William-Yi Wang1,2*, Xingyu Gao3, Haifeng Songg3*, Zi-Kui Liu4, Jinshan Li1,2*
1Northwestern Polytechnical University, Xi'an 710072, China
2Innovation Centre, NPU Chongqing, Chongqing, 40113 5, China.;
3Institute of Applied Physics and Computational Mathematics, Beijing 100088, China;
4The Pennsylvania State University, University Park, PA 16802, USA
EXTENDED ABSTRACT: Future has been moved forward. The long-time dreamed Exascale Era came on Oct. 18th, 2022, which was characterized by the released Frontier high performance computers in USA. Moreover, the new milestone of theoretical 2,000,000,000,000,000,000 operations per second will be obtained by the constructed El Capitan HPC in 2023. Based on the CPU+GPU frameworks, the computational efficiency of software has also been improved significantly. While those mathematical models and algorithms play a dominated role on this significant progress, the greater speed, scale, and accuracy will be dramatically evolved, both of which support the profound foundation to accelerating and enhancing the big-data mediated artificial intelligence of Materials Genome Engineering (MGE). First-principles calculation based on the density functional theory has been recognized as one of the dominated high-throughput computations and database technics in MGE, providing the insights into the physical fundamentals of predicted thermodynamic, kinetic, and mechanical properties of investigated phases. Here, based on our constructed HPC consisted of CPU and GPU units, the dramatically accelerated computational efficiencies by the hardware and the software have been systematically validated, which present the benefits in the preparing DPT-based database and enabling the faster discovery of advanced materials accurately. With the guidance of Zentropy theory and Entropy-mediated techniques, the contributions of microstates to the basic physical properties of materials are comprehensively discussed, which reveals the basic building blocks of Material Genomes. In the views of atomics and electronics, it is revealed the microstructural genetics of metal melts, the local phase transformation foundations of structural imperfections including grain boundaries, stacking faults, and anti-phase boundaries. Correspondingly, the models predicting the performance of designed materials are proposed through integrating the micro- and the macro-key property parameters, which also accelerate the developments of innovated algorithms and software/codes, thus, to utilize in the smart design of advanced Ti alloys, Mg alloys, and high-entropy materials.
William Yi Wang received his M.S. and Ph.D. in Materials Science and Engineering from the Penn State University in 2012 and 2013 and continuously worked as a postdoctoral fellow advised by Professor Zi-Kui Liu till May 2016. Afterward he joined the school of Materials Science and Engineering at Northwestern Polytechnical University in 2016 as an Associate Professor and had been promoted to be a tenured Professor in 2022. Professor Wang's research topic is the Data-Driven ICME for Advanced Materials. He has authored and co-authored more than 100 publications in respectable peer-reviewed journals and applied more than 10 patents together with his collaborators. He is the Principle Editor of Journal of Materials Research, the Youth Committee Editor Board member of J Mag. Ally, J Mater Infor. etc.