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Chin. Phys. B, 2020, Vol. 29(11): 116103    DOI: 10.1088/1674-1056/abc0e3
Special Issue: SPECIAL TOPIC — Machine learning in condensed matter physics
TOPICAL REVIEW—Machine learning in condensed matter physics Prev   Next  

Machine learning in materials design: Algorithm and application

Zhilong Song(宋志龙), Xiwen Chen(陈曦雯), Fanbin Meng(孟繁斌), Guanjian Cheng(程观剑), Chen Wang(王陈), Zhongti Sun(孙中体), and Wan-Jian Yin(尹万健)
College of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow University, Suzhou 215006, China

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning (ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm: (i) data acquisition → (ii) feature engineering → (iii) algorithm → (iv) ML model → (v) model evaluation → (vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’: (i) structure and composition → (ii) property → (iii) synthesis → (iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

Keywords:  machine learning      materials design      structure-property relationship      active learning  
Received:  06 July 2020      Revised:  24 August 2020      Accepted manuscript online:  14 October 2020
Fund: Project support by the National Natural Science Foundation of China (Grant Nos. 11674237 and 51602211), the National Key Research and Development Program of China (Grant No. 2016YFB0700700), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China, and China Post-doctoral Foundation (Grant No. 7131705619).
Corresponding Authors:  Corresponding author. E-mail: Corresponding author. E-mail:   

Cite this article: 

Zhilong Song(宋志龙), Xiwen Chen(陈曦雯), Fanbin Meng(孟繁斌), Guanjian Cheng(程观剑), Chen Wang(王陈), Zhongti Sun(孙中体), and Wan-Jian Yin(尹万健) Machine learning in materials design: Algorithm and application 2020 Chin. Phys. B 29 116103

Fig. 1.  

The main workflow of traditional supervised learning and active learning.

Name Function URL
Pymatgen Robust, open-source python library for materials analysis
AFLOW π A minimalist framework for high-throughput first principles calculations
FireWorks An open-source code for defining, managing, and executing calculation workflows
AiiDA A workflow to automate complex numerical procedures of calculation
Pymatflow A workflow simplifier for research on materials science by means of ab initio simulation
ASE Setting up, steering, and analyzing atomistic simulations
Atomate Built on top of state-of-the-art open-source libraries: pymatgen, custodian, and FireWorks
Custodian A simple, robust, and flexible just-in-time (JIT) job management framework
MPInterfaces A python tool that enables high throughput analysis of interfaces using VASP, VASPsol, and MP tools
Imeall A database framework for the calculation of the atomistic properties of grain boundaries
Pylada A modular python framework to control physics simulations
Pyiron An integrated development environment (IDE) for computational materials science
Table 1.  

HT tools.

Name Data type URL Free
Materials Project Multiple
ICSD Inorganic & Experimental ×
AFLOWLIB Inorganic & Computational
COD Multiple & Experimental
QM9 Organic molecules
OMDB-GAP1 Organic crystals
OQMD Multiple & Computational
NOMAD Multiple
Materials Cloud Multiple (3D, 2D)
NREL Materials Computational & Renewable
Clean Energy Project Solar cell ×
TEDesignLab Thermoelectric
HTEM Inorganic
Supercon Superconducting
MaterialsWeb 2D
CSD Multiple ×
CMR Multiple (3D, 2D)
Citrination Multiple
MatNavi Multiple
MatWeb Engineering
GDB Small organic molecules
ZINC Compounds
ChEMBL Bioactive molecules
ChemSpider Multiple
Materials Commons Computational
AiiDA Alloy Phase Diagram
ASM Inorganic ×
LPF Multiple ×
PCD Multiple ×
Nano-HUB Nanomaterials
EELS Data Base Spectra
XAFS database Spectra
Table 2.  

Material databases.

Name Description URL
QML A python toolkit for representation learning of properties of molecules and solids
AMP A modular approach to machine learning in atomistic simulations
Magpie Materials-agnostic platform for informatics and exploration
RDkit A collection of cheminformatics and machine-learning software written in C++ and Python
ChemML A ML program suite for the analysis, mining, and modeling of chemical and materials data
DScribe Library of descriptors for machine learning in materials science
Matminer A Python library for data mining the properties of materials
SchNet A deep learning architecture for quantum chemistry
DeepChem Deep-learning models for drug discovery and quantum chemistry
MEGNet An implementation of DeepMind’s graph networks for universal machine learning in materials science
CGCNN Implement crystal graph convolutional neural networks to arbitrary crystal structures
Table 3.  

Feature tools.

Fig. 2.  

A simple fully connected neural network structure with two hidden layers.

Fig. 3.  

A 2D CNN structure with two max-pooling layers and one convolution layer.

Fig. 4.  

The sketch of the methodology of RNN.

Fig. 5.  

The workflow of the SR program.

Fig. 6.  

The structure of the AI Feynman. Reprinted with permission from Ref. [137].

Fig. 7.  

The mechanism of the BO method. Reprinted with permission from Ref. [150]. Copyright (2020) Springer Nature.

Fig. 8.  

Four main parts of MCTS.

Fig. 9.  

Typical learning curve, y axis refers to the value of loss function, and x axis is the number of examples.

Fig. 10.  

(a) The workflow of classification of XRD data with data augmentation method. (b) The structure of CNN model they used. Reprinted with permission from Ref. [2].

Conventional Deep learning Tree-based Descriptor Active Unsupervised
Thermodynamic stability [86,188193] [26] [188–[190,194196] [195] [86]
Band gap [31,193,197204] [20,203,205] [31,196,201,204] [33] [206]
Superconductivity [207210] [211,212] [19,213,214] [215] [207,210,213]
Thermal conductivity [79,216221] [79,218,222,223] [79,224227] [216,222,228230] [216]
Curie temperature [6,231236] [235237] [6] [233,236]
Bulk and shear moduli [238242] [97,102,243] [25,240,244246] [246]
Debye temperature and heat capacity [239,242,247,248] [25,248]
Density of states [87,249,250] [251] [249,252]
Dielectric breakdown strength [23,253255] [255] [253]
grain boundary structure and properties [256259] [260] [257,258] [261,262] [263]
Lattice parameter [264266] [266]
Lithium ion batteries SOC and conduction [22,267274] [275277] [22,273,278]
melting temperature [221,279282] [279] [279]
Table 4.  

ML application for some materials properties.

Fig. 11.  

The main workflow of screening stable halide perovskites via ML in combination with DFT calculations. Reprinted with permission from Ref. [192]. Copyright (2019) John Wiley and Sons.

Fig. 12.  

(a) The search progress for the halide perovskites with ideal decomposition energy and band gap. (b) The performance of BO. Reprinted with permission from Ref. [150]. Copyright (2020) Springer Nature.

Fig. 13.  

Workflow of screening semiconductors from the MXene database and predicting band gaps. Reprinted with permission from Ref. [200]. Copyright (2018) American Chemical Society.

Fig. 14.  

(a) The representation matrix of doped graphene supercell systems. (b) One of the CNN structures to predict band gaps. Reprinted with permission from Ref. [20].

Fig. 15.  

(a) Prediction on testing set of low-Tc, iron-based, and cuprate superconductors. (b)–(c) Prediction from model trained on data only containing low-Tc materials. (d)–(e) Prediction from model trained on data only containing cuprate materials. Reprinted with permission from Ref. [19].

Fig. 16.  

(a) The prediction result for ITR from LSBoost. (b) The relation between ITR prediction and the thickness and temperature. Reprinted with permission from Ref. [226]. Copyright (2018) American Chemical Society.

Fig. 17.  

The workflow of MCTS for optimizing the surface roughness. Reprinted with permission from Ref. [229].

Fig. 18.  

(a) The design loop for searching high-temperature ferroelectric perovskites. Reprinted from Ref. [6]. (b) The relation between the Tc and chemical composition for the ternary system Al–Co–Fe. Reprinted with permission from Ref. [236]. Copyright (2019) American Physical Society.

Fig. 19.  

The workflow of the work of Raccuglia[78] et al. Copyright (2016) Springer Nature.

Iten R, Metger T, Wilming H, Del Rio L, Renner R 2020 Phys. Rev. Lett. 124 010508 DOI: 10.1103/PhysRevLett.124.010508
Oviedo F, Ren Z, Sun S, Settens C, Liu Z, Hartono N T P, Ramasamy S, DeCost B L, Tian S I P, Romano G, Gilad Kusne A, Buonassisi T 2019 npj Comput. Mater. 5 60 DOI: 10.1038/s41524-019-0196-x
Ryan K, Lengyel J, Shatruk M 2018 J. Am. Chem. Soc. 140 10158 DOI: 10.1021/jacs.8b03913
Ziletti A, Kumar D, Scheffler M, Ghiringhelli L M 2018 Nat. Commun. 9 2775 DOI: 10.1038/s41467-018-05169-6
Podryabinkin E V, Tikhonov E V, Shapeev A V, Oganov A R 2019 Phys. Rev. B 99 064114 DOI: 10.1103/PhysRevB.99.064114
Balachandran P V, Kowalski B, Sehirlioglu A, Lookman T 2018 Nat. Commun. 9 1668 DOI: 10.1038/s41467-018-03821-9
Xu Q, Li Z, Liu M, Yin W J 2018 J. Phys. Chem. Lett. 9 6948 DOI: 10.1021/acs.jpclett.8b03232
Fischer C C, Tibbetts K J, Morgan D, Ceder G 2006 Nat. Mater. 5 641 DOI: 10.1038/nmat1691
Segler M H S, Preuss M, Waller M P 2018 Nature 555 604 DOI: 10.1038/nature25978
Coley C W, Thomas D A, Lummiss J A M, Jaworski J N, Breen C P, Schultz V, Hart T, Fishman J S, Rogers L, Gao H, Hicklin R W, Plehiers P P, Byington J, Piotti J S, Green W H, John Hart A, Jamison T F, Jensen K F 2019 Science 365 eaax1566 DOI: 10.1126/science.aax1566
Frey N C, Wang J, Vega Bellido G I, Anasori B, Gogotsi Y, Shenoy V B 2019 ACS Nano 13 3031 DOI: 10.1021/acsnano.8b08014
Rao C N R, Natarajan S, Neeraj S 2000 J. Am. Chem. Soc. 122 2810 DOI: 10.1021/ja993892f
Kim E, Huang K, Saunders A, McCallum A, Ceder G, Olivetti E 2017 Chem. Mater. 29 9436 DOI: 10.1021/acs.chemmater.7b03500
Vasudevan R K, Laanait N, Ferragut E M, Wang K, Geohegan D B, Xiao K, Ziatdinov M, Jesse S, Dyck O, Kalinin S V 2018 npj Comput. Mater. 4 30 DOI: 10.1038/s41524-018-0086-7
Maksov A, Dyck O, Wang K, Xiao K, Geohegan D B, Sumpter B G, Vasudevan R K, Jesse S, Kalinin S V, Ziatdinov M 2019 npj Comput. Mater. 5 12 DOI: 10.1038/s41524-019-0152-9
Li W, Field K G, Morgan D 2018 npj Comput. Mater. 4 36 DOI: 10.1038/s41524-018-0093-8
Sanchez-Gonzalez A, Micaelli P, Olivier C, Barillot T R, Ilchen M, Lutman A A, Marinelli A, Maxwell T, Achner A, Agåker M, Berrah N, Bostedt C, Bozek J D, Buck J, Bucksbaum P H, Montero S C, Cooper B, Cryan J P, Dong M, Feifel R, Frasinski L J, Fukuzawa H, Galler A, Hartmann G, Hartmann N, Helml W, Johnson A S, Knie A, Lindahl A O, Liu J, Motomura K, Mucke M, O’Grady C, Rubensson J E, Simpson E R, Squibb R J, Såthe C, Ueda K, Vacher M, Walke D J, Zhaunerchyk V, Coffee R N, Marangos J P 2017 Nat. Commun. 8 15461 DOI: 10.1038/ncomms15461
Ghosh K, Stuke A, Todorović M, Jørgensen P B, Schmidt M N, Vehtari A, Rinke P 2019 Adv. Sci. 6 1801367 DOI: 10.1002/advs.201801367
Stanev V, Oses C, Kusne A G, Rodriguez E, Paglione J, Curtarolo S, Takeuchi I 2018 npj Comput. Mater. 4 29 DOI: 10.1038/s41524-018-0085-8
Dong Y, Wu C, Zhang C, Liu Y, Cheng J, Lin J 2019 npj Comput. Mater. 5 26 DOI: 10.1038/s41524-019-0165-4
Liu Y, Wu J, Wang Z, Lu X G, Avdeev M, Shi S, Wang C, Yu T 2020 Acta Mater. 195 454 DOI: 10.1016/j.actamat.2020.05.001
Ng M F, Zhao J, Yan Q, Conduit G J, Seh Z W 2020 Nat. Mach. Intell. 2 161 DOI: 10.1038/s42256-020-0156-7
Shen Z H, Wang J J, Jiang J Y, Huang S X, Lin Y H, Nan C W, Chen L Q, Shen Y 2019 Nat. Commun. 10 1843 DOI: 10.1038/s41467-019-09874-8
Xue D, Balachandran P V, Hogden J, Theiler J, Xue D, Lookman T 2016 Nat. Commun. 7 11241 DOI: 10.1038/ncomms11241
Isayev O, Oses C, Toher C, Gossett E, Curtarolo S, Tropsha A 2017 Nat. Commun. 8 15679 DOI: 10.1038/ncomms15679
Ye W, Chen C, Wang Z, Chu I H, Ong S P 2018 Nat. Commun. 9 3800 DOI: 10.1038/s41467-018-06322-x
Bartel C J, Sutton C, Goldsmith B R, Ouyang R, Musgrave C B, Ghiringhelli L M, Scheffler M 2019 Sci. Adv. 5 eaav0693 DOI: 10.1126/sciadv.aav0693
Liu Y, Wu J, Yang G, Zhao T, Shi S 2019 Sci. Bull. 64 1195 DOI: 10.1016/j.scib.2019.06.026
Liu Y, Zhao T, Yang G, Ju W, Shi S 2017 Comput. Mater. Sci. 140 315 DOI: 10.1016/j.commatsci.2017.09.008
Cao B, Adutwum L A, Oliynyk A O, Luber E J, Olsen B C, Mar A, Buriak J M 2018 ACS Nano 12 7434 DOI: 10.1021/acsnano.8b04726
Li J, Pradhan B, Gaur S, Thomas J 2019 Adv. Energy Mater. 9 1970181 DOI: 10.1002/aenm.201970181
Zubatyuk R, Smith J S, Leszczynski J, Isayev O 2019 Sci. Adv. 5 eaav6490 DOI: 10.1126/sciadv.aav6490
Ling J, Hutchinson M, Antono E, Paradiso S, Meredig B 2017 Integr. Mater. Manuf. Innov. 6 207 DOI: 10.1007/s40192-017-0098-z
Settles B 2012 Synth. Lect. Artif. Intell. Mach. Learn. 6 1 DOI: 10.2200/S00429ED1V01Y201207AIM018
Ong S P, Richards W D, Jain A, Hautier G, Kocher M, Cholia S, Gunter D, Chevrier V L, Persson K A, Ceder G 2013 Comput. Mater. Sci. 68 314 DOI: 10.1016/j.commatsci.2012.10.028
Pizzi G, Cepellotti A, Sabatini R, Marzari N, Kozinsky B 2016 Comput. Mater. Sci. 111 218 DOI: 10.1016/j.commatsci.2015.09.013
Jain A, Ong S P, Chen W, Medasani B, Qu X, Kocher M, Brafman M, Petretto G, Rignanese G M, Hautier G, Gunter D, Persson K A 2015 Concurr. Comput. 27 5037 DOI: 10.1002/cpe.3505
Lambert H, Fekete A, Kermode J R, De A 2018 Comput. Phys. Commun. 232 256 DOI: 10.1016/j.cpc.2018.04.029
Mathew K, Singh A K, Gabriel J J, Choudhary K, Sinnott S B, Davydov A V, Tavazza F, Hennig R G 2016 Comput. Mater. Sci. 122 183 DOI: 10.1016/j.commatsci.2016.05.020
Mathew K, Montoya J H, Faghaninia A, Dwarakanath S, Aykol M, Tang H, heng I, Smidt T, Bocklund B, Horton M, Dagdelen J, Wood B, Liu Z K, Neaton J, Ong S P, Persson K, Jain A 2017 Comput. Mater. Sci. 139 140 DOI: 10.1016/j.commatsci.2017.07.030
Supka A R, Lyons T E, Liyanage L, D’Amico P, Al Rahal Al Orabi R, Mahatara S, Gopal P, Toher C, Ceresoli D, Calzolari A, Curtarolo S, Nardelli M B, Fornari M 2017 Comput. Mater. Sci. 136 76 DOI: 10.1016/j.commatsci.2017.03.055
Hjorth Larsen A, Jørgen Mortensen J, Blomqvist J, Castelli I E, Christensen R, Dułak M, Friis J, Groves M N, Hammer B, Hargus C, Hermes E D, Jennings P C, Bjerre Jensen P, Kermode J, Kitchin J R, Leonhard Kolsbjerg E, Kubal J, Kaasbjerg K, Lysgaard S, Bergmann Maronsson J, Maxson T, Olsen T, Pastewka L, Peterson A, Rostgaard C, Schiøtz J, Schütt O, Strange M, Thygesen K S, Vegge T, Vilhelmsen L, Walter M, Zeng Z, Jacobsen K W 2017 J. Phys. Condens. Matter 29 273002 DOI: 10.1088/1361-648X/aa680e
Janssen J, Surendralal S, Lysogorskiy Y, Todorova M, Hickel T, Drautz R, Neugebauer J 2019 Comput. Mater. Sci. 163 24 DOI: 10.1016/j.commatsci.2018.07.043
Jain A, Ong S P, Hautier G, Chen W, Richards W D, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson K A 2013 APL Mater. 1 011002 DOI: 10.1063/1.4812323
Hellenbrandt M 2004 Crystallogr. Rev. 10 17 DOI: 10.1080/08893110410001664882
Kirklin S, Saal J E, Meredig B, Thompson A, Doak J W, Aykol M, Rühl S, Wolverton C 2015 npj Comput. Mater. 1 15010 DOI: 10.1038/npjcompumats.2015.10
Haastrup S, Strange M, Pandey M, Deilmann T, Schmidt P S, Hinsche N F, Gjerding M N, Torelli D, Larsen P M, Riis-Jensen A C, Gath J, Jacobsen K W, Mortensen J J, Olsen T, Thygesen K S 2018 2D Mater. 5 042002 DOI: 10.1088/2053-1583/aacfc1
Stevanović V, Lany S, Zhang X, Zunger A 2012 Phys. Rev. B - Condens. Matter Mater. Phys. 85 115104 DOI: 10.1103/PhysRevB.85.115104
Graulis S, Chateigner D, Downs R T, Yokochi A F T, Quirós M, Lutterotti L, Manakova E, Butkus J, Moeck P, Le Bail A 2009 J. Appl. Crystallogr. 42 726 DOI: 10.1107/S0021889809016690
Saal J E, Kirklin S, Aykol M, Meredig B, Wolverton C 2013 Jom 65 1501 DOI: 10.1007/s11837-013-0755-4
Borysov S S, Geilhufe R M, Balatsky A V 2017 PLoS One 12 e0171501 DOI: 10.1371/journal.pone.0171501
Ashton M, Paul J, Sinnott S B, Hennig R G 2017 Phys. Rev. Lett. 118 106101 DOI: 10.1103/PhysRevLett.118.106101
Williams A 2008 Chem. Int. 30
Choudhary K, Cheon G, Reed E, Tavazza F 2018 Phys. Rev. B 98 014107 DOI: 10.1103/PhysRevB.98.014107
Groom C R, Bruno I J, Lightfoot M P, Ward S C 2016 Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater. 72 171 DOI: 10.1107/S2052520616003954
Sterling T, Irwin J J 2015 J. Chem. Inf. Model. 55 2324 DOI: 10.1021/acs.jcim.5b00559
Landis D D, Hummelshøj J S, Nestorov S, Greeley J, Dulłak M, Bligaard T, Nørskov J K, Jacobsen K W 2012 Comput. Sci. Eng. 14 51 DOI: 10.1109/MCSE.2012.16
Gorai P, Gao D, Ortiz B, Miller S, Barnett S A, Mason T, Lv Q, Stevanović V, Toberer E S 2016 Comput. Mater. Sci. 112 368 DOI: 10.1016/j.commatsci.2015.11.006
Curtarolo S, Setyawan W, Wang S, Xue J, Yang K, Taylor R H, Nelson L J, Hart G L W, Sanvito S, Buongiorno-Nardelli M, Mingo N, Levy O 2012 Comput. Mater. Sci. 58 227 DOI: 10.1016/j.commatsci.2012.02.002
Gaulton A, Hersey A, Nowotka M L, Patricia Bento A, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis L J, Cibrian-Uhalte E, Davies M, Dedman N, Karlsson A, Magarinos M P, Overington J P, Papadatos G, Smit I, Leach A R 2017 Nucleic Acids Res. 45 D945 DOI: 10.1093/nar/gkw1074
Zakutayev A, Wunder N, Schwarting M, Perkins J D, White R, Munch K, Tumas W, Phillips C 2018 Sci. Data 5 180053 DOI: 10.1038/sdata.2018.53
Ramakrishnan R, Dral P O, Rupp M, Von Lilienfeld O A 2014 Sci. Data 1 140022 DOI: 10.1038/sdata.2014.22
Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, Amador-Bedolla C, Sánchez-Carrera R S, Gold-Parker A, Vogt L, Brockway A M, Aspuru-Guzik A 2011 J. Phys. Chem. Lett. 2 2241 DOI: 10.1021/jz200866s
Hill J, Mannodi-Kanakkithodi A, Ramprasad R, Meredig B 2018 Computational Materials System Design Cham Springer International Publishing 193 DOI: 10.1007/978-3-319-68280-8_9
Fink T, Reymond J 2007 J. Chem. Inf. Model. 47 342 DOI: 10.1021/ci600423u
Glasser L 2016 J. Chem. Educ. 93 542 DOI: 10.1021/acs.jchemed.5b00253
Puchala B, Tarcea G, Marquis E A, Hedstrom M, Jagadish H V, Allison J E 2016 JOM 68 2035 DOI: 10.1007/s11837-016-1998-7
Villars P, Onodera N, Iwata S 1998 J. Alloys Compd. 279 1 DOI: 10.1016/S0925-8388(98)00605-7
Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z, Kononova O, Persson K A, Ceder G, Jain A 2019 Nature 571 95 DOI: 10.1038/s41586-019-1335-8
Torayev A, Magusin P C M M, Grey C P, Merlet C, Franco A A 2019 J. Phys. Mater. 2 044004 DOI: 10.1088/2515-7639/ab3611
Swain M C, Cole J M 2016 J. Chem. Inf. Model. 56 1894 DOI: 10.1021/acs.jcim.6b00207
Jessop D M, Adams S E, Murray-Rust P 2011 J. Cheminform. 3 40 DOI: 10.1186/1758-2946-3-40
Jones D E, Igo S, Hurdle J, Facelli J C 2014 PLoS One 9 e83932 DOI: 10.1371/journal.pone.0083932
Krallinger M, Rabal O, Lourenço A, Oyarzabal J, Valencia A 2017 Chem. Rev. 117 7673 DOI: 10.1021/acs.chemrev.6b00851
Jensen Z, Kim E, Kwon S, Gani T Z H, Román-Leshkov Y, Moliner M, Corma A, Olivetti E 2019 ACS Cent. Sci. 5 892 DOI: 10.1021/acscentsci.9b00193
Fourches D, Muratov E, Tropsha A 2010 J. Chem. Inf. Model. 50 1189 DOI: 10.1021/ci100176x
Wilkinson M D, Dumontier M, Aalbersberg Ij J, Appleton G, Axton M, Baak A, Blomberg N, Boiten J W, da Silva Santos L B, Bourne P E, Bouwman J, Brookes A J, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo C T, Finkers R, Gonzalez-Beltran A, Gray A J G, Groth P, Goble C, Grethe J S, Heringa J t, Hoen P A C, Hooft R, Kuhn T, Kok R, Kok J, Lusher S J, Martone M E, Mons A, Packer A L, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz M A, Thompson M, Van Der Lei J, Van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B 2016 Sci. Data 3 160018 DOI: 10.1038/sdata.2016.18
Raccuglia P, Elbert K C, Adler P D F, Falk C, Wenny M B, Mollo A, Zeller M, Friedler S A, Schrier J, Norquist A J 2016 Nature 533 73 DOI: 10.1038/nature17439
Yang H, Zhang Z, Zhang J, Zeng X C 2018 Nanoscale 10 19092 DOI: 10.1039/C8NR05703F
Gilmer J, Schoenholz S S, Riley P F, Vinyals O, Dahl G E 2017 $34$th Int. Conf. Mach. Learn. ICML 2017 3 2053 DOI: 10.5555/3305381.3305512
Balachandran P V, Emery A A, Gubernatis J E, Lookman T, Wolverton C, Zunger A 2018 Phys. Rev. Mater. 2 043802 DOI: 10.1103/PhysRevMaterials.2.043802
Kajita S, Ohba N, Jinnouchi R, Asahi R 2017 Sci. Rep. 7 16991 DOI: 10.1038/s41598-017-17299-w
Hoffmann J, Maestrati L, Sawada Y, Tang J, Sellier J M, Bengio Y 2019 arXiv:1909.00949 [cs.LG]
Weininger D 1988 J. Chem. Inf. Comput. Sci. 28 31 DOI: 10.1021/ci00057a005
Rupp M, Tkatchenko A, Müller K R, Von Lilienfeld O A 2012 Phys. Rev. Lett. 108 058301 DOI: 10.1103/PhysRevLett.108.058301
Faber F, Lindmaa A, Von Lilienfeld O A, Armiento R 2015 Int. J. Quantum Chem. 115 1094 DOI: 10.1002/qua.24917
Schütt K T, Glawe H, Brockherde F, Sanna A, Müller K R, Gross E K U 2014 Phys. Rev. B 89 205118 DOI: 10.1103/PhysRevB.89.205118
Huang B, Von Lilienfeld O A 2016 J. Chem. Phys. 145 161102 DOI: 10.1063/1.4964627
Hansen K, Biegler F, Ramakrishnan R, Pronobis W, Von Lilienfeld O A, Müller K R, Tkatchenko A 2015 J. Phys. Chem. Lett. 6 2326 DOI: 10.1021/acs.jpclett.5b00831
Rogers D, Hahn M 2010 J. Chem. Inf. Model. 50 742 DOI: 10.1021/ci100050t
Huo H, Rupp M 2017 arXiv:1704.06439 [physics.chem-ph]
Bartók A P, Kondor R, Csányi G 2013 Phys. Rev. B 87 184115 DOI: 10.1103/PhysRevB.87.184115
Behler J 2011 J. Chem. Phys. 134 074106 DOI: 10.1063/1.3553717
Choudhary K, Decost B, Tavazza F 2018 Phys. Rev. Mater. 2 083801 DOI: 10.1103/PhysRevMaterials.2.083801
Mezey Paul G R, Stephen Berry J I B 1994 Graph Theoretical Approaches to Chemical Reactivity Bonchev D, Mekenyan O Dordrecht Springer Netherlands DOI: 10.1007/978-94-011-1202-4
Scarselli Franco, Gori Marco, Tsoi Ah Chung, Markus Hagenbuchner G M 2009 IEEE Trans. Neural Networks 20 61 DOI: 10.1109/TNN.2008.2005605
Xie T, Grossman J C 2018 Phys. Rev. Lett. 120 145301 DOI: 10.1103/PhysRevLett.120.145301
F R S K P 1901 London Edinburgh Dublin Philos. Mag. J. Sci. 2 559 DOI: 10.1080/14786440109462720
Khorshidi A, Peterson A A 2016 Comput. Phys. Commun. 207 310 DOI: 10.1016/j.cpc.2016.05.010
Ramsundar B, Eastman P, Walters P, Pande V, Leswing K, Wu Z 2019 Deep Learning for the Life Sciences Sebastopol O’Reilly Media
Ward L, Dunn A, Faghaninia A, Zimmermann N E R, Bajaj S, Wang Q, Montoya J, Chen J, Bystrom K, Dylla M, Chard K, Asta M, Persson K A, Snyder G J, Foster I, Jain A 2018 Comput. Mater. Sci. 152 60 DOI: 10.1016/j.commatsci.2018.05.018
Chen C, Ye W, Zuo Y, Zheng C, Ong S P 2019 Chem. Mater. 31 3564 DOI: 10.1021/acs.chemmater.9b01294
Himanen L, Jäger M O J, Morooka E V, Federici Canova F, Ranawat Y S, Gao D Z, Rinke P, Foster A S 2020 Comput. Phys. Commun. 247 106949 DOI: 10.1016/j.cpc.2019.106949
Schütt K T, Sauceda H E, Kindermans P J, Tkatchenko A, Müller K R 2018 J. Chem. Phys. 148 241722 DOI: 10.1063/1.5019779
Landrum G 2016 rdkit: open source cheminformatics software
Ward L, Agrawal A, Choudhary A, Wolverton C 2016 npj Comput. Mater. 2 16028 DOI: 10.1038/npjcompumats.2016.28
Haghighatlari M, Vishwakarma G, Altarawy D, Subramanian R, Kota B U, Sonpal A, Setlur S, Hachmann J 2020 WIREs Comput. Mol. Sci. 10 e1458
McKinney W 2011 Python High Perform. Sci. Comput. 14 DOI: 10.4018/978-1-5225-9902-9.ch008
Baranwal A, Bagwe B R, and M V 2011 J. Mach. Learn. Res. 12 128
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M others 2016 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16) 265 DOI: 10.1002/adts.201900215
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S 2019 Advances in Neural Information Processing Systems 32 Wallach H, Larochelle H, Beygelzimer A, Alché-Buc F, Fox E, Garnett R Curran Associates, Inc. 8024
Liu Y, Wu J, Avdeev M, Shi S 2020 Adv. Theory Simul. 3 1900215 DOI: 10.1002/adts.201900215
Chauhan N K, Singh K 2019 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018 IEEE 347 DOI: 10.1109/GUCON.2018.8675097
Schneider A, Hommel G, Blettner M 2010 Dtsch. Arztebl. 107 776 DOI: 10.3238/arztebl.2010.0776
Tibshirani R 1996 J. R. Stat. Soc. Ser. B 58 267 DOI: 10.1111/j.2517-6161.1996.tb02080.x
Hoerl A E, Kennard R W 1970 Technometrics 12 55 DOI: 10.1080/00401706.1970.10488634
Zou H, Hastie T 2005 J. R. Stat. Soc. Ser. B 67 301 DOI: 10.1111/j.1467-9868.2005.00503.x
Murphy K P 2012 Machine Learning: A Probabilistic Perspective Cambridge The MIT Press
A Smola B S 2004 Stat. Comput. 14 199 DOI: 10.1023/B:STCO.0000035301.49549.88
Cortes C, Vapnik V 1995 Mach. Learn. 20 273 DOI: 10.1007/BF00994018
O’Shea K, Nash R 2015 arXiv:1511.08458 [cs.NE]
Lecun Y, Bengio Y, Hinton G 2015 Nature 521 436 DOI: 10.1038/nature14539
Nielsen M A 2018 Neural Networks and Deep Learning Determination Press
Quinlan J R 1987 Int. J. Man. Mach. Stud. 27 221 DOI: 10.1016/S0020-7373(87)80053-6
Prokhorenkova L, Gusev G, Vorobev A, Dorogush A V, Gulin A 2018 Advances in neural information processing systems 6638 DOI: 10.1145/2939672.2939785
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T Y 2017 Advances in Neural Information Processing Systems 30 Guyon I, Luxburg U V, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R Curran Associates, Inc. 3146 DOI: 10.5555/3294996.3295074
Freund Y, Schapire R E 1996 Proc. 13th Int. Conf. Mach. Learn. 148 DOI: 10.5555/3091696.3091715
Chen T, Guestrin C 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’16 New York, NY, USA ACM 785 DOI: 10.1145/2939672.2939785
Xuan P, Sun C, Zhang T, Ye Y, Shen T, Dong Y 2019 Front. Genet. 10 459 DOI: 10.3389/fgene.2019.00459
Liaw A, Wiener M 2002 R News 2 18
Rabiner L, Juang B 1986 IEEE ASSP Mag. 3 4 DOI: 10.1109/MASSP.1986.1165342
Kingma D P, Welling M 2019 Found. Trends®Mach. Learn. 12 307 DOI: 10.1561/2200000056
Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath A A 2018 IEEE Signal Process. Mag. 35 53 DOI: 10.1109/MSP.2017.2765202
Ouyang R, Curtarolo S, Ahmetcik E, Scheffler M, Ghiringhelli L M 2018 Phys. Rev. Mater. 2 83802 DOI: 10.1103/PhysRevMaterials.2.083802
Augusto D A, Barbosa H J C 2000 Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks vol 2000-Janua IEEE Comput. Soc 173 DOI: 10.1109/SBRN.2000.889734
Wang Y, Wagner N, Rondinelli J M 2019 MRS Commun. 9 793 DOI: 10.1557/mrc.2019.85
Udrescu S M, Tegmark M 2020 Sci. Adv. 6 eaay2631 DOI: 10.1126/sciadv.aay2631
Dubčáková R 2011 Genet. Program. Evolvable Mach. 12 173 DOI: 10.1007/s10710-010-9124-z
Fan J, Lv J 2008 J. R. Stat. Soc. Ser. B 70 849 DOI: 10.1111/j.1467-9868.2008.00674.x
Eldar Y C, Kutyniok G 2012 Compressed Sensing: Theory and Applications Cambridge Cambridge University Press
Ghiringhelli L M, Vybiral J, Ahmetcik E, Ouyang R, Levchenko S V, Draxl C, Scheffler M 2017 New J. Phys. 19 023017 DOI: 10.1088/1367-2630/aa57bf
Pankajakshan P, Sanyal S, De Noord O E, Bhattacharya I, Bhattacharyya A, Waghmare U 2017 Chem. Mater. 29 4190 DOI: 10.1021/acs.chemmater.6b04229
Settles B 2012 Active Learning San Rafael Morgan & Claypool Publishers
Gubaev K, Podryabinkin E V, Shapeev A V 2018 J. Chem. Phys. 148 241727 DOI: 10.1063/1.5005095
Tran K, Ulissi Z W 2018 Nat. Catal. 1 696 DOI: 10.1038/s41929-018-0142-1
Yuan R, Liu Z, Balachandran P V, Xue D, Zhou Y, Ding X, Sun J, Xue D, Lookman T 2018 Adv. Mater. 30 1702884 DOI: 10.1002/adma.201702884
Botu V, Ramprasad R 2015 Int. J. Quantum Chem. 115 1074 DOI: 10.1002/qua.24836
Le T T, Fu W, Moore J H 2020 Bioinformatics 36 250 DOI: 10.1093/bioinformatics/btz470
Zhong M, Tran K, Min Y, Wang C, Wang Z, Dinh C T, De Luna P, Yu Z, Rasouli A S, Brodersen P, Sun S, Voznyy O, Tan C S, Askerka M, Che F, Liu M, Seifitokaldani A, Pang Y, Lo S C, Ip A, Ulissi Z, Sargent E H 2020 Nature 581 178 DOI: 10.1038/s41586-020-2242-8
Chen X, Wang C, Li Z, Hou Z, Yin W J 2020 Sci. Chin. Mater. 63 1024 DOI: 10.1007/s40843-019-1255-4
Dieb T M, Ju S, Shiomi J, Tsuda K 2019 MRS Commun. 9 532 DOI: 10.1557/mrc.2019.40
Dieb T M, Hou Z, Tsuda K 2018 J. Chem. Phys. 148 241716 DOI: 10.1063/1.5018065
M Dieb T, Ju S, Yoshizoe K, Hou Z, Shiomi J, Tsuda K 2017 Sci. Technol. Adv. Mater. 18 498 DOI: 10.1080/14686996.2017.1344083
Mnih V, Kavukcuoglu K, Silver D, Rusu A A, Veness J, Bellemare M G, Graves A, Riedmiller M, Fidjeland A K, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D 2015 Nature 518 529 DOI: 10.1038/nature14236
Meredig B, Antono E, Church C, Hutchinson M, Ling J, Paradiso S, Blaiszik B, Foster I, Gibbons B, Hattrick-Simpers J, Mehta A, Ward L 2018 Mol. Syst. Eng. 3 819 DOI: 10.1039/C8ME00012C
Butler K T, Davies D W, Cartwright H, Isayev O, Walsh A 2018 Nature 559 547 DOI: 10.1038/s41586-018-0337-2
Bergstra J, Bengio Y 2012 J. Mach. Learn. Res. 13 281 DOI: 10.1088/1749-4699/8/1/014008
Gao Y, Yang H, Zhang P, Zhou C, Hu Y 2019 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings 1
Liaw R, Liang E, Nishihara R, Moritz P, Gonzalez J E, Stoica I 2018 arXiv:1807.05118 [cs.LG]
Microsoft 2020 NNI (Neural Network Intelligence)
Bergstra J, Komer B, Eliasmith C, Yamins D, Cox D D 2015 Comput. Sci. Discov. 8 014008 DOI: 10.1088/1749-4699/8/1/014008
Jacobs R, Mayeshiba T, Afflerbach B, Miles L, Williams M, Turner M, Finkel R, Morgan D 2020 Comput. Mater. Sci. 176 109544 DOI: 10.1016/j.commatsci.2020.109544
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R 2014 J. Mach. Learn. Res. 15 1929 DOI: 10.5555/2627435.2670313
Oliynyk A O, Adutwum L A, Harynuk J J, Mar A 2016 Chem. Mater. 28 6672 DOI: 10.1021/acs.chemmater.6b02905
Oliynyk A O, Adutwum L A, Rudyk B W, Pisavadia H, Lotfi S, Hlukhyy V, Harynuk J J, Mar A, Brgoch J 2017 J. Am. Chem. Soc. 139 17870 DOI: 10.1021/jacs.7b08460
Graser J, Kauwe S K, Sparks T D 2018 Chem. Mater. 30 3601 DOI: 10.1021/acs.chemmater.7b05304
Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P 2002 J. Artif. Int. Res. 16 321 DOI: 10.5555/1622407.1622416
Park W B, Chung J, Jung J, Sohn K, Singh S P, Pyo M, Shin N, Sohn K S 2017 IUCrJ. 4 486 DOI: 10.1107/S205225251700714X
Oganov A R, Glass C W 2006 J. Chem. Phys. 124 244704 DOI: 10.1063/1.2210932
Oganov A R, Lyakhov A O, Valle M 2011 Acc. Chem. Res. 44 227 DOI: 10.1021/ar1001318
Lyakhov A O, Oganov A R, Stokes H T, Zhu Q 2013 Comput. Phys. Commun. 184 1172 DOI: 10.1016/j.cpc.2012.12.009
Wu S Q, Ji M, Wang C Z, Nguyen M C, Zhao X, Umemoto K, Wentzcovitch R M, Ho K M 2014 J. Phys.: Condens. Matter 26 035402 DOI: 10.1088/0953-8984/26/3/035402
Wang Y, Lv J, Zhu L, Ma Y 2010 Phys. Rev. B 82 094116 DOI: 10.1103/PhysRevB.82.094116
Wang Y, Lv J, Zhu L, Ma Y 2012 Comput. Phys. Commun. 183 2063 DOI: 10.1016/j.cpc.2012.05.008
Tian Y, Sun W, Chen B, Jin Y, Lu C 2019 Chin. Phys. B 28 103104 DOI: 10.1088/1674-1056/ab4274
Tang C, Kour G, Du A 2019 Chin. Phys. B 28 107306 DOI: 10.1088/1674-1056/ab41ea
Pickard C J, Needs R J 2011 J. Phys.: Condens. Matter 23 053201 DOI: 10.1088/0953-8984/23/5/053201
Doll K, Schön J C, Jansen M 2007 Phys. Chem. Chem. Phys. 9 6128 DOI: 10.1039/b709943f
Kirkpatrick S, Gelatt C D, Vecchi M P 1983 Science 220 671 DOI: 10.1126/science.220.4598.671
Wille L T 1987 Nature 325 374 DOI: 10.1038/325374c0
Wales D J, Doye J P K 1997 J. Phys. Chem. A 101 5111 DOI: 10.1021/jp970984n
Heiles S, Johnston R L 2013 Int. J. Quantum Chem. 113 2091 DOI: 10.1002/qua.24462
Burnham C J, English N J 2019 J. Chem. Theory Comput. 15 3889 DOI: 10.1021/acs.jctc.9b00073
Amsler M, Goedecker S 2010 J. Chem. Phys. 133 224104 DOI: 10.1063/1.3512900
Goedecker S 2004 J. Chem. Phys. 120 9911 DOI: 10.1063/1.1724816
Yamashita T, Sato N, Kino H, Miyake T, Tsuda K, Oguchi T 2018 Phys. Rev. Mater. 2 013803 DOI: 10.1103/PhysRevMaterials.2.013803
Seko A, Ishiwata S 2020 Phys. Rev. B 101 134101 DOI: 10.1103/PhysRevB.101.134101
Li W, Jacobs R, Morgan D 2018 Comput. Mater. Sci. 150 454 DOI: 10.1016/j.commatsci.2018.04.033
Ward L, Liu R, Krishna A, Hegde V I, Agrawal A, Choudhary A, Wolverton C 2017 Phys. Rev. B 96 024104 DOI: 10.1103/PhysRevB.96.024104
Schmidt J, Shi J, Borlido P, Chen L, Botti S, Marques M A L 2017 Chem. Mater. 29 5090 DOI: 10.1021/acs.chemmater.7b00156
Faber F A, Lindmaa A, Von Lilienfeld O A, Armiento R 2016 Phys. Rev. Lett. 117 135502 DOI: 10.1103/PhysRevLett.117.135502
Li Z, Xu Q, Sun Q, Hou Z, Yin W J 2019 Adv. Funct. Mater. 29 1807280 DOI: 10.1002/adfm.201807280
Stanley J C, Mayr F, Gagliardi A 2020 Adv. Theory Simul. 3 1900178 DOI: 10.1002/adts.201900178
Schmidt J, Chen L, Botti S, Marques M A L 2018 J. Chem. Phys. 148 241728 DOI: 10.1063/1.5020223
Schleder G R, Acosta C M, Fazzio A 2020 ACS Appl. Mater. Interfaces 12 20149 DOI: 10.1021/acsami.9b14530
Kailkhura B, Gallagher B, Kim S, Hiszpanski A, Han T Y J 2019 npj Comput. Mater. 5 108 DOI: 10.1038/s41524-019-0248-2
Pilania G, Mannodi-Kanakkithodi A, Uberuaga B P, Ramprasad R, Gubernatis J E, Lookman T 2016 Sci. Rep. 6 19375 DOI: 10.1038/srep19375
Lee J, Seko A, Shitara K, Nakayama K, Tanaka I 2016 Phys. Rev. B 93 115104 DOI: 10.1103/PhysRevB.93.115104
Pilania G, Gubernatis J E, Lookman T 2017 Comput. Mater. Sci. 129 156 DOI: 10.1016/j.commatsci.2016.12.004
Rajan A C, Mishra A, Satsangi S, Vaish R, Mizuseki H, Lee K R, Singh A K 2018 Chem. Mater. 30 4031 DOI: 10.1021/acs.chemmater.8b00686
Weston L, Stampfl C 2018 Phys. Rev. Mater. 2 085407 DOI: 10.1103/PhysRevMaterials.2.085407
Zhuo Y, Mansouri Tehrani A, Brgoch J 2018 J. Phys. Chem. Lett. 9 1668 DOI: 10.1021/acs.jpclett.8b00124
Olsthoorn B, Geilhufe R M, Borysov S S, Balatsky A V 2019 Adv. Quantum Technol. 2 1900023 DOI: 10.1002/qute.201900023
Lu S, Zhou Q, Ouyang Y, Guo Y, Li Q, Wang J 2018 Nat. Commun. 9 3405 DOI: 10.1038/s41467-018-05761-w
Gómez-Bombarelli R, Wei J N, Duvenaud D, Hernández-Lobato J M, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel T D, Adams R P, Aspuru-Guzik A 2018 ACS Cent. Sci. 4 268 DOI: 10.1021/acscentsci.7b00572
Dey P, Bible J, Datta S, Broderick S, Jasinski J, Sunkara M, Menon M, Rajan K 2014 Comput. Mater. Sci. 83 185 DOI: 10.1016/j.commatsci.2013.10.016
Ziatdinov M, Maksov A, Li L, Sefat A S, Maksymovych P, Kalinin S V 2016 Nanotechnology 27 475706 DOI: 10.1088/0957-4484/27/47/475706
Owolabi T O, Akande K O, Olatunji S O 2014 Adv. Phys. Theor. Appl. 35 12
Owolabi T O, Akande K O, Olatunji S O 2015 J. Supercond. Nov. Magn. 28 75 DOI: 10.1007/s10948-014-2891-7
Liu Y, Zhang H, Xu Y, Li S, Dai D, Li C, Ding G, Shen W, Qian Q 2018 Mater. Tehnol. 52 639 DOI: 10.17222/mit.2018.043
Le T D, Noumeir R, Quach H L, Kim J H, Kim J H, Kim H M 2020 IEEE Trans. Appl. Supercond. 30 1 DOI: 10.1109/TIA.2019.2957707
Konno T, Kurokawa H, Nabeshima F, Sakishita Y, Ogawa R, Hosako I, Atsutaka 2018 arxiv: 1812.01995
Hamidieh K 2018 Comput. Mater. Sci. 154 346 DOI: 10.1016/j.commatsci.2018.07.052
Matsumoto K, Horide T 2019 Appl. Phys. Express 12 073003 DOI: 10.7567/1882-0786/ab2922
Xie S R, Stewart G R, Hamlin J J, Hirschfeld P J, Hennig R G 2019 Phys. Rev. B 100 174513 DOI: 10.1103/PhysRevB.100.174513
Roekeghem A, Carrete J, Oses C, Curtarolo S, Mingo N 2016 Phys. Rev. X 6 041061 DOI: 10.1103/PhysRevX.6.041061
Zhan T, Fang L, Xu Y 2017 Sci. Rep. 7 7109 DOI: 10.1038/s41598-017-07150-7
Wei H, Zhao S, Rong Q, Bao H 2018 Int. J. Heat Mass Transf. 127 908 DOI: 10.1016/j.ijheatmasstransfer.2018.08.082
Qian X, Peng S, Li X, Wei Y, Yang R 2019 Mater. Today Phys. 10 100140 DOI: 10.1016/j.mtphys.2019.100140
Sosso G C, Deringer V L, Elliott S R, Csányi G 2018 Mol. Simul. 44 866 DOI: 10.1080/08927022.2018.1447107
Seko A, Hayashi H, Nakayama K, Takahashi A, Tanaka I 2017 Phys. Rev. B 95 144110 DOI: 10.1103/PhysRevB.95.144110
Wan J, Jiang J W, Park H S 2020 Carbon N. Y. 157 262 DOI: 10.1016/j.carbon.2019.10.037
Li R, Lee E, Luo T 2020 Mater. Today Phys. 12 100181 DOI: 10.1016/j.mtphys.2020.100181
Gaultois M W, Oliynyk A O, Mar A, Sparks T D, Mulholland G J, Meredig B 2016 APL Mater. 4 053213 DOI: 10.1063/1.4952607
Carrete J, Li W, Mingo N, Wang S, Curtarolo S 2014 Phys. Rev. X 4 011019 DOI: 10.1103/PhysRevX.4.011019
Wu Y J, Sasaki M, Goto M, Fang L, Xu Y 2018 ACS Appl. Nano Mater. 1 3355 DOI: 10.1021/acsanm.8b0057510.1038/s41524-019-0193-0
Wu Y J, Fang L, Xu Y 2019 npj Comput. Mater. 5 56 DOI: 10.1038/s41524-019-0193-010.1103/PhysRevX.7.021024
Ju S, Shiga T, Feng L, Hou Z, Tsuda K, Shiomi J 2017 Phys. Rev. X 7 021024 DOI: 10.1103/PhysRevX.7.021024
Ju S, Tsuda K, Shiomi J, Dieb T M 2018 32nd Conference on Neural Information Processing Systems (NIPS 2018)
Wei H, Bao H, Ruan X 2020 Nano Energy 71 104619 DOI: 10.1016/j.nanoen.2020.104619
Balachandran P V 2020 J. Mater. Res. 35 890 DOI: 10.1557/jmr.2020.38
Balachandran P V, Xue D, Lookman T 2016 Phys. Rev. B 93 144111 DOI: 10.1103/PhysRevB.93.144111
Dam H C, Nguyen V C, Pham T L, Nguyen A T, Terakura K, Miyake T, Kino H 2018 J. Phys. Soc. Jpn. 87 113801 DOI: 10.7566/JPSJ.87.113801
Nguyen D N, Pham T L, Nguyen V C, Nguyen A T, Kino H, Miyake T, Dam H C 2019 J. Phys. Conf. Ser. 1290 012009 DOI: 10.1088/1742-6596/1290/1/012009
Zhai X, Chen M, Lu W 2018 Comput. Mater. Sci. 151 41 DOI: 10.1016/j.commatsci.2018.04.031
Nelson J, Sanvito S 2019 Phys. Rev. Mater. 3 104405 DOI: 10.1103/PhysRevMaterials.3.104405
Zhang B, Zheng X Q, Zhao T Y, Hu F X, Sun J R, Shen B G 2018 Chin. Phys. B 27 067503 DOI: 10.1088/1674-1056/27/6/067503
De Jong M, Chen W, Notestine R, Persson K, Ceder G, Jain A, Asta M, Gamst A 2016 Sci. Rep. 6 34256 DOI: 10.1038/srep34256
Furmanchuk A, Agrawal A, Choudhary A 2016 RSC Adv. 6 95246 DOI: 10.1039/C6RA19284J
Mansouri Tehrani A, Oliynyk A O, Parry M, Rizvi Z, Couper S, Lin F, Miyagi L, Sparks T D, Brgoch J 2018 J. Am. Chem. Soc. 140 9844 DOI: 10.1021/jacs.8b02717
Chapman J, Batra R, Ramprasad R 2020 Comput. Mater. Sci. 174 109483 DOI: 10.1016/j.commatsci.2019.109483
Kauwe S K, Graser J, Murdock R, Sparks T D 2020 Comput. Mater. Sci. 174 109498 DOI: 10.1016/j.commatsci.2019.109498
Zhao Y, Yuan K, Liu Y, Louis S Y, Hu M, Hu J 2020 arXiv:2003.13425 [cond-mat.mtrl-sci]
Evans J D, Coudert F X 2017 Chem. Mater. 29 7833 DOI: 10.1021/acs.chemmater.7b02532
Kim G, Diao H, Lee C, Samaei A T, Phan T, de Jong M, An K, Ma D, Liaw P K, Chen W 2019 Acta Mater. 181 124 DOI: 10.1016/j.actamat.2019.09.026
Xiong J, Shi S Q, Zhang T Y 2020 Materials & Design 187 108378 DOI: 10.1016/j.matdes.2019.108378
Zhuo Y, Mansouri Tehrani A, Oliynyk A O, Duke A C, Brgoch J 2018 Nat. Commun. 9 4377 DOI: 10.1038/s41467-018-06625-z
Kauwe S K, Graser J, Vazquez A, Sparks T D 2018 Integr. Mater. Manuf. Innov. 7 43 DOI: 10.1007/s40192-018-0108-9
Yeo B C, Kim D, Kim C, Han S S 2019 Sci. Rep. 9 5879 DOI: 10.1038/s41598-019-42277-9
Umeno Y, Kubo A 2019 Comput. Mater. Sci. 168 164 DOI: 10.1016/j.commatsci.2019.06.005
Chandrasekaran A, Kamal D, Batra R, Kim C, Chen L, Ramprasad R 2019 npj Comput. Mater. 5 22 DOI: 10.1038/s41524-019-0162-7
Broderick S R, Aourag H, Rajan K 2011 J. Am. Ceram. Soc. 94 2974 DOI: 10.1111/j.1551-2916.2011.04476.x
Yuan F, Mueller T 2017 Sci. Rep. 7 17594 DOI: 10.1038/s41598-017-17535-3
Kim C, Pilania G, Ramprasad R 2016 J. Phys. Chem. C 120 14575 DOI: 10.1021/acs.jpcc.6b05068
Kim C, Pilania G, Ramprasad R 2016 Chem. Mater. 28 1304 DOI: 10.1021/acs.chemmater.5b04109
Wu X, Wang Y X, He K N, Li X, Liu W, Zhang Y, Xu Y, Liu C 2020 Mater. (Basel). 13 179 DOI: 10.3390/ma13010179
Huber L, Hadian R, Grabowski B, Neugebauer J 2018 npj Comput. Mater. 4 64 DOI: 10.1038/s41524-018-0122-7
Rosenbrock C W, Homer E R, Csányi G, Hart G L W 2017 npj Comput. Mater. 3 29 DOI: 10.1038/s41524-017-0027-x
Kiyohara S, Oda H, Miyata T, Mizoguchi T 2016 Sci. Adv. 2 e1600746 DOI: 10.1126/sciadv.1600746
Homer E R, Hensley D M, Rosenbrock C W, Nguyen A H, Hart G L W 2019 Front. Mater. 6 168 DOI: 10.3389/fmats.2019.00168
Kiyohara S, Oda H, Tsuda K, Mizoguchi T 2016 Jpn. J. Appl. Phys. 55 45502 DOI: 10.7567/JJAP.55.045502
Kikuchi S, Oda H, Kiyohara S, Mizoguchi T 2018 Phys. B Condens. Matter 532 24 DOI: 10.1016/j.physb.2017.03.006
Zhu Q, Samanta A, Li B, Rudd R E, Frolov T 2018 Nat. Commun. 9 467 DOI: 10.1038/s41467-018-02937-2
Alade I O, Olumegbon I A, Bagudu A 2020 J. Appl. Phys. 127 015303 DOI: 10.1063/1.5130664
Pilania G, Liu X Y 2018 J. Mater. Sci. 53 6652 DOI: 10.1007/s10853-018-1987-z
Liu Y, Zhao T, Ju W, Shi S 2017 J. Materiomics 3 159 DOI: 10.1016/j.jmat.2017.08.002
Hannan M A, Lipu M S H, Hussain A, Ker P J, Mahlia T M I, Mansor M, Ayob A, Saad M H, Dong Z Y 2020 Sci. Rep. 10 4687 DOI: 10.1038/s41598-020-61464-7
Xu Y, Hu M, Zhou A, Li Y, Li S, Fu C, Gong C 2020 Appl. Math. Model. 77 1255 DOI: 10.1016/j.apm.2019.09.011
Shen Y 2020 Electrochim. Acta 336 135664 DOI: 10.1016/j.electacta.2020.135664
Wang C, Aoyagi K, Wisesa P, Mueller T 2020 Chem. Mater. 32 3741 DOI: 10.1021/acs.chemmater.9b04663
Li S, Li J, He H, Wang H 2019 Energy Procedia 159 168 DOI: 10.1016/j.egypro.2018.12.046
Sendek A D, Cubuk E D, Antoniuk E R, Cheon G, Cui Y, Reed E J 2019 Chem. Mater. 31 342 DOI: 10.1021/acs.chemmater.8b03272
Attarian Shandiz M, Gauvin R 2016 Comput. Mater. Sci. 117 270 DOI: 10.1016/j.commatsci.2016.02.021
Shi S, Gao J, Liu Y, Zhao Y, Wu Q, Ju W, Ouyang C, Xiao R 2016 Chin. Phys. B 25 018212 DOI: 10.1088/1674-1056/25/1/018212
Chemali E, Kollmeyer P J, Preindl M, Emadi A 2018 J. Power Sources 400 242 DOI: 10.1016/j.jpowsour.2018.06.104
Bian C, He H, Yang S 2020 Energy 191 116538 DOI: 10.1016/
Wang W, Brady N W, Liao C, Fahmy Y A, Chemali E, West A C, M 2019 arxiv: 1909.02448
Sidhu M S, Ronanki D, Williamson S 2019 IECON 2019 - 45th Annu. Conf. IEEE Ind. Electron. Soc. 1 2732 DOI: 10.1109/iecon.2019.8927066
Sivaraman G, Jackson N E, Sanchez-Lengeling B, Vázquez-Mayagoitia A, Aspuru-Guzik A, Vishwanath V, De Pablo J J 2019 chemrxiv.9914378.v1
Qu N, Liu Y, Liao M, Lai Z, Zhou F, Cui P, Han T, Yang D, Zhu J 2019 Ceram. Int. 45 18551 DOI: 10.1016/j.ceramint.2019.06.076
Pilania G, Gubernatis J E, Lookman T 2015 Phys. Rev. B 91 214302 DOI: 10.1103/PhysRevB.91.214302
Seko A, Maekawa T, Tsuda K, Tanaka I 2014 Phys. Rev. B 89 054303 DOI: 10.1103/PhysRevB.89.054303
Jacobs R, Mayeshiba T, Booske J, Morgan D 2018 Adv. Energy Mater. 8 1702708 DOI: 10.1002/aenm.201702708
Sun Q, Yin W J 2017 J. Am. Chem. Soc. 139 14905 DOI: 10.1021/jacs.7b09379
De S, Bartók A P, Csányi G, Ceriotti M 2016 Phys. Chem. Chem. Phys. 18 13754 DOI: 10.1039/C6CP00415F
Jäger M O J, Morooka E V, Federici Canova F, Himanen L, Foster A S 2018 npj Comput. Mater. 4 37 DOI: 10.1038/s41524-018-0096-5
Winiarski M J, Wiendlocha B, Gołab S, Kushwaha S K, Wiśniewski P, Kaczorowski D, Thompson J D, Cava R J, Klimczuk T 2016 Phys. Chem. Chem. Phys. 18 21737 DOI: 10.1039/C6CP02856J
Kamihara Y, Watanabe T, Hirano M, Hosono H 2008 J. Am. Chem. Soc. 130 3296 DOI: 10.1021/ja800073m
Allen P B, Dynes R C 1975 Phys. Rev. B 12 905 DOI: 10.1103/PhysRevB.12.905
Drozdov A P, Eremets M I, Troyan I A, Ksenofontov V, Shylin S I 2015 Nature 525 73 DOI: 10.1038/nature14964
Seko A, Togo A, Hayashi H, Tsuda K, Chaput L, Tanaka I 2015 Phys. Rev. Lett. 115 205901 DOI: 10.1103/PhysRevLett.115.205901
Wang M, Wang J, Pan N, Chen S 2007 Phys. Rev. E 75 36702 DOI: 10.1103/PhysRevE.75.036702
He X, Chen S, Doolen G D 1998 J. Comput. Phys. 146 282 DOI: 10.1006/jcph.1998.6057
Feng T, Ruan X 2016 Carbon N. Y. 101 107 DOI: 10.1016/j.carbon.2016.01.082
Hu S, Zhang Z, Jiang P, Chen J, Volz S, Nomura M, Li B 2018 J. Phys. Chem. Lett. 9 3959 DOI: 10.1021/acs.jpclett.8b01653
Hu S, Zhang Z, Jiang P, Ren W, Yu C, Shiomi J, Chen J 2019 Nanoscale 11 11839 DOI: 10.1039/C9NR02548K
Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M, Palmer A, Settels V, Jaakkola T, Jensen K, Barzilay R 2019 J. Chem. Inf. Model. 59 3370 DOI: 10.1021/acs.jcim.9b00237
Faber F A, Hutchison L, Huang B, Gilmer J, Schoenholz S S, Dahl G E, Vinyals O, Kearnes S, Riley P F, Von Lilienfeld O A 2017 J. Chem. Theory Comput. 13 5255 DOI: 10.1021/acs.jctc.7b00577
Kearnes S, McCloskey K, Berndl M, Pande V, Riley P 2016 J. Comput. Aided. Mol. 30 595 DOI: 10.1007/s10822-016-9938-8
Li Y, Tarlow D, Brockschmidt M, Zemel R 2015 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings 1
Schütt K T, Arbabzadah F, Chmiela S, Müller K R, Tkatchenko A 2017 Nat. Commun. 8 13890 DOI: 10.1038/ncomms13890
Wu Z, Ramsundar B, Feinberg E N, Gomes J, Geniesse C, Pappu A S, Leswing K, Pande V 2018 Chem. Sci. 9 513 DOI: 10.1039/C7SC02664A
Chmiela S, Tkatchenko A, Sauceda H E, Poltavsky I, Schütt K T, Müller K R 2017 Sci. Adv. 3 e1603015 DOI: 10.1126/sciadv.1603015
Lu C, Liu Q, Wang C, Huang Z, Lin P, He L 2019 Proceedings of the AAAI Conference on Artificial Intelligence 33 1052 DOI: 10.1609/aaai.v33i01.33011052
Smith J S, Isayev O, Roitberg A E 2017 Sci. Data 4 170193 DOI: 10.1038/sdata.2017.193
Unke O T, Meuwly M 2019 J. Chem. Theory Comput. 15 3678 DOI: 10.1021/acs.jctc.9b00181
Chen B, Barzilay R, Jaakkola T 2019 arXiv:1905.12712 [cs.LG]
Sanyal S, Balachandran J, Yadati N, Kumar A, Rajagopalan P, Sanyal S, Talukdar P 2018 arXiv:1811.05660 [cs.LG]
Yamamoto T 2019 Crystal Graph Neural Networks for Data Mining in Materials Science Yokohama Research Institute for Mathematical and Computational Sciences, LLC
Klicpera J, Groß J, Günnemann S 2020 arXiv:2003.03123 [cs.LG]
Houben C, Lapkin A A 2015 Curr. Opin. Chem. Eng. 9 1 DOI: 10.1016/j.coche.2015.07.001
Wicker J G P, Cooper R I 2015 CrystEngComm 17 1927 DOI: 10.1039/C4CE01912A
Kayala M A, Azencott C A, Chen J H, Baldi P 2011 J. Chem. Inf. Model. 51 2209 DOI: 10.1021/ci200207y
Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Luu Nguyen Q, Ho S, Sloane J, Wender P, Pande V 2017 ACS Cent. Sci. 3 1103 DOI: 10.1021/acscentsci.7b00303
Coley C W, Barzilay R, Jaakkola T S, Green W H, Jensen K F 2017 ACS Cent. Sci. 3 434 DOI: 10.1021/acscentsci.7b00064
Wei J N, Duvenaud D, Aspuru-Guzik A 2016 ACS Cent. Sci. 2 725 DOI: 10.1021/acscentsci.6b00219
Houben C, Peremezhney N, Zubov A, Kosek J, Lapkin A A 2015 Org. Process Res. Dev. 19 1049 DOI: 10.1021/acs.oprd.5b00210
Collins K D, Gensch T, Glorius F 2014 Nat. Chem. 6 859 DOI: 10.1038/nchem.2062
Santanilla A B, Regalado E L, Pereira T, Shevlin M, Bateman K, Campeau L, Schneeweis J, Berritt S, Shi Z, Nantermet P, Liu Y, Helmy R, Welch C J, Vachal P, Davies I W, Cernak T, Dreher S D 2015 Science 347 49 DOI: 10.1126/science.1259203
Boyarshinov V 1997 Machine Learning Long Beach McGraw-Hill
Cundy C S, Cox P A 2003 Chem. Rev. 103 663 DOI: 10.1021/cr020060i
Yang S, Wang Y, Liu P, Cheng Y B, Zhao H J, Yang H G 1992 Nat. Energy 1 1 DOI: 10.1038/ng0492-1
Zhang H, Ren X, Chen X, Mao J, Cheng J, Zhao Y, Liu Y, Milic J, Yin W J, Grätzel M, Choy W C H 2018 Energy Environ. Sci. 11 2253 DOI: 10.1039/C8EE00580J
Correa-Baena J P, Saliba M, Buonassisi T, Grätzel M, Abate A, Tress W, Hagfeldt A 2017 Science 358 739 DOI: 10.1126/science.aam6323
Jeon N J, Noh J H, Yang W S, Kim Y C, Ryu S, Seo J, Il S 2015 Nature 517 476 DOI: 10.1038/nature14133
Kumawat N K, Gupta D, Kabra D 2017 Energy Technol. 5 1734 DOI: 10.1002/ente.201700356
Li W, Zhang W, Van Reenen S, Sutton R J, Fan J, Haghighirad A A, Johnston M B, Wang L, Snaith H J 2016 Energy Environ. Sci. 9 490 DOI: 10.1039/C5EE03522H
Wang F, Geng W, Zhou Y, Fang H H, Tong C J, Loi M A, Liu L M, Zhao N 2016 Adv. Mater. 28 9986 DOI: 10.1002/adma.201603062
Yu Y, Tan X, Ning S, Wu Y 2019 ACS Energy Lett. 4 397 DOI: 10.1021/acsenergylett.8b02451
Novoselov K S, Geim A K, Morozov S V, Jiang D, Zhang Y, Dubonos S V, Grigorieva I V, Firsov A A 2004 Science 306 666 DOI: 10.1126/science.1102896
Kwak J, Jo Y, Song S, Kim J H, Kim S Y, Lee J U, Lee S, Park J, Kim K, Do G, Yoo J W, Kim S Y, Kong Y M, Lee G H, Lee W G, Park J, Xu X, Cheong H, Yoon E, Lee Z, Kwon S Y 2018 Adv. Mater. 30 1707260 DOI: 10.1002/adma.201707260
Li Y, Zhou Z, Zhang S, Chen Z 2008 J. Am. Chem. Soc. 130 16739 DOI: 10.1021/ja805545x
Zheng F, Cai C, Ge S, Zhang X, Liu X, Lu H, Zhang Y, Qiu J, Taniguchi T, Watanabe K, Jia S, Qi J, Chen J H, Sun D, Feng J 2016 Adv. Mater. 28 4845 DOI: 10.1002/adma.201600100
Xu S Y, Ma Q, Shen H, Fatemi V, Wu S, Chang T R, Chang G, Valdivia A M M, Chan C K, Gibson Q D, Zhou J, Liu Z, Watanabe K, Taniguchi T, Lin H, Cava R J, Fu L, Gedik N, Jarillo-Herrero P 2018 Nat. Phys. 14 900 DOI: 10.1038/s41567-018-0189-6
Xu M, Tang B, Zhu C, Lu Y, Zhu C, Zheng L, Zhang J, Han N, Guo Y, Di J, Song P, He Y, Kang L, Zhang Z, Zhao W, Guan C, Wang X, Liu Z 2019 arXiv:1910.04603 [cond-mat.mtrl-sci]
Leo Frkanec Snežzana Miljanić, Mladen Žini ć Tomislav Biljan Zlatko Meić 2007 J. Raman Spectrosc. 38 1538 DOI: 10.1002/jrs.1902
Timoshenko J, Frenkel A I 2019 ACS Catal. 9 10192 DOI: 10.1021/acscatal.9b03599
Huang B, Clark G, Navarro-Moratalla E, Klein D R, Cheng R, Seyler K L, Di Zhong, Schmidgall E, McGuire M A, Cobden D H, Yao W, Xiao D, Jarillo-Herrero P, Xu X 2017 Nature 546 270 DOI: 10.1038/nature22391
Geim A K, Novoselov K S 2007 Nat. Mater. 6 183 DOI: 10.1038/nmat1849
Li Y, Kong Y, Peng J, Yu C, Li Z, Li P, Liu Y, Gao C F, Wu R 2019 J. Materiomics 5 413 DOI: 10.1016/j.jmat.2019.03.003
Akinwande D, Brennan C J, Bunch J S, Egberts P, Felts J R, Gao H, Huang R, Kim J S, Li T, Li Y, Liechti K M, Lu N, Park H S, Reed E J, Wang P, Yakobson B I, Zhang T, Zhang Y W, Zhou Y, Zhu Y 2017 Extrem. Mech. Lett. 13 42 DOI: 10.1016/j.eml.2017.01.008
De Heer W A, Berger C, Ruan M, Sprinkle M, Li X, Hu Y, Zhang B, Hankinson J, Conrad E 2011 Proc. Natl. Acad. Sci. USA 108 16900 DOI: 10.1073/pnas.1105113108
Gao Y, Cao T, Cellini F, Berger C, De Heer W A, Tosatti E, Riedo E, Bongiorno A 2018 Nat. Nanotechnol. 13 133 DOI: 10.1038/s41565-017-0023-9
Cellini F, Lavini F, Berger C, De Heer W, Riedo E 2019 2D Mater. 6 035043 DOI: 10.1088/2053-1583/ab1b9f
Ziatdinov M, Dyck O, Maksov A, Li X, Sang X, Xiao K, Unocic R R, Vasudevan R, Jesse S, Kalinin S V 2017 ACS Nano 11 12742 DOI: 10.1021/acsnano.7b07504
Dan J, Zhao X, Pennycook S J 2019 InfoMat 1 359 DOI: 10.1002/inf2.12026
Moore A M, Weiss P S 2008 Annu. Rev. Anal. Chem. 1 857 DOI: 10.1146/annurev.anchem.1.031207.112932
Muller D A 2009 Nat. Mater. 8 263 DOI: 10.1038/nmat2380
Wagner N, Rondinelli J M 2016 Front. Mater. 3 28 DOI: 10.3389/fmats.2016.00028
Okamoto Y, Kubo Y 2018 ACS Omega 3 7868 DOI: 10.1021/acsomega.8b00576
Molnar C 2019 Interpretable Machine Learning Leanpub
Schmidt M, Lipson H 2009 Science 324 81 DOI: 10.1126/science.1165893
Hernandez A, Balasubramanian A, Yuan F, Mason S A M, Mueller T 2019 npj Comput. Mater. 5 112 DOI: 10.1038/s41524-019-0249-1
Weng B, Song Z, Zhu R, Yan Q, Sun Q, Grice C G, Yan Y, Yin W J 2020 Nat. Commun. 11 3513 DOI: 10.1038/s41467-020-17263-9
Bartel C J, Millican S L, Deml A M, Rumptz J R, Tumas W, Weimer A W, Lany S, Stevanović V, Musgrave C B, Holder A M 2018 Nat. Commun. 9 4168 DOI: 10.1038/s41467-018-06682-4
Silver D, Huang A, Maddison C J, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D 2016 Nature 529 484 DOI: 10.1038/nature16961
Cao Z P, Zhao Y J, Liao J H, Yang X B 2017 RSC Adv. 7 37881 DOI: 10.1039/C7RA06891C
Li X, Yang Z, Catherine Brinson L, Choudhary A, Agrawal A, Chen W 2018 Proceedings of the ASME Design Engineering Technical Conference 2B-2018 1 DOI: 10.1115/DETC2018-85633
Nouira A, Sokolovska N, Crivello J C 2019 CEUR Workshop Proc. 2350
Sanchez-Lengeling B, Outeiral C, Guimaraes G L, Aspuru-Guzik A 2017 ChemRxiv.5309668.v3
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