Sparse representation for machine learning the properties of defects in 2D materials
Source abstract
Abstract
Abstract Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects. However, the search space for such structures is enormous, and ab-initio computations prohibitively expensive. We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration. The method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately. We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop. Also, our approach is an order of magnitude more resource-efficient than its contenders both for the training and inference part.
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Research context
OpenAlex topics: Machine Learning in Materials Science · 2D Materials and Applications · Electronic and Structural Properties of Oxides
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
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1. Lin, Y.-C., Torsi, R., Geohegan, D. B., Robinson, J. A. & Xiao, K. Controllable thin-film approaches for doping and alloying transition metal dichalcogenides monolayers. Adv. Sci. 8, 2004249 (2021).10.1002/advs.202004249 ↗
2. Novoselov, K. S. et al. Two-dimensional gas of massless dirac fermions in graphene. Nature 438, 197–200 (2005).10.1038/nature04233 ↗
3. Aharonovich, I., Englund, D. & Toth, M. Solid-state single-photon emitters. Nat. Photonics 10, pp.631–641 (2016).10.1038/nphoton.2016.186 ↗
4. Frey, N. C., Akinwande, D., Jariwala, D. & Shenoy, V. B. Machine learning-enabled design of point defects in 2d materials for quantum and neuromorphic information processing. ACS Nano 14, 13406–13417 (2020).10.1021/acsnano.0c05267 ↗
5. Chanussot, L. et al. Open catalyst 2020 (oc20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021).10.1021/acscatal.0c04525 ↗
6. Wang, Z. et al. Novel 2d material from amqs-based defect engineering for efficient and stable organic solar cells. 2D Mater. 6, 045017 (2019).10.1088/2053-1583/ab277d ↗
7. Bertoldo, F., Ali, S., Manti, S. & Thygesen, K. S. Quantum point defects in 2d materials-the qpod database. npj Comput. Mater. 8, 56 (2022).10.1038/s41524-022-00730-w ↗
8. Freysoldt, C. et al. First-principles calculations for point defects in solids. Rev. Mod. Phys. 86, 253 (2014).10.1103/RevModPhys.86.253 ↗
9. Smith, J. S., Isayev, O. & Roitberg, A. E. Ani-1: an extensible neural network potential with dft accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017).10.1039/C6SC05720A ↗
10. Stocker, S., Gasteiger, J., Becker, F., Günnemann, S. & Margraf, J. T. How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Mach. Learn.: Sci. Technol. 3, 045010 (2022).
11. Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 3564–3572 (2019).10.1021/acs.chemmater.9b01294 ↗
12. Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).10.1103/PhysRevLett.120.145301 ↗
13. Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. Schnet–a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).10.1063/1.5019779 ↗
14. Gasteiger, J., Becker, F. & Günnemann, S. Gemnet: Universal directional graph neural networks for molecules. Adv. Neural Inf. Process. Syst. 34, 6790–6802 (2021).
15. Huang, P. et al. Unveiling the complex structure-property correlation of defects in 2d materials based on high throughput datasets. npj 2D Mater. Appl. 7, 6 (2023).10.1038/s41699-023-00369-1 ↗
16. Park, C. W. & Wolverton, C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys. Rev. Mater. 4, 063801 (2020).10.1103/PhysRevMaterials.4.063801 ↗
17. Klicpera, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. Published at the International Conference on Learning Representations (ICLR) 2020. Preprint at https://arXiv.org/abs/2003.03123 (2020).
18. Shuaibi, M. et al. Rotation invariant graph neural networks using spin convolutions. Preprint at https://arXiv.org/abs/2106.09575 (2021).
19. Choudhary, K. & DeCost, B. Atomistic line graph neural network for improved materials property predictions. npj Comput. Mater. 7, 1–8 (2021).10.1038/s41524-021-00650-1 ↗
20. Ying, C. et al. Do transformers really perform badly for graph representation? Adv. Neural Inform. Process. Syst. 34, 28877–28888 (2021).
21. Shi, Y. et al. Benchmarking graphormer on large-scale molecular modeling datasets. Preprint at https://arXiv.org/abs/2203.04810 (2022).
22. Sun, Q. & Chan, G. K.-L. Quantum embedding theories. Acc. Chem. Res. 49, 2705–2712 (2016).10.1021/acs.accounts.6b00356 ↗
23. Huang, P. et al. Carbon and vacancy centers in hexagonal boron nitride. Phys. Rev. B 106, 014107 (2022).10.1103/PhysRevB.106.014107 ↗
24. Deml, A. M., Holder, A. M., O’Hayre, R. P., Musgrave, C. B. & Stevanović, V. Intrinsic material properties dictating oxygen vacancy formation energetics in metal oxides. J. Phys. Chem. Lett. 6, 1948–1953 (2015).10.1021/acs.jpclett.5b00710 ↗
25. Choudhary, K. & Sumpter, B. G. A deep-learning model for fast prediction of vacancy formation in diverse materials. Preprint at https://arXiv.org/abs/2205.08366 (2022).
26. Wexler, R. B., Gautam, G. S., Stechel, E. B. & Carter, E. A. Factors governing oxygen vacancy formation in oxide perovskites. J. Am. Chem. Soc. 143, 13212–13227 (2021).10.1021/jacs.1c05570 ↗
27. Witman, M., Goyal, A., Ogitsu, T., McDaniel, A. & Lany, S. Materials discovery for high-temperature, clean-energy applications using graph neural network models of vacancy defects and free-energy calculations. Preprint at https://chemrxiv.org/engage/chemrxiv/article-details/63b7181c1f24031e9a1789e0.
28. Ward, L. et al. Matminer: An open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60–69 (2018).10.1016/j.commatsci.2018.05.018 ↗
29. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).10.1023/A:1010933404324 ↗
30. Manzoor, A. et al. Machine learning based methodology to predict point defect energies in multi-principal element alloys. Front. Mater. 8 https://www.frontiersin.org/article/10.3389/fmats.2021.673574 (2021).10.3389/fmats.2021.673574 ↗
31. Ostadhossein, A. et al. Reaxff reactive force-field study of molybdenum disulfide (MoS2). J. Phys. Chem. Lett. 8, 631–640 (2017).10.1021/acs.jpclett.6b02902 ↗
32. Patra, T. K. et al. Defect dynamics in 2-d MoS2 probed by using machine learning, atomistic simulations, and high-resolution microscopy. ACS Nano 12, 8006–8016 (2018).10.1021/acsnano.8b02844 ↗
33. Banik, S. et al. Learning with delayed rewards–A case study on inverse defect design in 2d materials. ACS Appl. Mater. Interfaces 13, 36455–36464 (2021).10.1021/acsami.1c07545 ↗
34. Shytov, A. V., Abanin, D. A. & Levitov, L. S. Long-range interaction between adatoms in graphene. Phys. Rev. Lett. 103, 016806 (2009).10.1103/PhysRevLett.103.016806 ↗
35. Ong, S. P. et al. Python materials genomics (pymatgen): A robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).10.1016/j.commatsci.2012.10.028 ↗
36. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. Adv. Neural Inform. Process. Syst. 31, 6639–6649 (2018).
37. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).10.1103/PhysRevLett.77.3865 ↗
38. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169 (1996).10.1103/PhysRevB.54.11169 ↗
39. Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).10.1016/0927-0256(96)00008-0 ↗
40. Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953 (1994).10.1103/PhysRevB.50.17953 ↗
41. Battaglia, P. W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at https://research.google/pubs/pub47094/ (2018).
42. Kostenetskiy, P. S., Chulkevich, R. A. & Kozyrev, V. I. HPC resources of the higher school of economics. J. Phys.: Conf. Ser. 1740, 012050 (2021).
43. Krivovichev, S. V. Structural complexity of minerals: information storage and processing in the mineral world. Mineral. Mag. 77, 275–326 (2013).10.1180/minmag.2013.077.3.05 ↗
44. Ong, S. P. et al. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).10.1016/j.commatsci.2012.10.028 ↗
45. Lam Pham, T. et al. Machine learning reveals orbital interaction in materials. Sci. Technol. Adv. Mater. 18, 756–765 (2017).10.1080/14686996.2017.1378060 ↗
46. Choudhary, K., DeCost, B. & Tavazza, F. Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape. Phys. Rev. Mater. 2, 083801 (2018).10.1103/PhysRevMaterials.2.083801 ↗