High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys
WANG Guanjie1,2, LI Kaiqi1,2, PENG Liyu1,2, ZHANG Yiming1,2, ZHOU Jian1,2, SUN Zhimei1,2()
1. School of Materials Science and Engineering, Beihang University, Beijing 100191, China 2. Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
Cite this article:
WANG Guanjie, LI Kaiqi, PENG Liyu, ZHANG Yiming, ZHOU Jian, SUN Zhimei. High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys. Acta Metall Sin, 2022, 58(1): 75-88.
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.
Fund: National Key Research and Development Program of China(2017YFB0701700);National Natural Science Foundation of China(51872017);the High-Performance Computing (HPC) Resources at Beihang University
About author: SUN Zhimei, professor, Tel: (010)82317747, E-mail: zmsun@buaa.edu.cn
Fig.1 The AMDIV design philosophy of ALKEMIE2.0 platform (ALKEMIE—Artificial Learning and Knowledge Enhanced Materials Informatics Engineering, DB—database, ML—machine learning)
Fig.2 The architecture of ALKEMIE2.0 (GUI—graphical user interface, SSH—secure shell, AI—artificial intelligence, DOS—density of state, DFT—discrete Fourier transform, I/O—imput and output)
Fig.3 The outline of ALKEMIE2.0 platform (AIMD—abinitio molecular dynamics, HT—high-throughput, TC WF—Curie temperature workflow)
Fig.4 The flowchart of high-throughput automatic calculation and error correction
Fig.5 The types of databases in ALKEMIE2.0-Data Vault
Fig.6 High-throughput screening of the thermodynamic stability of aluminum alloys (a) illustration of the not calculated elements (gray), the energetically unstable (red), and stable elements (white) in Al, respectively (b) the formation energy of the 81 alloying element (c, d) the average bond length (c) and lattice constant (d) of the alloyed compounds (The dashed lines indicate the value of the pure Al)
Fig.7 High-throughput screening of mechanical properties and electrical conductivities of aluminum alloys (The dotted line represents the reference value corresponding to pure aluminum calculated by DFT) (a) bulk modulus (b) shear modulus (c) Young's modulus (d) conductivity of different alloying elements
1
Agrawal A , Choudhary A . Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science [J]. APL Mater., 2016, 4: 053208
2
Holdren J P . Materials genome initiative for global competitiveness [R]. Washington, DC: Executive Office of the President of the United States, National Science and Technology Council, 2011
3
Lin L C , Berger A H , Martin R L , et al . In silico screening of carbon-capture materials [J]. Nat. Mater., 2012, 11: 633
4
Su Y J , Fu H D , Bai Y , et al . Progress in materials genome engineering in China [J]. Acta Metall. Sin., 2020, 56: 1313
Yang K S , Setyawan W , Wang S D , et al . A search model for topological insulators with high-throughput robustness descriptors [J]. Nat. Mater., 2012, 11: 614
6
Jain A , Ong S P , Hautier G , et al . Commentary: The Materials Project: A materials genome approach to accelerating materials innovation [J]. APL Mater., 2013, 1: 011002
7
Ong S P , Richards W D , Jain A , et al . Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis [J]. Comput. Mater. Sci., 2013, 68: 314
8
Jain A , Ong S P , Chen W , et al . FireWorks: A dynamic workflow system designed for high-throughput applications [J]. Concurr. Comput., 2015, 27: 5037
9
Mathew K , Montoya J H , Faghaninia A , et al . Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows [J]. Comput. Mater. Sci., 2017, 139: 140
10
Supka A R , Lyons T E , Liyanage L , et al . AFLOWπ: A minimalist approach to high-throughput ab initio calculations including the generation of tight-binding hamiltonians [J]. Comput. Mater. Sci., 2017, 136: 76
11
Pizzi G , Cepellotti A , Sabatini R , et al . AiiDA: Automated interactive infrastructure and database for computational science [J]. Comput. Mater. Sci., 2016, 111: 218
12
Larsen A H , Mortensen J J , Blomqvist J , et al . The atomic simulation environment—A Python library for working with atoms [J]. J. Phys.: Condens. Matter, 2017, 29: 273002
13
Belsky A , Hellenbrandt M , Karen V L , et al . New developments in the Inorganic Crystal Structure Database (ICSD): Accessibility in support of materials research and design [J]. Acta Cryst., 2002, 58B: 364
14
Quirós M , Gražulis S , Girdzijauskaitė S , et al . Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database [J]. J. Cheminform., 2018, 10: 23
15
Curtarolo S , Setyawan W , Wang S D , et al . AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations [J]. Comput. Mater. Sci., 2012, 58: 227
16
Kirklin S , Saal J E , Meredig B , et al . The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies [J]. npj Comput. Mater., 2015, 1: 15010
17
Draxl C , Scheffler M . The NOMAD laboratory: From data sharing to artificial intelligence [J]. J. Phys. Mater., 2019, 2: 036001
18
Pedregosa F , Varoquaux G , Gramfort A , et al . Scikit-learn: Machine learning in Python [J]. J. Mach. Learn. Res., 2011, 12: 2825
19
Abadi M, Agarwal A, Barham P, et al . TensorFlow: Large-scale machine learning on heterogeneous distributed systems [Z]. arXiv:1603.04467, 2016
20
Paszke A , Gross S , Massa F , et al . PyTorch: An imperative style, high-performance deep learning library [A]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) [C]. Vancouver, Canada: Curran Associates, 2019: 32
21
Ouyang R H , Ahmetcik E , Carbogno C , et al . Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO [J]. J. Phys. Mater., 2019, 2: 024002
22
Gossett E , Toher C , Oses C , et al . AFLOW-ML: A RESTful API for machine-learning predictions of materials properties [J]. Comput. Mater. Sci., 2018, 152: 134
23
Ward L , Dunn A , Faghaninia A , et al . Matminer: An open source toolkit for materials data mining [J]. Comput. Mater. Sci., 2018, 152: 60
24
Wang G J , Peng L Y , Li K Q , et al . ALKEMIE: An intelligent computational platform for accelerating materials discovery and design [J]. Comput. Mater. Sci., 2021, 186: 110064
25
Kingma D P, Ba J. Adam: A method for stochastic optimization [Z]. arXiv:1412.6980, 2014
26
Zhang C Y, Bengio S, Hardt M, et al . Understanding deep learning requires rethinking generalization [Z]. arXiv:1611.03530, 2016
27
Hunter J D . Matplotlib: A 2D graphics environment [J]. Comput. Sci. Eng., 2007, 9: 90
28
Kresse G , Hafner J . Ab initio molecular dynamics for liquid metals [J]. Phys. Rev., 1993, 47B: 558
29
Giannozzi P , Baroni S , Bonini N , et al . Quantum Espresso: A modular and open-source software project for quantum simulations of materials [J]. J. Phys.: Condens. Matter, 2009, 21: 395502
30
Huang Y D , Zhou J , Wang G J , et al . Abnormally strong electron-phonon scattering induced unprecedented reduction in lattice thermal conductivity of two-dimensional Nb2C [J]. J. Am. Chem. Soc., 2019, 141: 8503
31
Gonze X , Amadon B , Anglade P M , et al . ABINIT: First-principles approach to material and nanosystem properties [J]. Comput. Phys. Commun., 2009, 180: 2582
32
Plimpton S . Fast parallel algorithms for short-range molecular dynamics [J]. J. Comput. Phys., 1995, 117: 1
33
Tegeler M , Shchyglo O , Kamachali R D , et al . Parallel multiphase field simulations with OpenPhase [J]. Comput. Phys. Commun., 2017, 215: 173
34
Lukas H L , Fries S G , Sundman B . Computational Thermodynamics: The Calphad Method [M]. Cambridge: Cambridge University Press, 2007: 265
35
Otero-de-la-Roza A , Abbasi-Pérez D , Luaña V . Gibbs2: A new version of the quasiharmonic model code. II. Models for solid-state thermodynamics, features and implementation [J]. Comput. Phys. Commun., 2011, 182: 2232
36
Morgan B J . Lattice-geometry effects in garnet solid electrolytes: A lattice-gas Monte Carlo simulation study [J]. Roy. Soc. Open Sci., 2017, 4: 170824
37
Sun Z M , Zhou J , Blomqvist A , et al . Formation of large voids in the amorphous phase-change memory Ge2Sb2Te5 alloy [J]. Phys. Rev. Lett., 2009, 102: 075504
38
Cheng Y X , Zhu L G , Wang G J , et al . Vacancy formation energy and its connection with bonding environment in solid: A high-throughput calculation and machine learning study [J]. Comput. Mater. Sci., 2020, 183: 109803
39
Peng Q , Zhou J , Chen J T , et al . Cu single atoms on Ti2CO2 as a highly efficient oxygen reduction catalyst in a proton exchange membrane fuel cell [J]. J. Mater. Chem., 2019, 7A: 26062
40
Peng L Y , Li Z , Wang G J , et al . Reduction in thermal conductivity of Sb2Te phase-change material by scandium/yttrium doping [J]. J. Alloys Compd., 2020, 821: 153499
41
Kalinin S V , Sumpter B G , Archibald R K . Big-deep-smart data in imaging for guiding materials design [J]. Nat. Mater., 2015, 14: 973
42
Pan F S , Zhang D F , et al . Alumimum Alloy and Application [M]. Beijing: Chemical Industry Press, 2006: 155
潘复生, 张丁非 等 . 铝合金及应用 [M]. 北京: 化学工业出版社, 2006: 155
43
Guan R G , Luo H F , Huang H , et al . Development of aluminum alloy materials: Current status, trend, and prospects [J]. Strateg. Stud. CAE, 2020, 22(5): 68
Madsen G K H , Singh D J . BoltzTraP. A code for calculating band-structure dependent quantities [J]. Comput. Phys. Commun., 2006, 175: 67
45
Wang G J , Zhou J , Elliott S R , et al . Role of carbon-rings in polycrystalline GeSb2Te4 phase-change material [J]. J. Alloys Compd., 2019, 782: 852
46
Wang G J , Zhou J , Sun Z M . First principles investigation on anomalous lattice shrinkage of W alloyed rock salt GeTe [J]. J. Phys. Chem. Solids, 2020, 137: 109220
47
Mouhat F , Coudert F X . Necessary and sufficient elastic stability conditions in various crystal systems [J]. Phys. Rev., 2014, 90B: 224104
48
Hill R . The elastic behaviour of a crystalline aggregate [J]. Proc. Phys. Soc., 1952, 65A: 349
49
Voigt W . Lehrbuch der Kristallphysik [M]. Leipzig: B. G. Teubner, 1928: 1
50
Guo X Q , Podloucky R , Freeman A J . First principles calculation of the elastic constants of intermetallic compounds: Metastable Al3Li [J]. J. Mater. Res., 1991, 6: 324
51
Chinmulgund M , Inturi R B , Barnard J A . Effect of Ar gas pressure on growth, structure, and mechanical properties of sputtered Ti, Al, TiAl, and Ti3Al films [J]. Thin Solid Films, 1995, 270: 260
52
Zhang D T , Li Y Y , Luo Z Q . A review on the progress of rapidly solidified hypereutectic Al-Si alloy materials [J]. Light Alloy Fabr. Technol., 2001, 29(2): 1
