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REFERENCES
1. Sha W, Guo Y, Yuan Q, et al. Artificial intelligence to power the future of materials science and engineering. Adv Intell Syst
2020;2:1900143. DOI
2. Choudhary K, DeCost B, Chen C, et al. Recent advances and applications of deep learning methods in materials science. npj Comput
Mater 2022;8:59. DOI
3. Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett
2007;98:146401. DOI
4. Schütt KT, Kindermans PJ, Sauceda HE, Chmiela S, Tkatchenko A, Müller KR. SchNet: a continuous-filter convolutional neural network
for modeling quantum interactions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems.
Curran Associates Inc.; 2017. pp. 992-1002. Available from: https://dl.acm.org/doi/abs/10.5555/3294771.3294866. [Last accessed on 28
Mar 2024]
5. Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys
Rev Lett 2018;120:145301. DOI
6. Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. npj Comput Mater
2021;7:185. DOI
7. 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;31:3564–72. DOI
8. Zuo Y, Qin M, Chen C, et al. Accelerating materials discovery with Bayesian optimization and graph deep learning. Mater Today
2021;51:126–35. DOI
9. Conway BE, Tilak BV. Interfacial processes involving electrocatalytic evolution and oxidation of H 2, and the role of chemisorbed H.
Electrochim Acta 2002;47:3571–94. DOI
10. Loffreda D. Theoretical insight of adsorption thermodynamics of multifunctional molecules on metal surfaces. Surf Sci 2006;600:2103–
12. DOI
11. Thomas N, Smidt T, Kearnes S, et al. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds.
arXiv. [Preprint] 18 May 2018 [accessed on 2024 Mar 28]. Available from: https://arxiv.org/abs/1802.08219.
12. Batzner S, Musaelian A, Sun L, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat
Commun 2022;13:2453. DOI
13. Brandstetter J, Hesselink R, van der Pol E, Bekkers EJ, Welling M. Geometric and physical quantities improve E(3) equivariant message
passing. arXiv. [Preprint] 26 Mar 2022 [accessed on 2024 Mar 28]. Available from: https://arxiv.org/abs/2110.02905.
14. Batatia I, Kovács DP, Simm GNC, Ortner C, Csányi G. MACE: higher order equivariant message passing neural networks for fast and
accurate force fields. arXiv. [Preprint] 26 Jan 2023 [accessed on 2024 Mar 28]. Available from: https://arxiv.org/abs/2206.07697.
15. Musaelian A, Batzner S, Johansson A, et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat Commun
2023;14:579. DOI
16. Liao YL, Smidt T. Equiformer: equivariant graph attention transformer for 3D atomistic graphs. arXiv. [Preprint] 28 Feb 2023 [accessed
on 2024 Mar 28]. Available from: https://arxiv.org/abs/2206.11990.
17. Zitnick CL, Das A, Kolluru A, et al. Spherical channels for modeling atomic interactions. arXiv. [Preprint] 13 Oct 2022 [accessed on
2024 Mar 28]. Available from: https://arxiv.org/abs/2206.14331.
18. Chanussot L, Das A, Goyal S, et al. Open catalyst 2020 (OC20) dataset and community challenges. ACS Catal 2021;11:6059–72. DOI
19. Passaro S, Zitnick CL. Reducing SO(3) convolutions to SO(2) for efficient equivariant GNNs. arXiv. [Preprint] 14 Jun 2023 [accessed
on 2024 Mar 28]. Available from: https://arxiv.org/abs/2302.03655.
20. Lan J, Palizhati A, Shuaibi M, et al. AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine
learning potentials. npj Comput Mater 2023;9:172. DOI
21. Wang Z, Wang C, Zhao S, et al. Heterogeneous relational message passing networks for molecular dynamics simulations. npj Comput
Mater 2022;8:53. DOI
22. Zhong Y, Yu H, Su M, Gong X, Xiang H. Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids.
npj Comput Mater 2023;9:182. DOI
23. Al Zoubi W, Assfour B, Allaf AW, Leoni S, Kang JH, Ko YG. Experimental and theoretical investigation of high-entropy-alloy/support
as a catalyst for reduction reactions. J Energy Chem 2023;81:132–42. DOI
24. Liu W, Tkatchenko A, Scheffler M. Modeling adsorption and reactions of organic molecules at metal surfaces. Acc Chem Res
2014;47:3369–77. DOI
25. Shee J, Rudshteyn B, Arthur EJ, Zhang S, Reichman DR, Friesner RA. On achieving high accuracy in quantum chemical calculations
of 3d transition metal-containing systems: a comparison of auxiliary-field quantum monte carlo with coupled cluster, density functional
theory, and experiment for diatomic molecules. J Chem Theory Comput 2019;15:2346–58. DOI
26. Ghanekar PG, Deshpande S, Greeley J. Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis.
Nat Commun 2022;13:5788. DOI