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Tools and Applications of the Schrödinger Suite for Battery Materials Simulations
The design and manufacturing of safer, less expensive, and highly efficient energy storage devices is a critical challenge in a wide variety of industries including the automotive, aviation, and energy sectors. Atomistic-scale materials modeling for battery applications has become an essential tool for the development of novel device components - cathodes, anodes, and electrolytes - that support higher power density, capacity, rate capability, faster charging, and improved degradation resilience. In this presentation, we will review Schrödinger’s Materials Science software platform that provides a powerful atomistic-scale modeling solution for comprehensive analysis of battery materials. The review will include the latest examples in physics-based and machine-learning predictions of the key materials properties including, but not limited to, ion diffusion, mechanical response, and electrochemical response in electrodes and electrolytes, as well as dielectric properties of potential electrolyte compounds.

薛定谔(Schrödinger)软件套件应用于电池材料创新
在众多行业中,包括汽车,航空和能源工业,设计和制造更安全,更便宜,更高效的能量存储设备是一项严峻的挑战。可应用于电池材料的原子尺度下的材料计算模拟已成为开发新型设备组件(阴极,阳极和电解质)的重要工具。这些新型组件可支持更高的功率密度,容量,倍率能力,更快的充电速度和更高的降解弹性。在本次网络研讨会中,我们将介绍Schrödinger的材料科学软件平台(MSS),及如何利用此平台提供的功能强大的原子尺度计算模拟解决方案对电池材料进行全面分析。我们将包括基于物理学和机器学习预测的关键材料特性的最新示例,这些特性包括,但不限于,电极和电解质中的离子扩散,机械响应和电化学响应,以及潜在电解质化合物的介电特性。

Jun 8, 2021 09:00 AM in Beijing, Shanghai

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Speakers

Yuling An
Product Manager of Machine Learning and Informatics for Materials Science @Schrödinger