Xanadu develops new method for quantum computer simulation of lithium batteries
Lithium-ion battery technology is one of the foundations of 21st century lifestyles and holds great promise for green energy storage. However, their development and improvement have not kept pace with the times.
In an ideal world, a better battery would have longer life, faster charging time, higher capacity, and lower cost; but in reality, complex electrochemistry means that performance tuning that improves one aspect Other aspects of performance tend to be degraded, and the effects of chemical and material adjustments are often too complex and difficult to interpret.
New techniques for quantum simulation promise to solve this problem: Quantum simulations reproduce the behavior of materials at the most fundamental chemical level. In theory, quantum simulation technology can perfectly understand lithium batteries, and the effects of tuning them. In reality, however, quantum simulations are powered by quantum computing technology, where currently available computing power is limited and quantum algorithms are few and far between.
Alain Delgado's team at Xanadu, a Canadian quantum computing company, has developed a method that promises to simulate lithium-ion batteries on a fault-tolerant quantum computer to provide the greatest insight into better performance. Their work was published on the preprint website arXiv under the title "How to Simulate Key Properties of Lithium-Ion Batteries with a Fault Tolerant Quantum Computer" [1]. These simulations have the ability to improve material properties, laying the foundation for industrial simulations at the quantum level.
Lithium-ion batteries contain various elements in different materials under different conditions. A battery consists of a positive electrode called a "cathode" that collects charge carriers, such as electrons, lithium ions; a negative electrode called an "anode", usually made of carbon, that produces the charge carriers; and an electrolyte material that can operate between positive and negative Ions are transported between electrodes. When the battery discharges, the anode reacts to release electrons from the lithium atoms, forming lithium ions; the electrons travel through the outside of the circuit to the cathode, while the lithium ions travel through the inner electrolyte to the cathode, where they combine with the electrons and become part of the crystal structure.
When the battery is being charged, the process is reversed.
Schematic of a typical lithium-ion battery. The negative electrode is usually graphitic carbon, which holds the lithium ions; the positive electrode is the source of the lithium ions. During discharge, lithium ions move from the negative electrode to the positive electrode within the battery. During charging, the process is reversed. The electrolyte transports lithium ions between the electrodes, while the separator acts as a physical barrier separating the cathode and anode. The negative and positive electrodes receive electrons from an external circuit during charging and discharging, respectively.
Typically, the anode stores more lithium than the cathode. Delgado publicly stated: "Cathode material is the main limiting factor in battery performance and is responsible for up to 50% of the total cost of the battery." Therefore, the improvement of the cathode is highly concerned.
A good starting point for any potential battery material is to understand its equilibrium voltage, which determines how much energy a battery can store. However, this voltage depends on the atomic structure of the cathode and the different materials formed inside it. Delgado and colleagues cite the example of the cathode material lithium cobalt oxide (LiCoO2). Lithium cobaltate also forms CoO2 when lithium ions migrate. So the equilibrium voltage depends on the balance between the two, which in turn depends on the electronic structure of each molecule.
Another important property is ionic mobility (the speed at which lithium ions move through the material's structure), which is also determined by the material's electronic structure.
Then there is the thermal stability of the cathode, which determines the safety of the battery. Because the cathode material is usually an oxide of lithium, oxygen can be released during the movement of lithium ions in and out of the cathode material; at the same time, lithium ions can form dendrites that extend on the electrolyte. This both consumes lithium ions and reduces the battery's capacity: the lithium heats up, and, if the dendrites extend across the gap, it can also short the battery. All of these create dangerous conditions for thermal runaway and eventual fire.
Knowing exactly how this all works is important for battery manufacturers, but it depends on the exact structure of the material at the atomic level.
Quantum computing for battery simulation. (a) Schematic depicting three key properties of Li-ion batteries. (b) Summarizes the main steps of the one-shot quantum algorithm implemented in this work. The ground state energy E of a given material can be obtained by running a quantization-based quantum phase estimation (QPE) algorithm on a quantum computer. (c) shows an example of measurable quantities that can be obtained. The cell voltage is given by the difference between the electrode chemical potentials (μ) calculated from the energy change (ΔE) of the cathode material; the activation energy (Ea), which is used to predict ionic mobility; and the Temperature curve.
Delgado said that all the properties of lithium-ion batteries should be available for quantum simulations in the near future. Subsequently, Delgado proposed the algorithms and computational properties required for these computations. These calculations determine the behavior of each electron included in the simulation. The simulation works by manipulating a quantum system so that each qubit represents a quantum state, such as the orbital state of an electron. However, the size of the simulation grows exponentially with the number of electrons, which is also an important limiting factor in the experiments.
Delgado's research team focused on a cathode material called dilithium iron silicate (Li2FeSiO4): the material's cell consists of 16 atoms (4 lithium, 2 iron, 2 silicon and 8 oxygen atoms) and 156 electrons. Simulating the behavior of each of these electrons is currently beyond the capabilities of today's quantum computers, but Delgado et al. show how the calculations can be optimized to produce useful predictions.
(a) Conventional unit cell of silicate Li2FeSiO4 cathode material. (b) The crystal structure of the βII polycrystal, in which all tetrahedra point in the same direction. LiO4 (green) runs parallel to the tetrahedral rows of SiO4 (blue) and FeO4 (brown), representing the quadruple coordination of lithium, silicon and iron atoms with oxygen atoms at the vertices.
The experimental results are a detailed blueprint for performing these calculations, discussing the gate cost, qubit cost, and estimated runtime of implementing a quantum algorithm to calculate the ground state energy of the Li2FeSiO4 cathode material, and it is expected that quantum computers powerful enough to perform these calculations will be available in the future. "To the best of our knowledge, this is the first attempt to estimate the resources required to perform quantum algorithms aimed at high-precision ground-state energy calculations for realistic cathode materials," said the researchers.
Non-Clifford gate costs for initial state preparation and quantum phase estimation. (a) Non-Clifford gate cost in the initial state fabrication circuit. (b) Toffoli gate cost of the quantum phase estimation algorithm. All calculations are performed for a unit cell of Li2FeSiO4 with 156 electrons.
An estimate of the time required to run the algorithm. The figure illustrates the total runtime for synthesizing all Toffoli gates, assuming the same number of plane waves used in state preparation and quantum phase estimation.
This interesting work shows how far quantum simulations have come, and how they might develop in the near future. If this is an early result, it will power the next generation of better lithium-ion battery devices. But its impact is far more profound. Quantum simulations herald a new era of designing materials at the quantum level with performance beyond the limits of anything we have today.
This should be pretty exciting.
Reference link:
[1] https://arxiv.org/abs/2204.11890
