The development of rechargeable Li-ion batteries (LIBs) has revolutionized electric vehicles and portable electronic devices. Further advancements are needed to improve the power density, safety, reliability, and lifetime of LIBs. Over the past few decades, atomistic modeling of battery materials has complemented experimental characterization techniques and has become an integral part of the development of new technologies. Reliable atomic scale modeling enables rapid initial evaluation of large chemical and material design space accelerating the development cycle of next-generation battery technologies.
In this webinar, we will demonstrate how Schrödinger’s advanced digital chemistry platform can be leveraged to accelerate the design and discovery of next-generation battery materials with improved properties. We will discuss the application of both physics-based and machine learning techniques for understanding structure-property relationships of different components of batteries including electrodes, electrolytes and electrode-electrolyte interfaces. We also discuss the automated active learning framework for the development of state-of-the-art neural network force fields for modeling liquid electrolytes. The framework allows training the force field using highly accurate range-separated hybrid density functional theory data which enables accurate prediction of critical bulk properties of high-performance liquid electrolytes for application in advanced batteries.
Key Learning Objectives:
- Understand predictive capabilities of physics-based modeling for battery materials
- Learn how automated high throughput simulation workflows enable rapid screening of new battery material candidates
- Application of advanced neural network force fields for accurate electrolyte property prediction
Who Should Attend:
- Synthetic Chemists
- Materials Scientists
- Digitization Managers
- R&D Scientists designing novel battery materials
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