| SPECIAL TOPIC — AI + Physical Science |
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Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics |
| Mei Ma(马梅)1, Wei Ma(马薇)2, Le Gao(高乐)3, Zong-Guo Wang(王宗国)4, and Hao Liu(刘昊)5,† |
1 School of Physics, Ningxia University, Yinchuan 750021, China;
2 School of Materials and New Energy, Ningxia University, Yinchuan 750021, China;
3 School of Information Engineering, Ningxia University, Yinchuan 750021, China;
4 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China;
5 School of Artificial Intelligence, Ningxia University, Yinchuan 750021, China |
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Abstract Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates, substantially reducing the costs associated with extensive experimental trial-and-error processes. However, existing methods, limited by static structural descriptors such as chemical composition and lattice parameters, fail to account for atomic vibrations, which may introduce spurious correlations and undermine predictive reliability. Here, we propose a deep learning model termed integrating graph and dynamical stability (IGDS) for predicting the synthesizability of inorganic crystals. IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties. Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems, achieving precision and recall values of 0.916/0.863 for ternary compounds. By capturing both static structural descriptors and dynamic features, IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals. This approach bridges the gap between theoretical design concepts and their practical implementation, thereby streamlining the development cycle of new materials and enhancing overall research efficiency
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Received: 17 June 2025
Revised: 20 August 2025
Accepted manuscript online: 02 September 2025
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PACS:
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61.50.Ah
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(Theory of crystal structure, crystal symmetry; calculations and modeling)
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63.20.D-
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(Phonon states and bands, normal modes, and phonon dispersion)
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81.05.Zx
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(New materials: theory, design, and fabrication)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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Corresponding Authors:
Hao Liu
E-mail: liuhao@nxu.edu.cn
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Cite this article:
Mei Ma(马梅), Wei Ma(马薇), Le Gao(高乐), Zong-Guo Wang(王宗国), and Hao Liu(刘昊) Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics 2026 Chin. Phys. B 35 016101
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[1] Nzereogu P U, Omah A D, Ezema F I, Iwuoha E I and Nwanya A C 2022 Appl. Surf. Sci. Adv. 9 100233
[2] Li X Y, Weng S T and Lin G 2020 Chin. Phys. B 29 028801
[3] Ding K, Yang T, Leung M T, Yang K, Cheng H, Zeng M, Li B and Yang M 2023 Curr. Opin. Electrochem. 42 101404
[4] Jo B, Chen W and Jung H S 2025 J. Energy Chem. 101 298
[5] Luo Y, Bag S, Zaremba O, Cierpka A, Andreo J, Wuttke S, Friederich P and Tsotsalas M 2022 Angew. Chem. Int. Ed. 61 e202200242
[6] Therrien F, Jones E B and Stevanovic V 2021 Appl. Phys. Rev. 8 031310
[7] Bartel C J, Millican S L, Deml A M, Rumptz J R, Tumas W, Weimer A W, Lany S, Stevanovic V, Musgrave C B and Holder A M 2018 Nat. Commun. 9 4168
[8] Chen J, Calvin J, Asplund M, King S W, Woodfield B F and Navrotsky A 2019 J. Chem. Thermodyn. 128 320
[9] Sun W, Dacek S T, Ong S P, Hautier G, Jain A, Richards W D, Gamst A C, Persson K A and Ceder G 2016 Sci. Adv. 2 e1600225
[10] Aykol M, Dwaraknath S S, Sun W and Persson K A 2018 Sci. Adv. 4 eaaq0148
[11] Antoniuk E R, Cheon G, Wang G, Bernstein D, Cai W and Reed E J 2023 npj Comput. Mater. 9 155
[12] Davariashtiyani A, Kadkhodaie Z and Kadkhodaei S 2021 Commun. Mater. 2 115
[13] Zhu R, Tian S I P, Ren Z, Li J, Buonassisi T and Hippalgaonkar K 2023 ACS Omega 8 8210
[14] Xie T and Grossman J C 2018 Phys. Rev. Lett. 120 145301
[15] Jang J, Gu G H, Noh J, Kim J and Jung Y 2020 J. Am. Chem. Soc. 142 18836
[16] Kittel C and McEuen P 2018 Introduction to Solid State Physics (8th edn.) (Hoboken: John Wiley & Sons)
[17] Tolborg K, Klarbring J, Ganose A M and Walsh A 2022 Digit. Discov. 1 586
[18] Jain A, Ong S P, Hautier G, Chen W, Richards W D, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G and Persson K A 2013 APL Mater. 1 011002
[19] Schutt K T, Arbabzadah F, Chmiela S, M uller K R and Tkatchenko A 2017 Nat. Commun. 8 13890
[20] Rupp M, Tkatchenko A, Muller K R and von Lilienfeld O A 2012 Phys. Rev. Lett. 108 058301
[21] Gorfman S 2020 Acta Crystallogr. A: Found. Adv. 76 713
[22] Choudhary K and DeCost B 2021 npj Comput. Mater. 7 185
[23] Stoffel R P, Wessel C, Lumey M W and Dronskowski R 2010 Angew. Chem. Int. Ed. 49 5242
[24] Wallace D C and Callen H 1972 Am. J. Phys. 40 1718
[25] Giustino F 2017 Rev. Mod. Phys. 89 015003
[26] Batatia I, Benner P, Chiang Y, et al. 2023 arXiv:2401.00096 [physics.chem-ph]
[27] Batatia I, Kovacs D P, Simm G, Ortner C and Csanyi G 2022 Advances in Neural Information Processing Systems, November 28–December 9, 2022 New Orleans, USA, pp. 11423
[28] Deng B, Zhong P, Jun K, Riebesell J, Han K, Bartel C J and Ceder G 2023 Nat. Mach. Intell. 5 1031
[29] Togo A, Chaput L, Tadano T and Tanaka I 2023 J. Phys.: Condens. Matter 35 353001
[30] Togo A 2022 J. Phys. Soc. Jpn. 92 012001
[31] Kingma D P and Ba J 2015 International Conference on Learning Representations, May 7–9, 2015 San Diego, USA, pp. 13
[32] Loshchilov I and Hutter F 2019 International Conference on Learning Representations, May 6–9, 2019 New Orleans, USA, pp. 11254–11263
[33] Smith L N and Topin N 2018 International Conference on Learning Representations Workshop, April 30–May 3, 2018 Vancouver, Canada, (Workshop paper)
[34] Chen C, Ye W, Zuo Y, Zheng C and Ong S P 2019 Chem. Mater. 31 3564
[35] Hellenbrandt M 2004 Crystallogr. Rev. 10 17
[36] Scheirer W J, Jain L P and Boult T E 2014 IEEE Trans. Pattern Anal. Mach. Intell. 36 2317
[37] Yan K, Liu Y, Lin Y and Ji S 2022 Advances in Neural Information Processing Systems, November 28–December 9, 2022 New Orleans, USA, p. 15066
[38] Choudhary K, Garrity K F, Reid A C E, et al. 2020 npj Comput. Mater. 6 173
[39] Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z, Kononova O, Persson K A, Ceder G and Jain A 2019 Nature 571 95
[40] Wei F, Deng Z, Sun S, Hartono N T, Seng H, Buonassisi T, Bristowe P and Cheetham A 2019 Chem. Commun. 55 3721
[41] Volonakis G, Haghighirad A A, Milot R L, Sio W H, Filip M R, Wenger B, Johnston M B, Herz L M, Snaith H J and Giustino F 2017 J. Phys. Chem. Lett. 8 772
[42] Kegler V D and Fiscehr S C 2018 Int. J. Adv. Eng. Res. Sci. 5 349
[43] Qi J, Ko T W, Wood B C, Pham T A and Ong S P 2024 npj Comput. Mater. 10 43
[44] Setyawan W and Curtarolo S 2010 Comput. Mater. Sci. 49 299 |
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