Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations
Abstract
Emerging applications of metal–organic frameworks (MOFs) in electronic devices will benefit from the design and synthesis of intrinsically, highly electronically conductive MOFs. However, very few are known to exist. It is a challenging task to search for electronically conductive MOFs within the tens of thousands of reported MOF structures. Using a new strategy (i.e., transfer learning) of combining machine learning techniques, statistical multivoting, and ab initio calculations, we screened 2932 MOFs and identified 6 MOF crystal structures that are metallic at the level of semilocal DFT band theory: Mn2[Re6X8(CN)6]4 (X = S, Se,Te), Mn[Re3Te4(CN)3], Hg[SCN]4Co[NCS]4, and CdC4. Five of these structures have been synthesized and reported in the literature, but their electrical characterization has not been reported. In conclusion, our work demonstrates the potential power of machine learning in materials science to aid in down-selecting from large numbers of potential candidates and provides the information and guidance to accelerate the discovery of novel advanced materials.
- Authors:
-
[1];
[1];
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Google Brain, Mountain View, CA (United States)
- Stanford Univ., Stanford, CA (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1465190
- Report Number(s):
- SAND-2018-4638J
Journal ID: ISSN 1948-7185; 663857
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Physical Chemistry Letters
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 16; Journal ID: ISSN 1948-7185
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
He, Yuping, Cubuk, Ekin D., Allendorf, Mark D., and Reed, Evan J. Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations. United States: N. p., 2018.
Web. doi:10.1021/acs.jpclett.8b01707.
He, Yuping, Cubuk, Ekin D., Allendorf, Mark D., & Reed, Evan J. Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations. United States. https://doi.org/10.1021/acs.jpclett.8b01707
He, Yuping, Cubuk, Ekin D., Allendorf, Mark D., and Reed, Evan J. Fri .
"Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations". United States. https://doi.org/10.1021/acs.jpclett.8b01707. https://www.osti.gov/servlets/purl/1465190.
@article{osti_1465190,
title = {Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations},
author = {He, Yuping and Cubuk, Ekin D. and Allendorf, Mark D. and Reed, Evan J.},
abstractNote = {Emerging applications of metal–organic frameworks (MOFs) in electronic devices will benefit from the design and synthesis of intrinsically, highly electronically conductive MOFs. However, very few are known to exist. It is a challenging task to search for electronically conductive MOFs within the tens of thousands of reported MOF structures. Using a new strategy (i.e., transfer learning) of combining machine learning techniques, statistical multivoting, and ab initio calculations, we screened 2932 MOFs and identified 6 MOF crystal structures that are metallic at the level of semilocal DFT band theory: Mn2[Re6X8(CN)6]4 (X = S, Se,Te), Mn[Re3Te4(CN)3], Hg[SCN]4Co[NCS]4, and CdC4. Five of these structures have been synthesized and reported in the literature, but their electrical characterization has not been reported. In conclusion, our work demonstrates the potential power of machine learning in materials science to aid in down-selecting from large numbers of potential candidates and provides the information and guidance to accelerate the discovery of novel advanced materials.},
doi = {10.1021/acs.jpclett.8b01707},
journal = {Journal of Physical Chemistry Letters},
number = 16,
volume = 9,
place = {United States},
year = {Fri Jul 27 00:00:00 EDT 2018},
month = {Fri Jul 27 00:00:00 EDT 2018}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 64 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Hybrid porous solids past, present, future
journal, January 2008
- Férey, Gérard
- Chem. Soc. Rev., Vol. 37, Issue 1, p. 191-214
Commensurate Adsorption of Hydrocarbons and Alcohols in Microporous Metal Organic Frameworks
journal, September 2011
- Wu, Haohan; Gong, Qihan; Olson, David H.
- Chemical Reviews, Vol. 112, Issue 2
Carbon Dioxide Capture in Metal–Organic Frameworks
journal, September 2011
- Sumida, Kenji; Rogow, David L.; Mason, Jarad A.
- Chemical Reviews, Vol. 112, Issue 2, p. 724-781
Electrically Conductive Porous Metal-Organic Frameworks
journal, January 2016
- Sun, Lei; Campbell, Michael G.; Dincă, Mircea
- Angewandte Chemie International Edition, Vol. 55, Issue 11
MOF-based electronic and opto-electronic devices
journal, January 2014
- Stavila, V.; Talin, A. A.; Allendorf, M. D.
- Chem. Soc. Rev., Vol. 43, Issue 16
Two-dimensional metal–organic frameworks with high thermoelectric efficiency through metal ion selection
journal, January 2017
- He, Yuping; Spataru, Catalin D.; Léonard, Francois
- Physical Chemistry Chemical Physics, Vol. 19, Issue 29
Tunable Electrical Conductivity in Metal-Organic Framework Thin-Film Devices
journal, December 2013
- Talin, A. Alec; Centrone, Andrea; Ford, Alexandra C.
- Science, Vol. 343, Issue 6166, p. 66-69
Signature of Metallic Behavior in the Metal–Organic Frameworks M 3 (hexaiminobenzene) 2 (M = Ni, Cu)
journal, September 2017
- Dou, Jin-Hu; Sun, Lei; Ge, Yicong
- Journal of the American Chemical Society, Vol. 139, Issue 39
Tunable Mixed-Valence Doping toward Record Electrical Conductivity in a Three-Dimensional Metal–Organic Framework
journal, May 2018
- Xie, Lilia S.; Sun, Lei; Wan, Ruomeng
- Journal of the American Chemical Society, Vol. 140, Issue 24
The Chemistry and Applications of Metal-Organic Frameworks
journal, August 2013
- Furukawa, H.; Cordova, K. E.; O'Keeffe, M.
- Science, Vol. 341, Issue 6149, p. 1230444-1230444
A Survey on Transfer Learning
journal, October 2010
- Pan, Sinno Jialin; Yang, Qiang
- IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10
A Group Process Model for Problem Identification and Program Planning
journal, July 1971
- Delbecq, André L.; Van de Ven, Andrew H.
- The Journal of Applied Behavioral Science, Vol. 7, Issue 4
Mastering the game of Go with deep neural networks and tree search
journal, January 2016
- Silver, David; Huang, Aja; Maddison, Chris J.
- Nature, Vol. 529, Issue 7587
Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
journal, February 2018
- Bulgarevich, Dmitry S.; Tsukamoto, Susumu; Kasuya, Tadashi
- Scientific Reports, Vol. 8, Issue 1
Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning
journal, January 2018
- Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan
- Scientific Reports, Vol. 8, Issue 1
“Property Phase Diagrams” for Compound Semiconductors through Data Mining
journal, January 2013
- Srinivasan, Srikant; Rajan, Krishna
- Materials, Vol. 6, Issue 1
Quantum Loop Topography for Machine Learning
journal, May 2017
- Zhang, Yi; Kim, Eun-Ah
- Physical Review Letters, Vol. 118, Issue 21
Information-Theoretic Approach for the Discovery of Design Rules for Crystal Chemistry
journal, June 2012
- Kong, Chang Sun; Luo, Wei; Arapan, Sergiu
- Journal of Chemical Information and Modeling, Vol. 52, Issue 7
Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches
journal, December 2017
- Oliynyk, Anton O.; Mar, Arthur
- Accounts of Chemical Research, Vol. 51, Issue 1
Machine-Learning-Augmented Chemisorption Model for CO 2 Electroreduction Catalyst Screening
journal, August 2015
- Ma, Xianfeng; Li, Zheng; Achenie, Luke E. K.
- The Journal of Physical Chemistry Letters, Vol. 6, Issue 18
A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016
- Ward, Logan; Agrawal, Ankit; Choudhary, Alok
- npj Computational Materials, Vol. 2, Issue 1
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
journal, January 2017
- Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D.
- Energy & Environmental Science, Vol. 10, Issue 1
Predicting the Band Gaps of Inorganic Solids by Machine Learning
journal, March 2018
- Zhuo, Ya; Mansouri Tehrani, Aria; Brgoch, Jakoah
- The Journal of Physical Chemistry Letters, Vol. 9, Issue 7
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
journal, February 2018
- Janet, Jon Paul; Chan, Lydia; Kulik, Heather J.
- The Journal of Physical Chemistry Letters, Vol. 9, Issue 5
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007
- Behler, Jörg; Parrinello, Michele
- Physical Review Letters, Vol. 98, Issue 14
Representations in neural network based empirical potentials
journal, July 2017
- Cubuk, Ekin D.; Malone, Brad D.; Onat, Berk
- The Journal of Chemical Physics, Vol. 147, Issue 2
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
journal, June 2015
- Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan
- The Journal of Physical Chemistry Letters, Vol. 6, Issue 12
Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
journal, March 2015
- Cubuk, E. D.; Schoenholz, S. S.; Rieser, J. M.
- Physical Review Letters, Vol. 114, Issue 10
A structural approach to relaxation in glassy liquids
journal, February 2016
- Schoenholz, S. S.; Cubuk, E. D.; Sussman, D. M.
- Nature Physics, Vol. 12, Issue 5
Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals
journal, October 2014
- Chung, Yongchul G.; Camp, Jeffrey; Haranczyk, Maciej
- Chemistry of Materials, Vol. 26, Issue 21
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015
- Kirklin, Scott; Saal, James E.; Meredig, Bryce
- npj Computational Materials, Vol. 1, Issue 1
Generalized Gradient Approximation Made Simple
journal, October 1996
- Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
- Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
Multi-fidelity machine learning models for accurate bandgap predictions of solids
journal, March 2017
- Pilania, G.; Gubernatis, J. E.; Lookman, T.
- Computational Materials Science, Vol. 129
Informatics-aided bandgap engineering for solar materials
journal, February 2014
- Dey, Partha; Bible, Joe; Datta, Somnath
- Computational Materials Science, Vol. 83
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques
journal, March 2016
- Lee, Joohwi; Seko, Atsuto; Shitara, Kazuki
- Physical Review B, Vol. 93, Issue 11
Selecting and interpreting measures of thematic classification accuracy
journal, October 1997
- Stehman, Stephen V.
- Remote Sensing of Environment, Vol. 62, Issue 1
Thermoelectric Properties of 2D Ni 3 (hitp) 2 and 3D Cu 3 (btc) 2 MOFs: First-Principles Studies
journal, January 2017
- He, Yuping; Talin, A. Alec; Allendorf, Mark D.
- ECS Journal of Solid State Science and Technology, Vol. 6, Issue 12
High Electrical Conductivity in Ni 3 (2,3,6,7,10,11-hexaiminotriphenylene) 2 , a Semiconducting Metal–Organic Graphene Analogue
journal, April 2014
- Sheberla, Dennis; Sun, Lei; Blood-Forsythe, Martin A.
- Journal of the American Chemical Society, Vol. 136, Issue 25
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
journal, October 1996
- Kresse, G.; Furthmüller, J.
- Physical Review B, Vol. 54, Issue 16, p. 11169-11186
Extended framework materials incorporating cyanide cluster complexes: structure of the first 3D architecture accommodating organic molecules
journal, January 2001
- Naumov, Nikolaj G.; Artemkina, Sofia B.; Fedorov, Vladimir E.
- Chemical Communications, Issue 6
A new organic–inorganic hybrid framework containing octahedral hexarhenium cluster and its transformation by ligand exchange
journal, April 2007
- Kim, Sehye; Kim, Youngmee; Kal, Yunseon
- Inorganica Chimica Acta, Vol. 360, Issue 6
On the Electronic Conduction in Dry Thin Films of Prussian Blue, Prussian Yellow, and Everitt's Salt
journal, December 1991
- Xidis, Anthony; Neff, Vernon D.
- Journal of The Electrochemical Society, Vol. 138, Issue 12
Electronic conductivity in Berlin green and Prussian blue
journal, April 2011
- Pajerowski, Daniel M.; Watanabe, Takeshi; Yamamoto, Takashi
- Physical Review B, Vol. 83, Issue 15
Crystal and molecular structure of mercury(II) tetrathiocyanatobis(dimethylformamide)cobaltate(II)
journal, January 1980
- Udupa, M. R.; Krebs, B.
- Inorganica Chimica Acta, Vol. 42
Electroconductive Porous Coordination Polymer Cu[Cu(pdt) 2 ] Composed of Donor and Acceptor Building Units
journal, October 2009
- Takaishi, Shinya; Hosoda, Miyuki; Kajiwara, Takashi
- Inorganic Chemistry, Vol. 48, Issue 19
Conductivity, Doping, and Redox Chemistry of a Microporous Dithiolene-Based Metal−Organic Framework
journal, July 2010
- Kobayashi, Yoji; Jacobs, Benjamin; Allendorf, Mark D.
- Chemistry of Materials, Vol. 22, Issue 14
A europium( iii ) based metal–organic framework: bifunctional properties related to sensing and electronic conductivity
journal, January 2014
- Hao, Zhaomin; Yang, Guocheng; Song, Xuezhi
- J. Mater. Chem. A, Vol. 2, Issue 1
Mn 2 (2,5-disulfhydrylbenzene-1,4-dicarboxylate): A Microporous Metal–Organic Framework with Infinite (−Mn–S−) ∞ Chains and High Intrinsic Charge Mobility
journal, May 2013
- Sun, Lei; Miyakai, Tomoyo; Seki, Shu
- Journal of the American Chemical Society, Vol. 135, Issue 22
First-Principles Study of Microporous Magnets M-MOF-74 (M = Ni, Co, Fe, Mn): the Role of Metal Centers
journal, August 2013
- Zhang, Qiuju; Li, Baihai; Chen, Liang
- Inorganic Chemistry, Vol. 52, Issue 16
Conductive metal–organic frameworks and networks: fact or fantasy?
journal, January 2012
- Hendon, Christopher H.; Tiana, Davide; Walsh, Aron
- Physical Chemistry Chemical Physics, Vol. 14, Issue 38, p. 13120-13132
Works referencing / citing this record:
Metal–Organic Frameworks in Modern Physics: Highlights and Perspectives
journal, July 2019
- Mezenov, Yuri A.; Krasilin, Andrei A.; Dzyuba, Vladimir P.
- Advanced Science, Vol. 6, Issue 17
Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials
journal, September 2019
- Dubbeldam, David; Walton, Krista S.; Vlugt, Thijs J. H.
- Advanced Theory and Simulations, Vol. 2, Issue 11
Computational Studies of Photocatalysis with Metal–Organic Frameworks
journal, September 2019
- Wu, Xin‐Ping; Choudhuri, Indrani; Truhlar, Donald G.
- ENERGY & ENVIRONMENTAL MATERIALS, Vol. 2, Issue 4
Simulation and design of energy materials accelerated by machine learning
journal, June 2019
- Wang, Hongshuai; Ji, Yujin; Li, Youyong
- Wiley Interdisciplinary Reviews: Computational Molecular Science
Deep learning for molecular design—a review of the state of the art
journal, January 2019
- Elton, Daniel C.; Boukouvalas, Zois; Fuge, Mark D.
- Molecular Systems Design & Engineering, Vol. 4, Issue 4
Designing promising molecules for organic solar cells via machine learning assisted virtual screening
journal, January 2019
- Sahu, Harikrishna; Yang, Feng; Ye, Xiaobo
- Journal of Materials Chemistry A, Vol. 7, Issue 29
Progress and Perspective: Soft Thermoelectric Materials for Wearable and Internet‐of‐Things Applications
journal, February 2019
- Zaia, Edmond W.; Gordon, Madeleine P.; Yuan, Pengyu
- Advanced Electronic Materials, Vol. 5, Issue 11
Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
journal, June 2019
- Cubuk, Ekin D.; Sendek, Austin D.; Reed, Evan J.
- The Journal of Chemical Physics, Vol. 150, Issue 21
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
journal, January 2019
- Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
- Chemical Science, Vol. 10, Issue 34
From DFT to machine learning: recent approaches to materials science–a review
journal, May 2019
- Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera
- Journal of Physics: Materials, Vol. 2, Issue 3
Metal–Organic Frameworks in Modern Physics: Highlights and Perspectives
journal, July 2019
- Mezenov, Yuri A.; Krasilin, Andrei A.; Dzyuba, Vladimir P.
- Advanced Science, Vol. 6, Issue 17