尽管从第一原理设计高通量计算材料的多个成功应用，但仍有许多因素阻碍其未来的采用。特别重要的是，以可靠的方式提供高保真度的能力有限，非专家用户的访问能力有限。我们提出了一种新的方法，高保真度的第一性原理模拟技术，混合屏蔽交换（HSE）和GW近似的密度泛函理论的应用程序的例子，标准化，并提供在线访问和可重复的设置。我们应用这种方法来提取电子带隙和能带结构的71种材料，从纯元素到III-V族和II-VI族化合物，三元氧化物和合金。我们发现，对于HSE和G 0 W 0，平均相对误差适合在20%以内，而对于传统的广义梯度近似的误差是55%。对于HSE，我们发现公共云提供商集中提供的最新服务器上的平均计算时间在48小时内。这项工作提供了一个具有成本效益的，可访问的和可重复的实际配方进行高保真第一性原理计算的电子材料在一个高通量的方式。
英文摘要：
Despite multiple successful applications of high-throughput computational materials design from first principles, there is a number of factors that inhibit its future adoption. Of particular importance are limited ability to provide high fidelity in a reliable manner and limited accessibility to non-expert users. We present example applications of a novel approach, where high-fidelity first-principles simulation techniques, Density Functional Theory with Hybrid Screened Exchange (HSE) and GW approximation, are standardized and made available online in an accessible and repeatable setting. We apply this approach to extract electronic band gaps and band structures for a diverse set of 71 materials ranging from pure elements to III-V and II-VI compounds, ternary oxides and alloys. We find that for HSE and G0W0, the average relative error fits within 20%, whereas for conventional Generalized Gradient Approximation the error is 55%. For HSE we find the average calculation time on an up-to-date server centrally available from a public cloud provider to fit within 48 hours. This work provides a cost-effective, accessible and repeatable practical recipe for performing high-fidelity first-principles calculations of electronic materials in a high-throughput manner.
Tuning the optoelectronic properties of wide bandgap perovskites: Data-driven insights from combinatorial synthesis and high-throughput experimentation
Empa–Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术研究院）
High-throughput, Non-Destructive, Three-Dimensional Imaging of GaN Threading Dislocations with in-Plane Burgers Vector Component via Phase-Contrast Microscopy
High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniques
Peking University（北京大学）；Eastern Institute of Technology（东方理工大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）；The University of Toledo（托莱多大学）；WuXi AppTec（药明康德）；Peng Cheng Laboratory（鹏城实验室）；Southern University of Science and Technology（南方科技大学）
