How Ford used qubits to model battery materials for electric vehicles

Quantum researchers at Ford have just released a new preprint study that uses a quantum computer to model key electric vehicle (EV) battery materials. While the results reveal nothing new about lithium-ion batteries, they show how more powerful quantum computers could be used to accurately simulate complex chemical reactions in the future.

To discover and test new materials with computers, researchers have to split the process into many separate calculations: one set for all the relevant properties of each individual molecule, another for how those properties are affected by minute environmental changes like temperature changes, another for all possible ways that any two molecules can interact with each other, and so on and so on. Even something that sounds simple, like the bonding of two hydrogen molecules, requires incredibly deep calculations.

However, developing materials on the computer has one major advantage: researchers don’t have to physically carry out every possible experiment, which can be incredibly time-consuming. Tools like AI and machine learning have been able to speed up the research process to develop novel materials, but quantum computing offers the potential to make it even faster. For electric vehicles, the search for better materials could lead to longer-lasting, faster-charging, and more powerful batteries.

Conventional computers use binary bits – which can be a zero or a one – to do all their calculations. While they’re capable of incredible things, there are some problems, like high-fidelity molecular modeling, that they simply can’t handle – and, due to the nature of the computations involved, may never be. Once researchers model more than a few atoms, the calculations become too large and time-consuming, requiring them to rely on approximations that reduce the accuracy of the simulation.

Instead of normal bits, quantum computers use qubits, which can be a zero, a one, or both at the same time. Qubits can also be entangled, rotated, and manipulated in other wild quantum ways to carry more information. This gives them the ability to solve problems that conventional computers can’t solve – including accurately modeling molecular reactions. In addition, molecules are quantum by nature and can therefore be mapped more accurately to qubits, which are represented as waveforms.

Unfortunately, much of this is still theory. Quantum computers are not yet powerful enough or reliable enough to be commercially viable on a large scale. There’s also a knowledge gap – because quantum computers work in a completely different way than traditional computers, researchers have yet to learn how best to 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 into play. Ford is interested in making batteries that are safer, more energy and power dense, and easier to recycle. To do this, they need to understand the chemical properties of potential new materials, such as charging and discharging mechanisms, as well as electrochemical and thermal stability.

The team wanted to calculate the ground-state energy (or normal atomic energy state) of LiCoO2, a material that could potentially be used in lithium-ion batteries. To do this, they used an algorithm called the Variational Quantum Eigensolver (VQE) to simulate the Li2Co2O4 and Co2O4 gas-phase models (basically the simplest possible form of a chemical reaction) that represent the charging and discharging of the battery. VQE uses a hybrid quantum-classical approach with the quantum computer (in this case 20 qubits in an IBM statevector simulator) currently being used to solve the parts of the molecular simulation that benefit most from its unique properties. Everything else is done by conventional computers.

Because this was a proof-of-concept for quantum computing, the team tested three approaches using 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). They compared not only the quantitative results but also the quantum resources required to perform the calculations accurately with classical wavefunction-based approaches. They found that k-UpCCGSD provided results similar to UCCSD at a lower cost, and that the results of the VQE methods were consistent with those obtained with classical methods – such as Coupled-Cluster Singles and Doubles (CCSD) and Complete Active Space Configuration Interaction (CASCI). .

Although not quite there yet, the researchers concluded that on the types of quantum computers that will become available in the near future, quantum-based computational chemistry “will play a crucial role in finding potential materials that improve battery performance and – improve robustness”. While they used a 20-qubit simulator, they suggest that a 400-qubit quantum computer (which will be available soon) would be necessary to fully model the Li2Co2O4 and Co2O4 system they are considering.

All of this is part of Ford’s attempt to become a dominant EV maker. Trucks like the F-150 Lightning are pushing the boundaries of current battery technology, so further advances – likely aided by quantum chemistry – will become increasingly necessary as the world moves away from gas-powered cars. And Ford isn’t the only player thinking about using quantum to get ahead of the battery chemistry game. IBM is also working with Mercedes and Mitsubishi on using quantum computing to reinvent the electric vehicle battery.

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