Lithium-ion batteries are the most established technology for applications ranging from personal electronics to electric vehicles, thanks to their competitive properties, such as high energy density and long lifespan. These characteristics have led lithium-ion batteries to become the gold standard across multiple industries, particularly in those where storing large amounts of energy within a limited volume or weight is essential.  

However, as technology advances, the demand for batteries with higher energy density, along with enhanced performance and safety, is rapidly rising. A major driving force behind this trend is the automotive industry, where over the past decade, the average energy density has risen by over 66%, from 150 Wh/kg to 250 Wh/kg. This rapid progress in battery performance has primarily resulted from R&D efforts on battery materials.

To meet the growing demand for batteries with higher energy density — driven by newer applications such as electric aviation — battery R&D needs to step up to the challenge and deliver novel materials with unprecedented performance levels.  Until recently, the industry has mostly relied on a trial-and-error approach to battery development. However, ushering in a new era of battery R&D requires embracing a rational design strategy for creating new materials.  

The electrolyte in battery R&D

The electrolyte is a crucial component in lithium-ion batteries as well as other battery technologies. This component plays a key role in the performance of batteries, as it facilitates the transfer of ions between electrodes, enabling energy storage and release during charging and discharging. As a result, the optimization of the electrolyte composition is a major focus of current R&D efforts.

The electrolyte is typically a solution containing ions that move from one electrode to the other, generating an electric current. Next-generation electrolytes often consist of a mixture of organic solvents, an ion salt, and additives specifically designed to boost performance.  

Due to its composition, the electrolyte is the most unstable component of a battery, making it prone to undergoing undesired reactions that can lead to aging, reduced performance, and safety risks. This poses a significant challenge for battery R&D, where a delicate balance between stability and performance is essential to ultimately develop the batteries of the future.  

The development of electrolyte formulations that meet high-quality standards is a complex and often time-consuming process. As the potential for further optimization diminishes, it has become imperative to integrate new approaches that enable the rational design of electrolyte formulations.  

Simulation tools for electrolyte characterization

Chemical simulations have emerged as a time- and cost-effective alternative to experimental trial-and-error approaches to R&D. Advanced tools that rely on multiscale simulations — combining methods such as molecular dynamics (MD), density functional theory (DFT), and machine learning — can now be seamlessly integrated into R&D workflows to accelerate the design and development of new materials and formulations. In particular, the rise of machine learning technology is driving critical and continuous improvements in the speed and accuracy of well-established simulation techniques.

As an AI-powered material design and discovery platform, QuantistryLab empowers users to predict, optimize, and design sustainable, cost-efficient materials through multiscale atomistic simulations, all accessible via a web browser. With no need for coding or expert knowledge, users can tap into deep scientific expertise and accelerate R&D with just a few clicks.

Among its diverse applications — including metals and alloys, lubricants and polymers, catalysis, and hydrogen fuel cells — QuantistryLab places a strong focus on battery development. The platform provides multiple tools to investigate battery materials, with the following features available to simulate and predict key properties of the electrolyte that can be integrated in battery development workflows.  

Electrolyte viscosity

The viscosity of the electrolyte is essential for efficient battery performance. Generally, a lower viscosity is desirable to ensure ion mobility, while a higher viscosity can hinder the movement of ions and reduce efficiency. When developing novel electrolyte formulations, studying their viscosity is a crucial step to predict battery performance. However, taking experimental measurements for each iteration of possible electrolyte formulations can quickly become time-consuming and resource intensive.

Chemical simulations can significantly reduce this burden. To determine the viscosity of an electrolyte formulation, molecular dynamics simulations are deployed using the Green-Kubo method, which relies on statistical mechanics to estimate the viscosity at equilibrium. Doing this is simple with QuantistryLab, as only a few clicks are required to prepare the desired electrolyte formulation and run a simulation workflow. This use case shows how QuantistryLab can predict the viscosity of a commercial electrolyte formulation, achieving excellent agreement with experimental results.  

Electrolyte density

The density of the electrolyte is a key property for quality control in development and manufacturing. Its density, primarily determined by the electrolyte’s chemical composition, should ideally remain constant during charge and discharge, though degradation or decomposition may cause changes. This makes density a reliable measure for ensuring the electrolyte composition meets all manufacturing requirements and thus preventing potential safety issues such as thermal runaway.  

Employing chemical simulations, the density of an electrolyte formulation can be calculated, offering a time- and cost-effective alternative to experimental measurements. QuantistryLab enables the user to model and predict the density of a preparation with just a few clicks by running molecular dynamics simulations of the chemical system of choice under the selected conditions of temperature and pressure. This use case demonstrates how QuantistryLab accurately predicts the density of various combinations of electrolyte solvents and salts, closely matching experimental data.

Electrolyte decomposition

Because the electrolyte is the most unstable component of a battery, it is prone to chemical reactions that cause its degradation, especially at high temperatures. This poses a major challenge for battery R&D, since electrolyte decomposition can significantly affect a battery’s cycle life and calendar life, while also producing toxic, corrosive, or even explosive chemical byproducts.

Studying electrolyte decomposition is extremely challenging due to the large number of possible reactions, which are in turn influenced by external conditions such as temperature. QuantistryLab provides insights into this complex network of reactions through its quantum nanoreactor feature, which relies on metadynamics simulations based on density functional theory (DFT), along with machine learning, to provide further insights into the chemicals formed. This use case showcases how QuantistryLab can identify multiple known products of the thermal decomposition of a commercial electrolyte formulation.

Electrolyte aging

As a battery operates, the electrolyte undergoes chemical reactions that gradually alter its original formulation. This process, known as aging, can significantly impact the performance of a battery over time. However, studying electrolyte aging is challenging with experimental methods, as it involves numerous compounds and the outcome of these aging reactions also depend on external conditions such as temperature.  

Chemical simulations are a powerful tool to investigate electrolyte aging, achieved by combining several techniques. This use case shows how QuantistryLab can identify the compounds formed during aging through the nanoreactor feature, and subsequently use these results to measure the viscosity of the aged electrolyte based on the compounds present in the solution.

The simulation toolbox

Chemical simulations have emerged as a technology with great potential to accelerate R&D workflows and provide detailed insights into complex processes that have traditionally been challenging to tackle experimentally. In combination with machine learning algorithms, simulations enable the rational design of novel materials across the full range of battery components, from electrode characterization to cutting-edge technologies such as solid-state electrolytes.    

Implementing simulation tools in battery R&D can significantly reduce the time and resources required for the development of novel, high-performance materials. QuantistryLab offers a comprehensive range of simulation tools, curated and tailored to each R&D challenge, thus empowering users to access deep scientific expertise and computational know-how directly through a web browser.  

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