The Application of Materials Genome Approach in Materials Design

doi:10.11871/jfdc.issn.2096-742X.2020.01.011

Abstract

Abstract:

[Objective] In this paper, we introduce the application of materials genome approach on materials design, including explorations of catalytic materials, thermoelectric materials, metal organic framework (MOF) materials, lithium battery materials and perovskite photovoltaic materials. [Methods] High-throughput computing is combined with data mining techniques, machine learning for instance. Database is generated from the high-throughput computing, and then data mining and deep analysis are performed. [Results] Potential novel materials are screened and discovered based on the data analysis. [Limitations] Currently, some hypothetical materials are hardly realized in experiments. Thus, the theoretical predictions and experiments need to be integrated more deeply. [Conclusion] With the further development of computational and experimental technology, materials genetic approach will perform a more significant role in materials development.

Key words: high-throughput calculations, materials genome approach, catalysis, thermoelectric materials, metal-organic frameworks, lithium battery, perovskites, machine learning

Qian Xu,Tian Ziqi. The Application of Materials Genome Approach in Materials Design[J]. Frontiers of Data and Computing, 2020, 2(1): 128-141.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Fig. 4

Fig. 5

Fig. 6

Fig.7

References 61

[1]	S. Curtarolo, G. L. Hart, M. B. Nardelli, N. Mingo, S. Sanvito and O. Levy . The high-throughput highway to computational materials design[J]. Nature Materials. 2013, 12, pp. 191-201.
[2]	S. Curtarolo, D. Morgan, K. Persson, J. Rodgers and G. Ceder. Predicting Crystal Structures with Data Mining of Quantum Calculations[J]. Physical Review Letters. 2003, 91, pp. 135503.
[3]	W. Setyawan and S. Curtarolo. High-throughput electronic band structure calculations: Challenges and tools[J]. Computational Materials Science. 2010, 49, pp. 299-312.
[4]	A. Jain, G. Hautier, C. J. Moore, S. Ping Ong, C. C. Fischer, T. Mueller, K. A. Persson and G. Ceder . A high-throughput infrastructure for density functional theory calculations[J]. Computational Materials Science. 2011, 50, pp. 2295-2310.
[5]	S. Wang, W. Zhao, W. Setyawan, N. Mingo and S. Curtarolo. Assessing the Thermoelectric Properties of Sintered Compounds via High-Throughput Ab-Initio Calculations[J]. Physical Review X. 2011, 1, pp. 1160-1164.
[6]	J. K. Norskov, T. Bligaard, J. Rossmeisl and C. H. Christensen. Towards the computational design of solid catalysts[J]. Nature Chemistry. 2009, 1, pp. 37-46.
[7]	A. Vojvodic and J. K. Norskov. New design paradigm for heterogeneous catalysts[J]. National Science Review. 2015, 2, pp. 140-143.
[8]	Z. Li, S. Wang, W. S. Chin, L. E. Achenie and H. Xin . High-throughput screening of bimetallic catalysts enabled by machine learning[J]. Journal of Materials Chemistry A. 2017, 5, pp. 24131-24138.
[9]	R. Jinnouchi, H. Hirata and R. Asahi . Extrapolating Energetics on Clusters and Single Crystal Surfaces to Nanoparticles by Machine-Learning Scheme[J]. Journal of Physical Chemistry C. 2017, pp. 26397-26405.
[10]	R. Jinnouchi and R. Asahi . Predicting Catalytic Activity of Nanoparticle by DFT-aided Machine-Learning Algorithm[J]. Journal of Physical Chemistry Letters. 2017, pp. 4279-4283.
[11]	T. Toyao, K. Suzuki, S. Kikuchi, S. Takakusagi, K.-I. Shimizu and I. Takigawa . Towards Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys[J]. Journal of Physical Chemistry C. 2018, pp. 8315-8326.
[12]	Z. W. Ulissi, M. T. Tang, J. Xiao, X. Liu, D. A. Torelli, M. Karamad, K. Cummins, C. Hahn, N.S. Lewis and T. F. Jaramillo . Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction[J]. Acs Catalysis. 2017, pp. 6600-6608.
[13]	Kevin, Tran, Zachary, W. and Ulissi. Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution[J]. Nature Catalysis. 2018, pp. 696-703.
[14]	J. H. Montoya and K. A. Persson . A high-throughput framework for determining adsorption energies on solid surfaces[J]. Npj Computational Materials. 2017, 3, pp. 14.
[15]	Jacob, Boes, Osman, Mamun, Kirsten Winther, Thomas and Bligaard. Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation[J]. Journal of Physical Chemistry. A. 2019, pp. 2281-2285.
[16]	O. Mamun, K. T. Winther, J.R. Boes and T. Bligaard. High-throughput calculations of catalytic properties of bimetallic alloy surfaces[J]. Scientific Data. 2019, 6, pp. 76.
[17]	K. T. Winther, M. J. Hoffmann, J. R. Boes, O. Mamun, M. Bajdich and T. Bligaard. Catalysis-Hub.org, an open electronic structure database for surface reactions[J]. Scientific Data. 2019, 6, pp. 75.
[18]	A. J. Medford, C. Shi, M. J. Hoffmann, A. C. Lausche, S. R. Fitzgibbon, T. Bligaard and J. K. Norskov. CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends[J]. Catalysis Letters. 2015, 145, pp. 794-807.
[19]	J. A. Garrido Torres, P. C. Jennings, M. H. Hansen, J. R. Boes and T. Bligaard. Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model[J]. Physical Review Letters. 2019, 122, pp. 156001.
[20]	H. C. Zhou, J. R. Long and O. M. Yaghi . Introduction to metal-organic frameworks[J]. Chemical Reviews. 2012, 112, pp. 673-4.
[21]	Y. J. Colon and R. Q. Snurr . High-throughput computational screening of metal-organic frameworks[J]. Chemical Society Reviews. 2014, 43, pp. 5735-49.
[22]	F. H. Allen . The Cambridge Structural Database: a quarter of a million crystal structures and rising[J]. Acta Crystallographica Section B-Structural Science. 2002, 58, pp. 380-388.
[23]	T. Watanabe and D. S. Sholl. Accelerating Applications of Metal-Organic Frameworks for Gas Adsorption and Separation by Computational Screening of Materials[J]. Langmuir. 2012, 28, pp. 14114-14128.
[24]	J. Goldsmith, A. G. Wong-Foy, M. J.. Cafarella and D. J. Siegel . Theoretical Limits of Hydrogen Storage in Metal-Organic Frameworks: Opportunities and Trade-Offs[J]. Chemistry of Materials. 2013, 25, pp. 3373-3382.
[25]	G. Avci, S. Velioglu and S. Keskin. High-Throughput Screening of MOF Adsorbents and Membranes for H2 Purification and CO2 Capture[J]. ACS Appl Mater Interfaces. 2018, 10, pp. 33693-33706.
[26]	Altintas, Cigdem, Erucar, Ilknur, Keskin and Seda. High-Throughput Computational Screening of the Metal Organic Framework Database for CH4/H-2 Separations[J]. ACS Applied Materials & Interfaces. 2018, 10, pp. 3668-3679.
[27]	A. N. V. Azar, S. Velioglu and S. Keskin . Large-Scale Computational Screening of Metal Organic Framework (MOF) Membranes and MOF-Based Polymer Membranes for H2/N2 Separations[J]. ACS Sustainable Chemistry & Engineering. 2019, 7, pp. 9525-9536.
[28]	H. Daglar and S. Keskin. High-Throughput Screening of Metal Organic Frameworks as Fillers in Mixed Matrix Membranes for Flue Gas Separation[J]. Advanced Theory and Simulations. 2019, 2, pp. 1900109.
[29]	Z. Qiao, Q. Xu and J. Jiang. High-throughput computational screening of metal-organic framework membranes for upgrading of natural gas[J]. Journal of Membrane Science. 2018, 551, pp. 47-54.
[30]	C. E. Wilmer, M. Leaf, C. Y. Lee, O. K. Farha, B. G. Hauser, J. T. Hupp and R. Q. Snurr . Large-scale screening of hypothetical metal-organic frameworks[J]. Nature Chemistry. 2011, 4, pp. 83-89.
[31]	D. A. Gómez-Gualdrón, C. E. Wilmer, O. K. Farha, J. T. Hupp and R. Q. Snurr . Exploring the Limits of Methane Storage and Delivery in Nanoporous Materials[J]. Journal of Physical Chemistry C. 2014, 118, pp. 6941-6951.
[32]	P. D. H. J. Goldsmid B, . Sc, F.Inst.P. Thermoelectric Refrigeration[J]. Electrical Engineering. 2013, 79, pp. 380-384.
[33]	G. K. H. Madsen and D. J. BoltzTraP . A code for calculating band-structure dependent quantities[J]. Computer Physics Communications. 2006, 175, pp. 67-71.
[34]	L. Xi, S. Pan, X. Li, Y. Xu and W. Zhang. Discovery of High Performance Thermoelectric Chalcogenides through Reliable High Throughput Material Screening[J]. Journal of the American Chemical Society. 2018, 140, pp. 10785-10793.
[35]	R.-z. Zhang, K. Chen, B. Du and M. J. Reece. Screening for Cu-S based thermoelectric materials using crystal structure features[J]. Journal of Materials Chemistry A. 2017, 5, pp. 5013-5019.
[36]	Robert W. McKinney, P. Gorai, V. Stevanović and E. S. Toberer . Search for new thermoelectric materials with low Lorenz number[J]. Journal of Materials Chemistry A. 2017, 5, pp. 17302-17311.
[37]	S. Guo, T. Jia and Y. Zhang. Electrical Property Dominated Promising Half-Heusler Thermoelectrics through High-Throughput Material Computations[J]. The Journal of Physical Chemistry C. 2019, 123, pp. 18824-18833.
[38]	C. Wei, J. H. Pohls, G. Hautier, D. Broberg, S. Bajaj, U. Aydemir, Z. M. Gibbs, Z. Hong, M. Asta and G. J. Snyder. Understanding Thermoelectric Properties from High-Throughput Calculations: Trends, Insights, and Comparisons with Experiment[J]. Journal of Materials Chemistry C. 2016, 4, pp. 4414-4426.
[39]	J. Carrete, L. Wu, N. Mingo, S. Wang and S. Curtarolo. Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling[J]. Physical Review X. 2014, 4, pp. 186-187.
[40]	A. Seko, A. Togo, H. Hayashi, K. Tsuda, L. Chaput and I. Tanaka. Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization[J]. Physical Review Letters. 2015, 115, pp. 205901.
[41]	P. Gorai, V. Stevanovic and E. S. Toberer. Computationally guided discovery of thermoelectric materials[J]. Nature Reviews Materials. 2017, 2, pp. 17053.
[42]	J. B. Goodenough and Y. Kim. Challenges for Rechargeable Li Batteries[J]. Chemistry of Materials. 2010, 22, pp. 587-603.
[43]	G. Hautier, A. Jain, H. Chen, C. Moore, S.P. Ong and G. Ceder. Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations[J]. Journal of Materials Chemistry. 2011, 21, pp. 17147.
[44]	A. Jain, G. Hautier, S. P. Ong, S. Dacek and G. Ceder. Relating voltage and thermal safety in Li-ion battery cathodes: a high-throughput computational study[J]. Physical Chemistry Chemical Physics. 2015, 17, pp. 5942-5953.
[45]	W. Zhang, D. M. Cupid, P. Gotcu, K. Chang, D. Li, Y. Du and H. J. Seifert. High-Throughput Description of Infinite Composition-Structure-Property-Performance Relationships of Lithium-Manganese Oxide Spinel Cathodes[J]. Chemistry of Materials. 2018, 30, pp. 2287-2298.
[46]	K. Chang, B. Hallstedt and D. Music. Thermodynamic description of the LiNiO 2 -NiO 2 pseudo-binary system and extrapolation to the Li(Co,Ni)O 2 -( Co,Ni)O 2 system[J]. CALPHAD: Computer Coupling of Phase Diagrams and Thermochemistry. 2012, 37, pp. 0-107.
[47]	S. Kirklin, B. Meredig and C. Wolverton. High-Throughput Computational Screening of New Li-Ion Battery Anode Materials[J]. Advanced Energy Materials. 2013, 3, pp. 252-262.
[48]	K. Edström, T. Gustafsson and J. O. Thomas. The cathode-electrolyte interface in the Li-ion battery[J]. Electrochimica Acta. 2004, 50, pp. 397-403.
[49]	M. Aykol, S. Kim, V. I. Hegde, D. Snydacker, Z. Lu, S. Hao, S. Kirklin, D. Morgan and C. Wolverton. High-throughput computational design of cathode coatings for Li-ion batteries[J]. Nature Communications. 2016, 7, pp. 13779.
[50]	M. Saliba, T. Matsui, J. Y. Seo, K. Domanski, J. P. Correa-Baena, M. K. Nazeeruddin, S. M. Zakeeruddin, W. Tress, A. Abate, A. Hagfeldt and M. Gratzel. Cesium-containing triple cation perovskite solar cells: improved stability, reproducibility and high efficiency[J]. Energy & Environmental Science. 2016, 9, pp. 1989-1997.
[51]	M. Lyu, J. H. Yun, C. Peng, M. Hao and L. Wang. Addressing Toxicity of Lead: Progress and Applications of Low‐Toxic Metal Halide Perovskites and Their Derivatives[J]. Advanced Energy Materials. 2017, 7, pp. 1602512.
[52]	S. Chakraborty, W. Xie, N. Mathews, M. Sherburne, R. Ahuja, M. Asta and S. G. Mhaisalkar. Rational Design: A High-Throughput Computational Screening and Experimental Validation Methodology for Lead-Free and Emergent Hybrid Perovskites[J]. ACS Energy Letters. 2017, 2, pp. 837-845.
[53]	T. Nakajima and K. Sawada. Discovery of Pb-Free Perovskite Solar Cells via High-Throughput Simulation on the K Computer[J]. Journal of Physical Chemistry Letters. 2017, 8, pp. 4826-4831.
[54]	Y. Li and K. Yang. High-throughput computational design of organic-inorganic hybrid halide semiconductors beyond perovskites for optoelectronics[J]. Energy & Environmental Science. 2019, 12, pp. 2233-2243.
[55]	P. V. Balachandran, A. A. Emery, J. E. Gubernatis, T. Lookman, C. Wolverton and A. Zunger. Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory[J]. Physical Review Materials. 2018, 2, pp. 043802.
[56]	W. Li, R. Jacobs and D. Morgan. Predicting the thermodynamic stability of perovskite oxides using machine learning models[J]. Computational Materials Science. 2018, 150, pp. 454-463.
[57]	A. Jain, S. P. Ong, G. Hautier, C. Wei and K. A. Persson. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation[J]. Apl Materials. 2013, 1, pp 011002.
[58]	T. Xie and J. C. Grossman. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties[J]. Phys Rev Lett. 2018, 120, pp. 145301.
[59]	Z. Z. Li, Q. C. Xu, Q. D. Sun, Z.F. Hou and W. J. Yin. Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning[J]. Advanced Functional Materials. 2019, 29, pp. 1807280.
[60]	S. Lu, Q. Zhou, Y. Ouyang, Y. Guo, Q. Li and J. Wang. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning[J]. Nature Communications. 2018, 9, pp. 3405.
[61]	S. H. Lu, Q. H. Zhou, L. Ma, Y. Guo and J. L. Wang. Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning[J]. Small Methods. 2019, 3, pp. 1900360.