High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys

GJ Wang and KQ Li and LY Peng and YM Zhang and J Zhou and ZM Sun, ACTA METALLURGICA SINICA, 58, 75-88 (2022).

DOI: 10.11900/0412.1961.2021.00041

The development of novel materials has experienced three paradigms: purely empirical, theoretical models, and computational materials science. Currently, the huge amount of data generated by experiments and simulations has facilitated a shift in materials science to a data- driven fourth paradigm. Therefore, the development of high-throughput automatic integrated computations and data mining algorithms based on material databases and artificial intelligence algorithms is critical for accelerating the design of novel materials. This paper presents an open-source distributed computational platform called Artificial Learning and Knowledge Enhanced Materials Informatics Engineering 2.0 (ALKEMIE2.0) based on the AMDIV (automation-modular-database- intelligence-visualization) design concepts. The ALKEMIE2.0 platform includes five core components of automation, modular, materials database, artificial intelligence, and visualization, which are suitable for the computational design of novel materials. The overall characteristics of ALKEMIE2.0 are divided into five pillars. ALKEMIE- Core integrates multiscale calculations and simulation software using the ALKEMIE-Plugin application programming interface. Its high- throughput calculation workflows that support 104 magnitude concurrencies are implemented by integrating the automatic frameworks of model constructions, calculation workflows, and data analyses. Furthermore, the platform is based on the ALKEMIE-Server, which can easily and automatically open daemon services and realize information interactions in distributed supercomputers. With its strong portability and scalability, ALKEMIE has been deployed in the National Supercomputing Tianjin Center. In addition, the multitype materials database called the ALKEMIE-Data Vault contains structure, task, workflow, and material property databases, which combined with the power of supercomputing, enables the rapid application of artificial intelligence algorithms in the design of new materials. In particular, the many user-friendly interfaces, which were elaborately designed using the ALKEMIE-GUI and are suitable for scientists with broad backgrounds, make structural building, work flowcharts, data analysis, and machine learning models more transparent and maneuverable. Finally, the main features of ALKEMIE2.0 are demonstrated using two examples of multiplatform deployment and high-throughput screening of binary aluminum alloys.

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