Abstract
An end-to-end platform for chemical science research has been developed that integrates data from computational and experimental approaches through a modern web-based interface. The platform offers an interactive visualization and analytics environment that functions well on mobile, laptop and desktop devices. It offers pragmatic solutions to ensure that large and complex data sets are more accessible. Existing desktop applications/frameworks were extended to integrate with high-performance computing resources, and offer command-line tools to automate interaction—connecting distributed teams to this software platform on their own terms. The platform was developed openly, and all source code hosted on the GitHub platform with automated deployment possible using Ansible coupled with standard Ubuntu-based machine images deployed to cloud machines. The platform is designed to enable teams to reap the benefits of the connected web—going beyond what conventional search and analytics platforms offer in this area. It also has the goal of offering federated instances, that can be customized to the sites/research performed. Data gets stored using JSON, extending upon previous approaches using XML, building structures that support computational chemistry calculations. These structures were developed to make it easy to process data across different languages, and send data to a JavaScript-based web client.
Highlights
The in-silico determination of chemical and materials properties is a vital capability that drives innovation across many market sectors
The software components were developed to serve the needs of data-centric chemistry research using open source approaches that embrace the use of open application programming interface (API), open data formats, and open components
Years of experience in using, converting, and developing data formats was drawn upon to create some new formats for the output of structured data from the NWChem code, ingestion into databases, and the subsequent visualization/analysis of data
Summary
The in-silico determination of chemical and materials properties is a vital capability that drives innovation across many market sectors. It is all too common for experimental and computational studies to take place independently, with large scale studies often involving heroic efforts developing one-off software projects dedicated to the specific resource, such as the Protein Data Bank [6] (over 100,000 experimental structures), Materials Project [7] (over 33,000 simulated materials), and Driven by the U.S Materials Genome Initiative the development of new and novel materials has become a multidisciplinary research endeavor where complex simulation and experimental data get integrated, and analytics such as machine learning techniques are utilized to aid in scientific discovery. There is a strong need to develop a collaborative scientific research software platform that enables researchers to define concepts and hypotheses, add them, and analyze integrated sets of experimental and computational data to offer effective knowledge discovery more universally. This must go well beyond a web portal to create an interactive platform integrating simulation, experimental data, and analytics, while leveraging semantic web technologies to support the federated storage of data across
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