Elements: Cyber-infrastructure for Interactive Computation and Display of Materials Datasets

该项目将通过创建一个名为TINC（交互式计算工具包）的软件基础设施来加速科学研究的进展。TINC将允许科学家交互式地处理高性能计算（HPC）集群生成的非常复杂的信息。它将提供软件工具，通过交互式可视化促进虚拟实验来帮助科学家，加快发现时间。通过将科学家的计算工作流程、脚本语言的科学数据分析和可视化结合在一起，TINC将使研究人员不仅可以分享他们的结果，还可以分享他们的互动工作流程，作为他们出版物的一部分，从而实现共享和传播结果的新方式。这项研究将开始其重点放在材料科学界的一个重要和基本的需求，加快时间，发现新材料，通过快速原型使用计算。通过应用TINC基础设施产生的基础科学是研究钠离子电池电极材料的电化学特性，这将有助于克服阻碍这种有前途的技术商业化的材料挑战。这一重要的概念证明将有助于深入研究科学，同时也有助于将该工具推广到其他学科。TINC的最终目标是为高性能计算创建一个新的范例，通过将交互式可视化与计算结合在一起来促进易用性。这种模式的转变不仅可以促进与更广泛的科学界的联系，而且可以通过TINC对再现性和来源追踪的关注，促进与知情的公众的联系。TINC是一个计算工具包，通过改进复杂数据分析中的交互工作流程来加快数据发现。这种改进是通过将交互性、计算和可视化与复杂的科学数据紧密结合来实现的。通过管理数据参数和实时计算之间的联系，TINC同时解决了大参数空间数据探索中的再现性和交互控制问题。通过脚本语言和数据笔记本的集成，科学家可以轻松地进行交互式计算和显示。通过一个强大的缓存机制，它将使新的方式共享和传播的结果，使研究人员不仅分享他们的结果，而且他们的互动工作流程作为他们的出版物的一部分。TINC将在研究循环中紧密集成交互性，计算和可视化，使科学家能够更快，更深入地理解，比较和验证他们的数据。因此，TINC将促进复杂科学计算模型与高性能交互式可视化的融合，并将实现对经验和理论模型以及大型实验数据集的实时探索。作为一组python和C++库提供，TINC将处理参数空间到数据的映射，交互式触发此参数空间的计算和缓存，以实现可扩展性，性能，完全再现性和数据来源跟踪。TINC将应用于钠离子电池候选电极材料层状插层化合物中离子传输机制和离子插入过程的统计力学研究。这对于材料模拟领域至关重要，该领域专注于研究合金系统中的传输机制，其中难以通过实验可视化特定机制。TINC将允许计算研究人员以一种以前不可能的方式提出和验证传输机制。该奖项由NSF高级网络基础设施办公室颁发，并得到NSF数学和物理科学理事会材料研究部的联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

This project will accelerate the progress of scientific research by creating a software infrastructure named TINC (The Toolkit for Interactive Computation). TINC will allow scientists to interactively work with very complex information generated by high performance computing (HPC) clusters. It will provide software tools to assist scientists by facilitating virtual experimentation through interactive visualization, speeding up time to discovery. By tying together the scientists' workflow of computation, scientific data analysis in scripting languages, and visualization, TINC will enable new ways of sharing and disseminating results by allowing researchers to share not only their results, but also their interactive workflow as part of their publications. This research will begin its focus on an important and essential need in the materials science community, speeding up time to discovery of new materials through rapid prototyping using computation. The basic science to be generated through the application of the TINC infrastructure is the study of the electrochemical properties of electrode materials for Sodium ion batteries that will help overcome materials challenges that are preventing the commercialization of this promising technology for large-scale grid storage applications. This important proof of concept will facilitate delving deep into the science while focusing on the generalization of the tool to other disciplines as well. The ultimate goal of TINC is to create a new paradigm for high performance computing, facilitating ease of use by tying together interactive visualization with computation. This paradigm shift may facilitate a connection not only to a wider scientific community but also to an informed general public as well through TINC's focus on reproducibility and provenance tracing. TINC is a computational toolkit that expedites data discovery by improving the interaction workflow in complex data analysis. This improvement is achieved by tightly integrating interactivity, computation and visualization with complex scientific data. By managing the connection between data parameters and on the fly computation, TINC simultaneously tackles the issues of reproducibility and interactive control in the exploration of data with large parameter spaces. With the integration of scripting languages and data notebooks, scientists can study their data with the ease of interactive computation and display. Through a robust caching mechanism it will enable new ways of sharing and disseminating results by allowing researchers to share not only their results, but also their interactive workflow as part of their publications. TINC will tightly integrate interactivity, computation and visualization in the research loop, allowing scientists to more quickly and more deeply understand, compare and validate their data. Thus, TINC will facilitate the merging of complex scientific computational models with high performance interactive visualization and will enable real-time exploration of empirical and theoretical models and large experimental datasets. Provided as a set of python and C++ libraries, TINC will handle parameter space mapping to data, interactive triggering of computation on this parameter space and caching to enable scalability, performance, full reproducibility and data provenance tracking. TINC will be applied to a statistical mechanics study of ionic transport mechanisms and ionic insertion processes in layered intercalation compounds that are candidate electrode materials for Sodium ion batteries. This is of critical importance to the area of materials simulation that focuses on the study of transport mechanisms in alloy systems, where visualizing specific mechanisms experimentally is difficult. TINC will allow computational researchers to propose and verify transport mechanisms in a way not previously possible.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.