A machine learning-based crystal graph network and its application in development of functional materials

Abstract

An active area of MGI (Materials Genome Initiative)/MGE (Materials Genome Engineering) is to accelerate the development of new materials by means of active learning and “digital trial-error” using a prediction model of material property. Machine learning methods have widely been employed for predicting crystalline materials properties with crystal graph neural networks (CGNN). The prediction accuracy of the state-of-the-art (SOTA) CGNN models based on big models and big data is generally higher. However, for the development of some classes of materials, the datasets obtained by experiments are usually lacking due to costly experiments and measurement costs. The lack of datasets will impact the accuracy of CGNN models and may result in overfitting during training models. This paper proposes a simplified crystal graph convolutional neural network (S-CGCNN) which possesses higher prediction accuracy while reducing the vast amount of train datasets and computation costs. The S-CGCNN model has successfully predicted properties of crystalline materials, such as piezoelectric materials and dielectric materials, and increased the prediction accuracy up to 12%–20% than existing SOTA CGNN models. Furthermore, the distribution map between properties and compositions of materials has been built to screen the latent space of candidate materials efficiently by principal component analysis.

Keywords

crystal graph neural network / crystalline materials / functional material / machine learning

References

1	Xiong W, Olson GB. Cybermaterials: materials by design and accelerated insertion of materials. NPJ Comput Mater. 2016;2(1):15009.
2	Wang WY, Yin J, Liu ZK, et al. Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products. J Mater Inf. 2022;2:1.
3	Xie JX, Su YJ, Xue DH, Jiang X, Fu HD, Huang HY. Machine learning for materials research and development. Acta Metall Sin. 2021;57(11):1343-1361.
4	Xu DG, Zhang Q, Huo XG, Wang YT, Yang ML. Advances in dataassisted high-throughput computations for material design. MGE Adv. 2023;1(1).
5	Geng XX, Wang FY, Wu HH, et al. Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology. MGE Adv. 2023;1(1).
6	Xie WJ, Wang WH, Liu YH. On the application of high-throughput experimentation and data-driven approaches in metallic glasses. MGE Adv. 2023;1(1).
7	Kirklin S, Saal JE, Meredig B, et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. NPJ Comput Mater. 2015;1:15010.
8	Curtarolo S, Setyawan W, Wang S, et al. AFLOWLIB. ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput Mater Sci. 2012;58:227.
9	Castelli IE, Hüser F, Pandey M, et al. New light-harvesting materials using accurate and efficient bandgap calculations. Adv Energy Mater. 2015;5(2):1400915.
10	NIMS Database. https://mits.nims.go.jp/index_en.html
11	Van de Walle A, Nataraj C, Liu ZK. The thermodynamic database. Calphad Comput Coupling Phase Diagrams Thermochem. 2018;61:173-178.
12	Jain A, Ong SP, Hautier G, et al. The Materials Project: a materials genome approach to accelerating materials innovation. Apl Mater. 2013;1:011002.
13	Choudhary K, Kalish I, Beams R, Tavazza F. High-throughput identification and characterization of two-dimensional materials using density functional theory. Sci Rep. 2017;7(1):5179.
14	Cheng GJ, Gong XG, Yin WJ. Crystal structure prediction by combining graph network and optimization algorithm. Nat Commun. 2022;13(1):1492.
15	Schmidt J, Marques MRG, Botti S. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater. 2019;5(1):83.
16	Kim K, Ward L, He J, Krishna A, Agrawal A, Wolverton C. Machine learning-accelerated high-throughput materials screening: discovery of novel quaternary Heusler compounds. Phys Rev Mater. 2018;2(12):123801.
17	Ward L, Agrawal A, Choudhary A, Wolverton CA. general-purpose machine learning framework for predicting properties of inorganic materials. NPJ Comput Mater. 2016;2(1):16028.
18	Ward L, Liu R, Krishna A, et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys Rev B. 2017;96(2):024104.
19	Im S, Shang SL, Smith ND, et al. Thermodynamic properties of the Nd-Bi system via emf measurements, DFT calculations, machine learning, and CALPHAD modeling. Acta Mater. 2022;223:117448.
20	Jha D, Ward L, Paul A, et al. ElemNet: deep learning the chemistry of materials from only elemental composition. Sci Rep. 2018;8(1):17593.
21	Ye W, Chen C, Wang Z, Chu IH, Ong SP. Deep neural networks for accurate predictions of crystal stability. Nat Commun. 2018;9(1):3800.
22	Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet -a deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24):241722.
23	Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural Message Passing for Quantum Chemistry; 2017. arXiv:1704.01212.
24	Jørgensen PB, Jacobsen KW, Schmidt MN. Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials; 2018. arXiv:1806.03146.
25	Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301.
26	Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-3572.
27	Park CW, Wolverton C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys Rev Mater. 2020;4(6):063801.
28	Karamad M, Magar R, Shi Y, Siahrostami S, Gates ID, Barati Farimani A. Orbital graph convolutional neural network for material property prediction. Phys Rev Mater. 2020;4(9):093801.
29	Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. NPJ Comput Mater. 2021;7(1):185.
30	Omee SS, Louis S-Y. Hu J.-J, et al. Scalable deeper graph neural networks for high performance materials property prediction. Patterns. 2022;3(5):100491.
31	Rong Z, Kitchaev D, Canepa P, Huang W, Ceder G. An efficient algorithm for finding the minimum energy path for cation migration in ionic materials. J Chem Phys. 2016;7:74112.
32	Rao Z, Springer H, Ponge D, Li Z. Combinatorial development of multicomponent invar alloys via rapid alloy prototyping. Materialia. 2022;21:101326.
33	Wen C, Zhang Y, Wang CD, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019;170:109-117.
34	Zhou Z, Zhou Y, He Q, Ding Z, Li F, Yang Y. Machine learning guided appraisal and exploration of phase design for high entropy alloys. NPJ Comput Mater. 2019;5:1.
35	Rao Z, Tung PY, Xie R, et al. Machine learning-enabled high-entropy alloy discovery. Science. 2022;378(6615):78-85.
36	Choudhary K, Garrity KF, Sharma V, Biacchi AJ, Hight Walker AR, Tavazza F. High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses. NPJ Comput Mater. 2020;6(1):64.
37	Choudhary K, Bercx M, Jiang J, Pachter R, Lamoen D, Tavazza F. Accelerated discovery of efficient solar-cell materials using quantum and machine-learning methods. Chem Mater. 2019;31(15):5900-5908.
38	Choudhary K, Garrity KF, Jiang J, Pachter R, Tavazza F. Computational search for magnetic and non-magnetic 2D topological materials using unified spin–orbit spillage screening. NPJ Comput Mater. 2020;6(1):49.
39	Choudhary K, Cheon G, Reed E, Tavazza F. Elastic properties of bulk and lowdimensional materials using van der Waals density functional. Phys Rev B. 2018;98(1):014107.
40	Coley CW, Barzilay R, Green WH, Jaakkola TS, Jensen KF. Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Model. 2017;57(8):1757-1772.
41	Faber FA, Hutchison L, Huang B, et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. J Chem Theor Comput. 2017;13(11):5255-5264.
42	Chen S, Wulamu A, Zou Q, et al. MD-GNN: a mechanism-data-driven graph neural network for molecular properties prediction and new material discovery. J Mol Graph Model. 2023;123:108506.
43	Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des. 2016;30(8):595-608.
44	Li Y, Tarlow D, Brockschmidt M, Zemel R. Gated Graph Sequence Neural Networks; 2015. arXiv:1511.05493.
45	Aouichaoui A, Fan F, Mansouri SS, Abildskov J, Sin G. Combining group-contribution concept and graph neural networks toward interpretable molecular property models. J Chem Inf Model. 2023;63(3):725-744.
46	Kim Y, Jeong Y, Kim J, Lee EK, Kim WJ, Choi IS. MolNet: a chemically intuitive graph neural network for prediction of molecular properties. Asian Chemistry. 2022;17(16):e202200269.
47	Gautschi G. Piezoelectric Sensorics. Springer;2002:73-91.
48	Nalwa HS. Handbook of Low and High Dielectric Constant Materials and Their Applications, Two-Volume Set. Elsevier;1999.
49	Uchino K. Piezoelectric Actuators and Ultrasonic Motors. Vol 1. Springer Science & Business Media;1996.
50	Kasap SO. Principles of Electronic Materials and Devices. Vol 2. McGraw-Hill;2006.
51	Safari A, Akdogan EK. Piezoelectric and Acoustic Materials for Transducer Applications. Springer Science & Business Media;2008.
52	Hao XJ. A review on the dielectric materials for high energy-storage application. J Adv Dielectr. 2013;3(01):1330001.
53	Kotecki DE. A review of high dielectric materials for DRAM capacitors. J Integr Ferroelectr. 1997;16(1-4):1-19.
54	Ong SP, Cholia S, Jain A, et al. The Materials application programming interface (API): a simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles. Comput Mater Sci. 2015;97:209-215.
55	Wang Y, Lv J, Zhu L, Ma Y. Crystal structure prediction via particles warm optimization. Phys Rev B. 2010;82(9):094116.
56	Yamashita T, Sato N, Kino H, Miyake T, Tsuda K, Oguchi T. Crystal structure prediction accelerated by Bayesian optimization. Phys Rev Mater. 2018;2(1):013803.
57	Sekhar MC, Veena E, Kumar NS, Naidu KCB, Mallikarjuna A, Basha DB. A review on piezoelectric materials and their applications. Cryst Res Technol. 2022;58(2).