based computational chemistry on the types of quantum computers that will be available in the near future will play “an important role to find potential materials that can enhance performance of battery and stability.” While they use a 20-qubit simulator, they suggest a 400-qubit quantum computer (which will soon become available) is required to fully model the Li2Co2O4 and Co2O4 systems they are considering.

It’s all part of Ford’s attempt to become a dominant EV manufacturer. Trucks like its F-150 Lightning are pushing the limits of current battery technology, so further advances—likely aided by quantum chemistry—will be increasingly necessary as the world moves away from combustion-powered vehicles. of gas. And Ford isn’t the only player thinking of using quantum to get ahead of the game in battery chemistry. IBM is also working with Mercedes and Mitsubishi on using quantum computers to reinvent the EV battery.

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