Chinese Materials Sciences Webinar Series, Online

May 10, 2021 to June 8, 2021

We hope you'll take the opportunity to join us for the materials science webinar series taking place from April 7 - June 8, 2021. Each of the webinars will cover different topics in material science featuring Schrödinger's Yuling An as the presenter. To register for any of the sessions, please use the links below.
 
欢迎大家参加我们的材料科学中文网络讲座系列。本系列将在2021年4月7日到6月7日,北京时间上午9点举办。讲座将涉及不同的材料科学的课题,并录制为视频,方便大家回看。欢迎大家点击下面的链接报名。

Agenda:

DateTimePresentationRegister
Wednesday
April 7
9:00 AM  CSTCombining High-throughput Atomic Scale Simulation and Deep Reinforcement Learning in the Discovery of Novel OLED Materials with Targeted Optoelectronic Properties
Featuring Dr. Yuling An / 安羽翎博士

Hole-transporting materials (HTM) are a critical class of organic semiconductors, required for the fabrication of a variety of state-of-the-art display and semiconductor devices. In this work, we apply the technique of using Recurrent Neural Networks (RNN) as a generative model on SMILES representation of molecules, which has demonstrated success in drug discovery, to design HTMs targeting specific properties. A set of training compounds were selected from OLED material chemical space expanded from commercial catalogs to run optoelectronic properties calculations using Quantum Mechanics (QM) tools from Schrӧdinger’s Materials Science Suite. Reinforcement learning was used to fine-tune pre-trained RNN based on both the prior likelihood and a scoring function defined by the user, thus allowing optimization of multiple properties at once, which has been a huge challenge in material design. We demonstrated that by carefully defining the applicable chemical space, and providing accurate physics-based property calculation on a large number of compounds, our data-driven approach, with the aid of advanced machine learning techniques, can be expanded to many different domains to systematically discover novel materials with targeted properties.

高通量原子尺度计算模拟与深度强化学习结合应用于具有针对光电特性的新型OLED材料的研发
空穴传输材料(HTM)作为有机半导体的关键类别是制造各种先进的显示器和半导体器件所必需的。在这项研究中,我们应用递归神经网络(RNN)作用于分子的SMILES的技术(该技术已在药物研发中有所成绩),来设计具有针对特定光电特性的HTM。我们从OLED材料化学领域(商业目录扩展而来)中选择了一组训练样例化合物,使用Schrӧdinger材料科学软件套件中的量子力学(QM)工具进行光电性能计算。强化学习根据先验可能性和用户定义的函数对预先训练的RNN进行微调,从而允许一次优化多个属性,这在材料设计中一直是一项巨大的挑战。我们的研究证明,通过选用合适的化学空间,并提供大量准确的基于物理学的化合物的性质计算数据,借助于先进的机器学习技术,我们的数据驱动的方法可以扩展到许多不同的领域,从而系统地发现具有针对性能的新型材料。
VIEW RECORDING
Monday
May 10
9:00 AM CSTPolymer Innovation with Computational Chemistry
Featuring Dr. Yuling An / 安羽翎博士
 

Polymers have found use in industries from aerospace composites to food packaging to drug delivery.  Their usefulness is tied to the intersection of chemistry, macromolecular structure, and microscale behavior.  As the use of polymers has grown so has the need for better tools to predict and understand how the chemistry, processing, and macromolecular structure impacts performance. This webinar will review computational chemistry developed for polymers and how it has become a practical tool for polymer research engineers and scientists to have in their toolbox.

  • Polymer property predictions using chemically informed simulations
  • Computational chemistry techniques for thermosets and thermoplastics 
  • Examples in area of epoxy resins, polyacrylates, polyolefins
  • Techniques for integrating simulation into industrial research and development

利用计算化学来实现聚合物领域的创新
聚合物已广泛应用于航空复合材料,食品包装,药物输送等多方行业。它们的实用性与化学,大分子结构和微观行为交叉联系在一起。随着聚合物用途的增长,对更好的了解并预测化学和大分子结构是如何影响性能的工具的需求也越来越大。我们借此网络研讨会将回顾聚合物开发的计算化学发展技术,以及它们如何成为可以帮助聚合物研究工程师和科学家的实用工具。
  • 使用化学信息模拟进行聚合物性能预测
  • 应用于热固性和热塑性塑料的计算化学技术
  • 环氧树脂,聚丙烯酸酯,聚烯烃领域的实例
  • 如何将计算模拟技术融合到工业研发中
VIEW RECORDING 
Tuesday
June 8
9:00 AM CSTTools and Applications of the Schrödinger Suite for Battery Materials Simulations
Featuring Dr. Yuling An / 安羽翎博士
 

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。
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