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首页> 外文会议>Charging amp; infrastructure symposium 2018 >Recent Achievements of Quantum Chemical Simulations: Material Screenings, Materials Morphology and Dynamics, Artificial Intelligence
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Recent Achievements of Quantum Chemical Simulations: Material Screenings, Materials Morphology and Dynamics, Artificial Intelligence

机译：量子化学模拟的最新成就：材料筛选，材料形态和动力学，人工智能

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Recent success of advanced computational chemistry in predicting chemical reactivity, or materials properties, rendered them a valuable and accepted means to tackle problems in academia and nowadays in industry. The recent development of new simulation methods and new computer architectures enables an enormous improvement of the productivity of research and development of new chemical synthesis and materials. Approaches like virtual high throughput screenings are highly scalable and allow a deep and very fast insight into the impact of new promising system modifications. The time to market and risk of new products development can be decreased significantly. The field of computational chemistry is diverse and offers a number of inherently different approaches suitable for different problems, targeting isolated molecules in the gas phase to extended solids, polymers and liquids. Simulating not only energy and derivative properties, but adding a time domain to the problem, accesses the dynamics of the system of interest. New methods are enabling the access to molecular level material morphology and dynamics of a material, which can have a crucial impact on materials properties and stability. For research a development (R&D) projects, computational chemistry is an ideal tool to test new ideas for chemical transformations and new materials on the molecular level, because the computations do not require synthesis or dedicated lab equipment. Furthermore, experimentally hard to characterize species and materials can be investigated to full detail. While many innovative simulation methods already exist, new simulation methods and potential game-changers are continuously being developed, i.e. current developments in chemical artificial intelligence shows the way to the future R&D. We present the application of modern quantum mechanics in materials optimization. We show how thousands of new material derivatives can be investigated in few days at high accuracy with virtual high throughput screenings and modern optimization algorithms. We present how material morphology and dynamics looks like at the atomic level, and what we can learn from that insight. And finally we show how artificial intelligence research is reaching the R&D in chemistry.
机译：先进的计算化学在预测化学反应性或材料特性方面的最新成功使它们成为解决学术界和当今工业问题的有价值且被接受的手段。新的仿真方法和新的计算机体系结构的最新发展极大地提高了新化学合成和材料研究与开发的生产率。虚拟高通量筛选等方法具有高度的可扩展性，可以深入而快速地洞察新的有前途的系统修改的影响。上市时间和新产品开发的风险可以大大减少。计算化学领域是多种多样的，并提供了许多适用于不同问题的固有不同方法，将气相中的分离分子靶向扩展的固体，聚合物和液体。不仅模拟能量和导数性质，而且为问题添加时域，都可以访问目标系统的动力学。新方法使人们能够获得分子水平的材料形态和材料动力学，这可能对材料的性能和稳定性产生至关重要的影响。对于研究（R＆D）项目，计算化学是在分子水平上测试化学转化新思想和新材料的理想工具，因为计算不需要合成或专用实验室设备。此外，可以详细地研究难以表征物种和材料的实验。尽管已经存在许多创新的仿真方法，但新的仿真方法和潜在的规则改变者正在不断开发，即化学人工智能的最新发展为未来的研发指明了道路。我们介绍了现代量子力学在材料优化中的应用。我们展示了如何通过虚拟高通量筛选和现代优化算法在几天之内高精度地研究成千上万的新材料衍生物。我们将介绍原子形态下的材料形态和动力学，以及从中可以学到的东西。最后，我们展示了人工智能研究如何达到化学领域的研发水平。

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