APPLICATION PROGRESS OF MATERIALS GENOME TECHNOLOGY IN THE FIELD OF NEW ENERGY MATERIALS

Authors

  • Kevin Ortner (Corresponding Author) University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada.

Keywords:

Materials genome, New energy materials, High-throughput computing, High-throughput experiment

Abstract

Materials genome integrates high-throughput computing, high-throughput preparation, high-throughput detection and database systems of materials. It is a "paradigm revolution" in materials research and development. With its profound scientific connotation and significant application potential, it will accelerate New materials discovery and applications. This article focuses on the use of materials genome in the research and development of new energy materials to shorten the "discovery-development-production-application" cycle of new energy materials. It introduces the internationally representative Materials Project and OQMD two material genome platforms, as well as some important the application of materials genome computing technologies, such as material conformation characterization, high-throughput computing and screening, machine learning, neural network technology, optimization algorithms and new high-throughput preparation and characterization technologies, in the research and development of new energy materials, and the next step the development of materials genome puts forward prospects, such as developing high-precision high-throughput computing, using artificial intelligence to develop high-throughput experimental systems and platforms, generating material big data, and making full use of material big data through intelligent computing to create computing and experiments. The integrated materials genome big data artificial intelligence system accelerates the discovery and application of new energy materials.

References

[1] National Science and Technology Council. Materials genome initiative strategic plan. USA, 2014.

[2] RACCUGLIA P, ELBERT K C, ADLER P D F. Machine-learning-assisted materials discovery using failed experiments. Nature, 2016, 533 (7601): 73-76.

[3] Wang Hong, Xiang Yong, Xiang Xiaodong. Materials genome -a new model for materials research and development. Science and Technology Herald, 2015, 33(10): 13-19.

[4] DREYSSE H, CEDER G, FONTAINE D D. Determination of effective pair interactions and segregation behavior at alloy surfaces. Vacuum,1990, 41(1): 446-448.

[5] CEDER G, HUANG P, MENON S. Ab initio calculation of the Cu-Pd one-dimensional long period superstructure phase diagram. Acta Metallurgica Et Materialia,1990, 38(11): 2299-2308.

[6] FONTAINE DD, CEDER G, ASTA M. Thermodynamics of oxygen ordering in YBa2Cu3Oz. Journal of the Less-Common Metals, 1990, 164(11): 108-123.

[7] CEDER G, ASTA M, CARTER W C. Phase diagram and low-temperature behavior of oxygen ordering in YBa2Cu3Oz using ab initio interactions. Physical Review B Condensed Matter, 1990, 41(13). https://doi.org/10.1103/PhysRevB.41.8698 .

[8] CEDER G. A computational study of oxygen ordering in YBa2Cu3Oz and its relation to superconductivity. Molecular Simulation, 1994,12(2): 141-153.

[9] FONTAINE D D, CEDER G, ASTA M. Low-temperature long-range oxygen order in YBa2Cu3Oz. Nature, 1990, 343(6258): 544-546.

[10] VEN A V D, MARIANETTI C, MORGAN D. Phase transformations and volume changes in spinel LixMn2O4. Solid State Ionics, 2000, 135(1): 21-32.

[11] VEN A V D, CEDER G. Lithium diffusion mechanisms in layered intercalation compounds. Journal of Power Sources, 2001(97/98): 529-531.

[12] DOMPABLO M E, MARIANETTI C, VEN A V D. Jahn-Teller mediated ordering in layered LixMO2 compounds. Physical Review B Condensed Matter, 2001, 63(63). https://doi.org/10.1103/ PhysRevB.63.144107.

[13] VAN D V A, CEDER G, ASTAM. First-principles theory of ionic diffusion with nondilute carriers. Physical Review B, 2001, 64(18):607-611.

[14] DOMPABLO M E, VEN A V D, CEDER G. First-principles calculations of lithium ordering and phase stability on LixNiO2.Physical Review B, 2002, 66(6): 340-351.

[15] CURTAROLO S, MORGAN D, PERSSON K. Predicting crystal structures with data mining of quantum calculations. Physical Review Letters, 2003, 91(13): doi: 10.1103/PhysRevLett.91.135503.

[16] MORGAN D. Data mining approach to ab-initio prediction of crystal structure. MRS Proceedings, 2003, 804: 1-6.

[17] MORGAN D, CEDER G, CURTAROLO S. High-throughput and data mining with abinitio methods. Measurement Science & Technology, 2004, 16(16): 296-301.

[18] WU Y, LAZIC P, HAUTIER G. First principles high throughput screening of oxynitrides for water-splitting photocatalysts. Energy & Environmental Science, 2012, 6(1): 157-168.

[19] MUELLER T, HAUTIER G, JAIN A. Evaluation of tavorite-structured cathode materials for lithium-ion batteries using high-throughput computing. Chemistry of Materials, 2011, 23(17): 3854-3862.

[20] CEDER G, MORGAN D, FISCHER C. Data-mining-driven quantum mechanics for the prediction of structure. MRS Bulletin, 2006, 31(12): 981-985.

[21] JAIN A, HAUTIER G, MOORE C J. A high-throughput infrastructure for density functional theory calculations. Computational Materials Science, 2011, 50(8): 2295-2310.

[22] HAUTIER G, JAIN A, ONG S P. Phosphates as lithium-ion battery cathodes: An evaluation based on high-throughput ab initio calculations. Chemistry of Materials, 2011, 23(15): 3495-3508.

[23] HAUTIER G, FISCHER C C, JAIN A. Finding nature's missing ternary oxide compounds using machine learning and density functional theory. Chemistry of Materials, 2010, 22(12): 3762-3767.

[24] HAUTIER G, FISCHER C, EHRLACHER V. Data mined ionic substitutions for the discovery of new compounds. Inorganic Chemistry, 2011, 50(2): doi: 10.1021/ic102031h.

[25] YANG L, CEDER G. Data-mined similarity function between material compositions. Physical Review B, 2013, 88(22): 330-339.

[26] MEREDIG B, WOLVERTON C. Dissolving the periodic table in cubic zirconia: Data mining to discover chemical trends. Chemistry of Materials, 2014, 26(6): 1985-1991.

[27] MEREDIG B, AGRAWAL A, KIRKLIN S. Combinatorial screening for new materials in unconstrained composition space with machine learning. Physical Review B, 2014, 89(9): 82-84.

Downloads

Published

2024-01-01

How to Cite

Ortner, K. (2024). Application Progress Of Materials Genome Technology In The Field Of New Energy Materials. Eurasia Journal of Science and Technology, 2(1), 11-20. https://doi.org/10.61784/wjms240168