Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing–structure–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

中文翻译：

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云中3D材料科学的BisQue：微观结构-属性链接

加快新型先进材料的设计和开发是现代材料科学的优先事项之一。这些努力严重依赖于综合材料网络基础设施的开发，这些基础设施可以实现有效的数据存储，管理，共享和协作，以及有助于建立处理结构，属性之间关系的计算工具集成。在此贡献中，我们将此类计算工具的实现呈现到称为BisQue的基于云的平台中（Kvilekval等人，Bioinformatics 26（4）：554，2010）。首先，我们将BisQue的现状描述为云中跨学科研究的开源平台，以及其在3D材料科学中的潜力。然后，我们演示如何主要针对处理，结构，属性关系的新计算工具，可以实现到系统中。具体来说，在这项工作中，我们为BisQue开发了一个模块，该模块能够对两相材料的有效屈服强度进行微结构敏感的预测。为此，我们在BisQue平台中提供了一种计算有效的数据驱动模型的实现。新模块可以在线使用（网址：https：//bisque.ece.ucsb.edu/module_service/Composite_Strength/），并且可以从网络浏览器中使用，而无需任何特殊软件，并且对用户端的计算要求最低。基于包含（i）合成生成和（ii）从实验中获得的大量3D样本的3D微结构信息的数据集，在两种不同方法的案例研究中证明了该模块用于快速特性筛选的功能。