Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

The amorphous state as a frontier in computational materials design

Nature Reviews Materials volume 10, pages 228–241 (2025)Cite this article

Subjects

Abstract

One of the grand challenges in the physical sciences is to ‘design’ a material before it is ever synthesized. There has been fast progress in predicting new solid-state compounds with the help of quantum-mechanical computations and supervised machine learning, and yet such progress has largely been limited to materials with ordered crystal structures. In this Perspective, we argue that the computational design of entirely non-crystalline, amorphous solids is an emerging and rewarding frontier in materials research. We show how recent advances in computational modelling and artificial intelligence can provide the previously missing links among atomic-scale structure, microscopic properties and macroscopic functionality of amorphous solids. Accordingly, we argue that the combination of physics-based modelling and artificial intelligence is now bringing amorphous functional materials ‘by design’ within reach. We discuss new implications for laboratory synthesis, and we outline our vision for the development of the field in the years ahead.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Amorphous materials for emerging technologies.
Fig. 2: Simulating structures of amorphous materials.
Fig. 3: Predicting properties of amorphous materials.
Fig. 4: A closed-loop perspective for amorphous materials design.

Similar content being viewed by others

References

  1. Nayak, P. K., Mahesh, S., Snaith, H. J. & Cahen, D. Photovoltaic solar cell technologies: analysing the state of the art. Nat. Rev. Mater. 4, 269–285 (2019).

    Article  CAS  Google Scholar 

  2. Kuzum, D., Jeyasingh, R. G. D., Lee, B. & Wong, H.-S. P. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12, 2179–2186 (2012).

    Article  CAS  PubMed  Google Scholar 

  3. van de Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. & Salleo, A. Organic electronics for neuromorphic computing. Nat. Electron. 1, 386–397 (2018).

    Article  Google Scholar 

  4. Zhang, W., Mazzarello, R., Wuttig, M. & Ma, E. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing. Nat. Rev. Mater. 4, 150–168 (2019).

    Article  CAS  Google Scholar 

  5. Mannsfeld, S. C. B. et al. Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers. Nat. Mater. 9, 859–864 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Laurila, T., Sainio, S. & Caro, M. A. Hybrid carbon based nanomaterials for electrochemical detection of biomolecules. Prog. Mater. Sci. 88, 499–594 (2017).

    Article  CAS  Google Scholar 

  7. Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V. & Pernice, W. H. P. The rise of intelligent matter. Nature 594, 345–355 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Keen, D. A. & Goodwin, A. L. The crystallography of correlated disorder. Nature 521, 303–309 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Simonov, A. & Goodwin, A. L. Designing disorder into crystalline materials. Nat. Rev. Chem. 4, 657–673 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Elliott, S. R. Medium-range structural order in covalent amorphous solids. Nature 354, 445–452 (1991).

    Article  CAS  Google Scholar 

  11. Wright, A. C. The great crystallite versus random network controversy: a personal perspective. Int. J. Appl. Glass Sci. 5, 31–56 (2014).

    Article  Google Scholar 

  12. Tanaka, H., Tong, H., Shi, R. & Russo, J. Revealing key structural features hidden in liquids and glasses. Nat. Rev. Phys. 1, 333–348 (2019).

    Article  Google Scholar 

  13. Savoie, B. M. et al. Mesoscale molecular network formation in amorphous organic materials. Proc. Natl Acad. Sci. USA 111, 10055–10060 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kim, S., Agarwala, A. & Chowdhury, D. Fractionalization and topology in amorphous electronic solids. Phys. Rev. Lett. 130, 026202 (2023).

    Article  CAS  PubMed  Google Scholar 

  15. Nomura, K. et al. Room-temperature fabrication of transparent flexible thin-film transistors using amorphous oxide semiconductors. Nature 432, 488–492 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Smith, R. D. L. et al. Photochemical route for accessing amorphous metal oxide materials for water oxidation catalysis. Science 340, 60–63 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Han, F. et al. High electronic conductivity as the origin of lithium dendrite formation within solid electrolytes. Nat. Energy 4, 187–196 (2019).

    Article  CAS  Google Scholar 

  18. Hong, S. et al. Ultralow-dielectric-constant amorphous boron nitride. Nature 582, 511–514 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Heo, J. et al. Amorphous iron fluorosulfate as a high-capacity cathode utilizing combined intercalation and conversion reactions with unexpectedly high reversibility. Nat. Energy 8, 30–39 (2022).

    Article  Google Scholar 

  20. Hautier, G., Jain, A. & Ong, S. P. From the computer to the laboratory: materials discovery and design using first-principles calculations. J. Mater. Sci. 47, 7317–7340 (2012).

    Article  CAS  Google Scholar 

  21. Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 65, 1501–1509 (2013).

    Article  CAS  Google Scholar 

  22. Butler, T. et al. Computational materials design of crystalline solids. Chem. Soc. Rev. 45, 6138–6146 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jain, A., Shin, Y. & Persson, K. A. Computational predictions of energy materials using density functional theory. Nat. Rev. Mater. 1, 15004 (2016).

    Article  CAS  Google Scholar 

  24. Gorai, P., Stevanović, V. & Toberer, E. S. Computationally guided discovery of thermoelectric materials. Nat. Rev. Mater. 2, 17053 (2017).

    Article  CAS  Google Scholar 

  25. Therrien, F., Jones, E. B. & Stevanović, V. Metastable materials discovery in the age of large-scale computation. Appl. Phys. Rev. 8, 031310 (2021).

    Article  CAS  Google Scholar 

  26. Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).

    Article  CAS  PubMed  Google Scholar 

  29. Miao, J., Ercius, P. & Billinge, S. J. L. Atomic electron tomography: 3D structures without crystals. Science 353, aaf2157 (2016).

    Article  PubMed  Google Scholar 

  30. Yang, Y. et al. Determining the three-dimensional atomic structure of an amorphous solid. Nature 592, 60–64 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Yuan, Y. et al. Three-dimensional atomic packing in amorphous solids with liquid-like structure. Nat. Mater. 21, 95–102 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Chang, C., Deringer, V. L., Katti, K. S., Van Speybroeck, V. & Wolverton, C. M. Simulations in the era of exascale computing. Nat. Rev. Mater. 8, 309–313 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Erhard, L. C., Rohrer, J., Albe, K. & Deringer, V. L. Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning. Nat. Commun. 15, 1927 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yabuuchi, N., Kubota, K., Dahbi, M. & Komaba, S. Research development on sodium-ion batteries. Chem. Rev. 114, 11636–11682 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Janek, J. & Zeier, W. G. A solid future for battery development. Nat. Energy 1, 16141 (2016).

    Article  Google Scholar 

  36. Choi, J. W. & Aurbach, D. Promise and reality of post-lithium-ion batteries with high energy densities. Nat. Rev. Mater. 1, 16013 (2016).

    Article  CAS  Google Scholar 

  37. Li, M., Lu, J., Chen, Z. & Amine, K. 30 years of lithium-ion batteries. Adv. Mater. 30, 1800561 (2018).

    Article  Google Scholar 

  38. Vaalma, C., Buchholz, D., Weil, M. & Passerini, S. A cost and resource analysis of sodium-ion batteries. Nat. Rev. Mater. 3, 18013 (2018).

    Article  Google Scholar 

  39. Nayak, P. K., Yang, L., Brehm, W. & Adelhelm, P. From lithium-ion to sodium-ion batteries: advantages, challenges, and surprises. Angew. Chem. Int. Ed. 57, 102–120 (2018).

    Article  CAS  Google Scholar 

  40. Grey, C. P. & Hall, D. S. Prospects for lithium-ion batteries and beyond — a 2030 vision. Nat. Commun. 11, 6279 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Guo, T., Hu, P., Li, L., Wang, Z. & Guo, L. Amorphous materials emerging as prospective electrodes for electrochemical energy storage and conversion. Chem 9, 1080–1093 (2023).

    Article  CAS  Google Scholar 

  42. Jiang, Y. et al. Amorphous Fe2O3 as a high-capacity, high-rate and long-life anode material for lithium ion batteries. Nano Energy 4, 23–30 (2014).

    Article  CAS  Google Scholar 

  43. Lin, L., Xu, X., Chu, C., Majeed, M. K. & Yang, J. Mesoporous amorphous silicon: a simple synthesis of a high-rate and long-life anode material for lithium-ion batteries. Angew. Chem. Int. Ed. 128, 14269–14272 (2016).

    Article  Google Scholar 

  44. Ding, J., Ji, D., Yue, Y. & Smedskjaer, M. M. Amorphous materials for lithium-ion and post-lithium-ion batteries. Small 20, 2304270 (2024).

    Article  CAS  Google Scholar 

  45. Wang, X. et al. Glassy Li metal anode for high-performance rechargeable Li batteries. Nat. Mater. 19, 1339–1345 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Stevens, D. A. & Dahn, J. R. High capacity anode materials for rechargeable sodium‐ion batteries. J. Electrochem. Soc. 147, 1271 (2000).

    Article  CAS  Google Scholar 

  47. Stevens, D. A. & Dahn, J. R. The mechanisms of lithium and sodium insertion in carbon materials. J. Electrochem. Soc. 148, A803 (2001).

    Article  CAS  Google Scholar 

  48. Zhao, R., Sun, N. & Xu, B. Recent advances in heterostructured carbon materials as anodes for sodium-ion batteries. Small Struct. 2, 2100132 (2021).

    Article  CAS  Google Scholar 

  49. Kudu, Ö. U. et al. A review of structural properties and synthesis methods of solid electrolyte materials in the Li2S−P2S5 binary system. J. Power Sources 407, 31–43 (2018).

    Article  CAS  Google Scholar 

  50. Hu, Y. et al. Superionic amorphous NaTaCl6 halide electrolyte for highly reversible all-solid-state Na-ion batteries. Matter 7, 1018–1034 (2024).

    Article  CAS  Google Scholar 

  51. Ridley, P. et al. Amorphous and nanocrystalline halide solid electrolytes with enhanced sodium-ion conductivity. Matter 7, 485–499 (2024).

    Article  CAS  Google Scholar 

  52. Wuttig, M. & Yamada, N. Phase-change materials for rewriteable data storage. Nat. Mater. 6, 824–832 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Akola, J. & Jones, R. O. Structure of amorphous Ge8Sb2Te11: GeTe-Sb2Te3 alloys and optical storage. Phys. Rev. B 79, 134118 (2009).

    Article  Google Scholar 

  54. Kang, D.-H., Young Kim, N., Jeong, H. & Cheong, B. Understanding on the current-induced crystallization process and faster set write operation thereof in non-volatile phase change memory. Appl. Phys. Lett. 100, 063508 (2012).

    Article  Google Scholar 

  55. Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768–775 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Rao, F. et al. Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing. Science 358, 1423–1427 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Liu, B. et al. Y-doped Sb2Te3 phase-change materials: toward a universal memory. ACS Appl. Mater. Interfaces 12, 20672–20679 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Zhang, Y. et al. Characteristics of Si-doped Sb2Te3 thin films for phase-change random access memory. Appl. Surf. Sci. 254, 5602–5606 (2008).

    Article  CAS  Google Scholar 

  59. Yang, J., Wang, D., Han, H. & Li, C. Roles of cocatalysts in photocatalysis and photoelectrocatalysis. Acc. Chem. Res. 46, 1900–1909 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Zhang, L. et al. Photoelectrocatalytic arene C–H amination. Nat. Catal. 2, 366–373 (2019).

    Article  CAS  Google Scholar 

  61. Shan, B. et al. Binary molecular-semiconductor p–n junctions for photoelectrocatalytic CO2 reduction. Nat. Energy 4, 290–299 (2019).

    Article  CAS  Google Scholar 

  62. Wang, B. M., Biesold, G., Zhang, M. & Lin, Z. Amorphous inorganic semiconductors for the development of solar cell, photoelectrocatalytic and photocatalytic applications. Chem. Soc. Rev. 50, 6914–6949 (2021).

    Article  CAS  PubMed  Google Scholar 

  63. Anantharaj, S. & Noda, S. Amorphous catalysts and electrochemical water splitting: an untold story of harmony. Small 16, 1905779 (2020).

    Article  CAS  Google Scholar 

  64. Chemelewski, W. D., Lee, H.-C., Lin, J.-F., Bard, A. J. & Mullins, C. B. Amorphous FeOOH oxygen evolution reaction catalyst for photoelectrochemical water splitting. J. Am. Chem. Soc. 136, 2843–2850 (2014).

    Article  CAS  PubMed  Google Scholar 

  65. Duan, Y. et al. Scaled-up synthesis of amorphous NiFeMo oxides and their rapid surface reconstruction for superior oxygen evolution catalysis. Angew. Chem. Int. Ed. 58, 15772–15777 (2019).

    Article  CAS  Google Scholar 

  66. Morales-Guio, C. G., Tilley, S. D., Vrubel, H., Grätzel, M. & Hu, X. Hydrogen evolution from a copper(I) oxide photocathode coated with an amorphous molybdenum sulphide catalyst. Nat. Commun. 5, 3059 (2014).

    Article  PubMed  Google Scholar 

  67. Wu, L. et al. The origin of high activity of amorphous MoS2 in the hydrogen evolution reaction. ChemSusChem 12, 4383–4389 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Yu, L. et al. Amorphous NiFe layered double hydroxide nanosheets decorated on 3D nickel phosphide nanoarrays: a hierarchical core–shell electrocatalyst for efficient oxygen evolution. J. Mater. Chem. A 6, 13619–13623 (2018).

    Article  CAS  Google Scholar 

  69. Hu, Y. et al. Single Ru atoms stabilized by hybrid amorphous/crystalline FeCoNi layered double hydroxide for ultraefficient oxygen evolution. Adv. Energy Mater. 11, 2002816 (2021).

    Article  CAS  Google Scholar 

  70. Tang, C. W. & VanSlyke, S. A. Organic electroluminescent diodes. Appl. Phys. Lett. 51, 913–915 (1987).

    Article  CAS  Google Scholar 

  71. Noguchi, Y., Tanaka, Y., Ishii, H. & Brütting, W. Understanding spontaneous orientation polarization of amorphous organic semiconducting films and its application to devices. Synth. Met. 288, 117101 (2022).

    Article  CAS  Google Scholar 

  72. Ito, E. et al. Spontaneous buildup of giant surface potential by vacuum deposition of Alq3 and its removal by visible light irradiation. J. Appl. Phys. 92, 7306–7310 (2002).

    Article  CAS  Google Scholar 

  73. Tanaka, M., Auffray, M., Nakanotani, H. & Adachi, C. Spontaneous formation of metastable orientation with well-organized permanent dipole moment in organic glassy films. Nat. Mater. 21, 819–825 (2022).

    Article  CAS  PubMed  Google Scholar 

  74. Street, R. A. Thin-film transistors. Adv. Mater. 21, 2007–2022 (2009).

    Article  CAS  Google Scholar 

  75. Nolas, G. S. & Goldsmid, H. J. The figure of merit in amorphous thermoelectrics. Phys. Stat. Sol. 194, 271–276 (2002).

    Article  CAS  Google Scholar 

  76. Liang, H. et al. Flexible X-ray detectors based on amorphous Ga2O3 thin films. ACS Photon. 6, 351–359 (2019).

    Article  CAS  Google Scholar 

  77. Clarke, D. R. & Phillpot, S. R. Thermal barrier coating materials. Mater. Today 8, 22–29 (2005).

    Article  CAS  Google Scholar 

  78. Croissant, J. G., Fatieiev, Y., Almalik, A. & Khashab, N. M. Mesoporous silica and organosilica nanoparticles: physical chemistry, biosafety, delivery strategies, and biomedical applications. Adv. Healthc. Mater. 7, 1700831 (2018).

    Article  Google Scholar 

  79. He, S. et al. Semiconductor glass with superior flexibility and high room temperature thermoelectric performance. Sci. Adv. 6, eaaz8423 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Fu, Y. et al. Superflexible inorganic Ag2Te0.6S0.4 fiber with high thermoelectric performance. Adv. Sci. 10, 2207642 (2023).

    Article  CAS  Google Scholar 

  81. Croissant, J. G., Butler, K. S., Zink, J. I. & Brinker, C. J. Synthetic amorphous silica nanoparticles: toxicity, biomedical and environmental implications. Nat. Rev. Mater. 5, 886–909 (2020).

    Article  CAS  Google Scholar 

  82. Toh, C.-T. et al. Synthesis and properties of free-standing monolayer amorphous carbon. Nature 577, 199–203 (2020).

    Article  CAS  PubMed  Google Scholar 

  83. Tian, H. et al. Disorder-tuned conductivity in amorphous monolayer carbon. Nature 615, 56–61 (2023).

    Article  CAS  PubMed  Google Scholar 

  84. Wang, W. H., Dong, C. & Shek, C. H. Bulk metallic glasses. Mater. Sci. Eng. R. Rep. 44, 45–89 (2004).

    Article  Google Scholar 

  85. Štich, I., Car, R. & Parrinello, M. Amorphous silicon studied by ab initio molecular dynamics: preparation, structure, and properties. Phys. Rev. B 44, 11092–11104 (1991).

    Article  Google Scholar 

  86. McCulloch, D. G., McKenzie, D. R. & Goringe, C. M. Ab initio simulations of the structure of amorphous carbon. Phys. Rev. B 61, 2349–2355 (2000).

    Article  CAS  Google Scholar 

  87. Akola, J. & Jones, R. O. Structural phase transitions on the nanoscale: the crucial pattern in the phase-change materials Ge2Sb2Te5 and GeTe. Phys. Rev. B 76, 235201 (2007).

    Article  Google Scholar 

  88. Hegedüs, J. & Elliott, S. R. Microscopic origin of the fast crystallization ability of Ge–Sb–Te phase-change memory materials. Nat. Mater. 7, 399–405 (2008).

    Article  PubMed  Google Scholar 

  89. Aykol, M., Dwaraknath, S. S., Sun, W. & Persson, K. A. Thermodynamic limit for synthesis of metastable inorganic materials. Sci. Adv. 4, eaaq0148 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Behler, J. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angew. Chem. Int. Ed. 56, 12828–12840 (2017).

    Article  CAS  Google Scholar 

  91. Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).

    Article  CAS  Google Scholar 

  92. Friederich, P., Häse, F., Proppe, J. & Aspuru-Guzik, A. Machine-learned potentials for next-generation matter simulations. Nat. Mater. 20, 750–761 (2021).

    Article  CAS  PubMed  Google Scholar 

  93. Cheng, B., Mazzola, G., Pickard, C. J. & Ceriotti, M. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature 585, 217–220 (2020).

    Article  CAS  PubMed  Google Scholar 

  94. Muhli, H. et al. Machine learning force fields based on local parametrization of dispersion interactions: application to the phase diagram of C60. Phys. Rev. B 104, 054106 (2021).

    Article  CAS  Google Scholar 

  95. Zhou, Y., Kirkpatrick, W. & Deringer, V. L. Cluster fragments in amorphous phosphorus and their evolution under pressure. Adv. Mater. 34, 2107515 (2022).

    Article  CAS  Google Scholar 

  96. Fan, Z. & Tanaka, H. Microscopic mechanisms of pressure-induced amorphous–amorphous transitions and crystallisation in silicon. Nat. Commun. 15, 368 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Zhou, Y., Zhang, W., Ma, E. & Deringer, V. L. Device-scale atomistic modelling of phase-change memory materials. Nat. Electron. 6, 746–754 (2023).

    Article  CAS  Google Scholar 

  98. Deringer, V. L. & Csányi, G. Machine learning based interatomic potential for amorphous carbon. Phys. Rev. B 95, 094203 (2017).

    Article  Google Scholar 

  99. Sosso, G. C., Miceli, G., Caravati, S., Behler, J. & Bernasconi, M. Neural network interatomic potential for the phase change material GeTe. Phys. Rev. B 85, 174103 (2012).

    Article  Google Scholar 

  100. Mocanu, F. C. et al. Modeling the phase-change memory material, Ge2Sb2Te5, with a machine-learned interatomic potential. J. Phys. Chem. B 122, 8998–9006 (2018).

    Article  CAS  PubMed  Google Scholar 

  101. Deringer, V. L. et al. Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics. J. Phys. Chem. Lett. 9, 2879–2885 (2018).

    Article  CAS  PubMed  Google Scholar 

  102. Konstantinou, K., Mocanu, F. C., Lee, T.-H. & Elliott, S. R. Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5. Nat. Commun. 10, 3065 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Simoncelli, M., Mauri, F. & Marzari, N. Thermal conductivity of glasses: first-principles theory and applications. npj Comput. Mater. 9, 106 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Caro, M. A., Deringer, V. L., Koskinen, J., Laurila, T. & Csányi, G. Growth mechanism and origin of high sp3 content in tetrahedral amorphous carbon. Phys. Rev. Lett. 120, 166101 (2018).

    Article  CAS  PubMed  Google Scholar 

  105. Caro, M. A., Csányi, G., Laurila, T. & Deringer, V. L. Machine learning driven simulated deposition of carbon films: from low-density to diamond-like amorphous carbon. Phys. Rev. B 102, 174201 (2020).

    Article  CAS  Google Scholar 

  106. Choy, K. L. Chemical vapour deposition of coatings. Prog. Mater. Sci. 48, 57–170 (2003).

    Article  CAS  Google Scholar 

  107. Yang, H.-S. et al. Anomalously high thermal conductivity of amorphous Si deposited by hot-wire chemical vapor deposition. Phys. Rev. B 81, 104203 (2010).

    Article  Google Scholar 

  108. Maita, J. M., Song, G., Colby, M. & Lee, S.-W. Atomic arrangement and mechanical properties of chemical-vapor-deposited amorphous boron. Mater. Des. 193, 108856 (2020).

    Article  CAS  Google Scholar 

  109. Sun, L. et al. Chemical vapour deposition. Nat. Rev. Methods Primers 1, 5 (2021).

    Article  CAS  Google Scholar 

  110. Wang, H. et al. Efficient screening framework for organic solar cells with deep learning and ensemble learning. npj Comput. Mater. 9, 200 (2023).

    Article  CAS  Google Scholar 

  111. Basha, B. et al. Designing of novel organic semiconductors materials for organic solar cells: a machine learning assisted proficient pipeline. Inorg. Chem. Commun. 153, 110818 (2023).

    Article  CAS  Google Scholar 

  112. Yoo, P. et al. Deep learning workflow for the inverse design of molecules with specific optoelectronic properties. Sci. Rep. 13, 20031 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Kilgour, M., Gastellu, N., Hui, D. Y. T., Bengio, Y. & Simine, L. Generating multiscale amorphous molecular structures using deep learning: a study in 2D. J. Phys. Chem. Lett. 11, 8532–8537 (2020).

    Article  CAS  PubMed  Google Scholar 

  114. Kwon, H. et al. Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models. Mach. Learn. Sci. Technol. 5, 045037 (2024).

    Article  Google Scholar 

  115. Madanchi, A., Kilgour, M., Zysk, F., Kühne, T. D. & Simine, L. Simulations of disordered matter in 3D with the morphological autoregressive protocol (MAP) and convolutional neural networks. J. Chem. Phys. 160, 024101 (2024).

    Article  CAS  PubMed  Google Scholar 

  116. Barkema, G. T. & Mousseau, N. Event-based relaxation of continuous disordered systems. Phys. Rev. Lett. 77, 4358–4361 (1996).

    Article  CAS  PubMed  Google Scholar 

  117. Mousseau, N. & Barkema, G. T. Traveling through potential energy landscapes of disordered materials: the activation–relaxation technique. Phys. Rev. E 57, 2419–2424 (1998).

    Article  CAS  Google Scholar 

  118. Madanchi, A. et al. Is the future of materials amorphous? Challenges and opportunities in simulations of amorphous materials. Preprint at https://arxiv.org/abs/2410.05035 (2024).

  119. Opletal, G. et al. Hybrid approach for generating realistic amorphous carbon structure using metropolis and reverse Monte Carlo. Mol. Simul. 28, 927–938 (2002).

    Article  CAS  Google Scholar 

  120. Nicholas, T. C. et al. Geometrically frustrated interactions drive structural complexity in amorphous calcium carbonate. Nat. Chem. 16, 36–41 (2024).

    Article  CAS  PubMed  Google Scholar 

  121. Leist, C., He, M., Liu, X., Kaiser, U. & Qi, H. Deep-learning pipeline for statistical quantification of amorphous two-dimensional materials. ACS Nano 16, 20488–20496 (2022).

    Article  CAS  PubMed  Google Scholar 

  122. Zarrouk, T., Ibragimova, R., Bartók, A. P. & Caro, M. A. Experiment-driven atomistic materials modeling: a case study combining X-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon. J. Am. Chem. Soc. 146, 14645–14659 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Anker, A. S., Butler, K. T., Le, M. D., Perring, T. G. & Thiyagalingam, J. Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data. Digit. Discov. 2, 578–590 (2023).

    Article  CAS  Google Scholar 

  124. Khan, A., Lee, C.-H., Huang, P. Y. & Clark, B. K. Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images. npj Comput. Mater. 9, 85 (2023).

    Article  Google Scholar 

  125. Sosso, G. C., Donadio, D., Caravati, S., Behler, J. & Bernasconi, M. Thermal transport in phase-change materials from atomistic simulations. Phys. Rev. B 86, 104301 (2012).

    Article  Google Scholar 

  126. Aryana, K. et al. Tuning network topology and vibrational mode localization to achieve ultralow thermal conductivity in amorphous chalcogenides. Nat. Commun. 12, 2817 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Liu, Y. et al. Unraveling thermal transport correlated with atomistic structures in amorphous gallium oxide via machine learning combined with experiments. Adv. Mater. 35, 2210873 (2023).

    Article  CAS  Google Scholar 

  128. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017).

    Article  Google Scholar 

  129. Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83 (2019).

    Article  Google Scholar 

  130. Raty, J. Y. et al. Aging mechanisms in amorphous phase-change materials. Nat. Commun. 6, 7467 (2015).

    Article  CAS  PubMed  Google Scholar 

  131. Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).

    Article  PubMed  Google Scholar 

  132. Li, W., Ando, Y., Minamitani, E. & Watanabe, S. Study of Li atom diffusion in amorphous Li3PO4 with neural network potential. J. Chem. Phys. 147, 214106 (2017).

    Article  PubMed  Google Scholar 

  133. Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718–728 (2022).

    Article  PubMed  Google Scholar 

  134. Deng, B. et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat. Mach. Intell. 5, 1031–1041 (2023).

    Article  Google Scholar 

  135. Zheng, H. et al. The ab initio amorphous materials database: empowering machine learning to decode diffusivity. Preprint at https://arxiv.org/abs/2402.00177 (2024).

  136. Abou El Kheir, O., Bonati, L., Parrinello, M. & Bernasconi, M. Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential. npj Comput. Mater. 10, 33 (2024).

    Article  CAS  Google Scholar 

  137. Orava, J., Greer, A. L., Gholipour, B., Hewak, D. W. & Smith, C. E. Characterization of supercooled liquid Ge2Sb2Te5 and its crystallization by ultrafast-heating calorimetry. Nat. Mater. 11, 279–283 (2012).

    Article  CAS  PubMed  Google Scholar 

  138. Wilson, H. W. On the velocity of solidification and viscosity of super-cooled liquids. Philos. Mag. 50, 238–250 (1900).

    Article  Google Scholar 

  139. Niefind, F., Shivhare, R., Mannsfeld, S. C. B., Abel, B. & Hambsch, M. Investigating the morphology of bulk heterojunctions by laser photoemission electron microscopy. Polym. Test. 116, 107791 (2022).

    Article  CAS  Google Scholar 

  140. Yi, Y., Coropceanu, V. & Brédas, J.-L. Exciton-dissociation and charge-recombination processes in pentacene/C60 solar cells: theoretical insight into the impact of interface geometry. J. Am. Chem. Soc. 131, 15777–15783 (2009).

    Article  CAS  PubMed  Google Scholar 

  141. Vandervelden, C. A., Khan, S. A., Scott, S. L. & Peters, B. Site-averaged kinetics for catalysts on amorphous supports: an importance learning algorithm. React. Chem. Eng. 5, 77–86 (2019).

    Article  Google Scholar 

  142. Zhang, J., Hu, P. & Wang, H. Amorphous catalysis: machine learning driven high-throughput screening of superior active site for hydrogen evolution reaction. J. Phys. Chem. C 124, 10483–10494 (2020).

    Article  CAS  Google Scholar 

  143. Zhang, D. et al. Unlocking the performance of ternary metal (hydro)oxide amorphous catalysts via data-driven active-site engineering. Energy Environ. Sci. 16, 5065–5075 (2023).

    Article  Google Scholar 

  144. Zhang, X., Li, K., Wen, B., Ma, J. & Diao, D. Machine learning accelerated DFT research on platinum-modified amorphous alloy surface catalysts. Chin. Chem. Lett. 34, 107833 (2023).

    Article  CAS  Google Scholar 

  145. Noh, J. et al. Inverse design of solid-state materials via a continuous representation. Matter 1, 1370–1384 (2019).

    Article  Google Scholar 

  146. Ren, Z. et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter 5, 314–335 (2022).

    Article  CAS  Google Scholar 

  147. Ben Mahmoud, C., Gardner, J. L. A. & Deringer, V. L. Data as the next challenge in atomistic machine learning. Nat. Comput. Sci. 4, 384–387 (2024).

    Article  PubMed  Google Scholar 

  148. Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).

    Article  Google Scholar 

  149. Zakutayev, A. et al. An open experimental database for exploring inorganic materials. Sci. Data 5, 180053 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  150. Mocanu, F. C., Konstantinou, K. & Elliott, S. R. Quench-rate and size-dependent behaviour in glassy Ge2Sb2Te5 models simulated with a machine-learned Gaussian approximation potential. J. Phys. D Appl. Phys. 53, 244002 (2020).

    Article  CAS  Google Scholar 

  151. Cole, J. M. A design-to-device pipeline for data-driven materials discovery. Acc. Chem. Res. 53, 599–610 (2020).

    Article  CAS  PubMed  Google Scholar 

  152. Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. Preprint at https://arxiv.org/abs/1301.3781 (2013).

  153. Pennington, J., Socher, R. & Manning, C. GloVe: global vectors for word representation. In Proc. 2014 Conf. Empir. Methods Nat. Lang. Process. (eds Moschitti, A., Pang, B. & Daelemans, W.) 1532–1543 (Association for Computational Linguistics, 2014).

  154. Peters, M. E. et al. Deep contextualized word representations. In Proc. 2018 Conf. North Am. Chapter Assoc. Comput. Linguist. (eds Walker, M., Ji, H. & Stent, A.) 2227–2237 (ACL, 2018).

  155. Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017).

    Article  Google Scholar 

  156. Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).

    Article  CAS  PubMed  Google Scholar 

  157. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. (eds Burstein, J., Doran, C. & Solorio, T.) 4171–4186 (ACL, 2019).

  158. Brown, T. B. et al. Language models are few-shot learners. In Proc. 34th Int. Conf. Neural Inform. Process. Syst. (eds Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F. & Lin, H.) 1877–1901 (Curran Associates, 2020).

  159. Lee, J. et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234–1240 (2020).

    Article  CAS  PubMed  Google Scholar 

  160. Court, C. J. & Cole, J. M. Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning. npj Comput. Mater. 6, 18 (2020).

    Article  Google Scholar 

  161. Kononova, O. et al. Opportunities and challenges of text mining in materials research. iScience 24, 102155 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  162. Gupta, T., Zaki, M., Krishnan, N. M. A. & Mausam MatSciBERT: a materials domain language model for text mining and information extraction. npj Comput. Mater. 8, 102 (2022).

    Article  Google Scholar 

  163. Dagdelen, J. et al. Structured information extraction from scientific text with large language models. Nat. Commun. 15, 1418 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).

    Article  CAS  PubMed  Google Scholar 

  165. Chang, Y. et al. A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. 15, 39:1–39:45 (2024).

    Article  Google Scholar 

  166. Sun, W. & David, N. A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes. Faraday Discuss. https://doi.org/10.1039/D4FD00112E (2024).

  167. Boström, H. L. B. et al. How reproducible is the synthesis of Zr–porphyrin metal–organic frameworks? An interlaboratory study. Adv. Mater. 36, 2304832 (2024).

    Article  Google Scholar 

  168. Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).

    Article  CAS  PubMed  Google Scholar 

  169. Colliandre, L. & Muller, C. in High Performance Computing for Drug Discovery and Biomedicine (ed. Heifetz, A.) 101–136 (Springer, 2024).

  170. Häse, F., Roch, L. M., Kreisbeck, C. & Aspuru-Guzik, A. Phoenics: a Bayesian optimizer for chemistry. ACS Cent. Sci. 4, 1134–1145 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  171. Braconi, E. & Godineau, E. Bayesian optimization as a sustainable strategy for early-stage process development? A case study of Cu-catalyzed C–N coupling of sterically hindered pyrazines. ACS Sustain. Chem. Eng. 11, 10545–10554 (2023).

    Article  CAS  Google Scholar 

  172. Liu, C. et al. Understanding causalities in organic photovoltaics device degradation in a machine-learning-driven high-throughput platform. Adv. Mater. 36, 2300259 (2024).

    Article  CAS  Google Scholar 

  173. Coli, G. M., Boattini, E., Filion, L. & Dijkstra, M. Inverse design of soft materials via a deep learning-based evolutionary strategy. Sci. Adv. 8, eabj6731 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  174. Zhou, Z., Li, X. & Zare, R. N. Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci. 3, 1337–1344 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).

    Article  CAS  PubMed  Google Scholar 

  176. Valleti, M., Vasudevan, R. K., Ziatdinov, M. A. & Kalinin, S. V. Bayesian optimization in continuous spaces via virtual process embeddings. Digit. Discov. 1, 910–925 (2022).

    Article  CAS  Google Scholar 

  177. Anker, A. S. et al. Characterising the atomic structure of mono-metallic nanoparticles from X-ray scattering data using conditional generative models. Preprint at https://chemrxiv.org/abs/12662222.v1 (2020).

  178. Kjær, E. T. S. et al. DeepStruc: towards structure solution from pair distribution function data using deep generative models. Digit. Discov. 2, 69–80 (2023).

    Article  PubMed  Google Scholar 

  179. Anstine, D. M. & Isayev, O. Generative models as an emerging paradigm in the chemical sciences. J. Am. Chem. Soc. 145, 8736–8750 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C. The Cambridge structural database. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater. 72, 171–179 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Belsky, A., Hellenbrandt, M., Karen, V. L. & Luksch, P. New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. Acta Crystallogr. B 58, 364–369 (2002).

    Article  PubMed  Google Scholar 

  182. Zunger, A., Wei, S.-H., Ferreira, L. G. & Bernard, J. E. Special quasirandom structures. Phys. Rev. Lett. 65, 353–356 (1990).

    Article  CAS  PubMed  Google Scholar 

  183. Thyagarajan, R. & Sholl, D. S. A database of porous rigid amorphous materials. Chem. Mater. 32, 8020–8033 (2020).

    Article  CAS  Google Scholar 

  184. Cheng, J., Fong, K. D. & Persson, K. A. Materials design principles of amorphous cathode coatings for lithium-ion battery applications. J. Mater. Chem. A 10, 22245–22256 (2022).

    Article  CAS  Google Scholar 

  185. Batatia, I. et al. A foundation model for atomistic materials chemistry. Preprint at https://arxiv.org/abs/2401.00096 (2023).

  186. Zhang, D. et al. DPA-2: a large atomic model as a multi-task learner. Preprint at https://arxiv.org/abs/2312.15492 (2024).

  187. Gardner, J. L. A., Baker, K. T. & Deringer, V. L. Synthetic pre-training for neural-network interatomic potentials. Mach. Learn. Sci. Technol. 5, 015003 (2024).

    Article  Google Scholar 

  188. Kaur, H. et al. Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies. Faraday Discuss. https://doi.org/10.1039/D4FD00107A (2024).

  189. Lunt, A. M. et al. Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry. Chem. Sci. 15, 2456–2463 (2024).

    Article  CAS  PubMed  Google Scholar 

  190. Pithan, L. et al. Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments. J. Synchrotron Radiat. 30, 1064–1075 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  191. Huang, J.-X., Csányi, G., Zhao, J.-B., Cheng, J. & Deringer, V. L. First-principles study of alkali-metal intercalation in disordered carbon anode materials. J. Mater. Chem. A 7, 19070–19080 (2019).

    Article  CAS  Google Scholar 

  192. Deringer, V. L. et al. Towards an atomistic understanding of disordered carbon electrode materials. Chem. Commun. 54, 5988–5991 (2018).

    Article  CAS  Google Scholar 

  193. Ke, G. et al. LightGBM: a highly efficient gradient boosting decision tree. In 31st Conf. Advances Neural Inform. Process. Syst. (eds Guyon, I. et al.) (Curran Associates, 2017).

  194. El-Machachi, Z. et al. Accelerated first-principles exploration of structure and reactivity in graphene oxide. Angew. Chem. Int. Ed. https://doi.org/10.1002/anie.202410088 (2024).

  195. Spöri, C., Kwan, J. T. H., Bonakdarpour, A., Wilkinson, D. P. & Strasser, P. The stability challenges of oxygen evolving catalysts: towards a common fundamental understanding and mitigation of catalyst degradation. Angew. Chem. Int. Ed. 56, 5994–6021 (2017).

    Article  Google Scholar 

  196. Wu, G. et al. A general synthesis approach for amorphous noble metal nanosheets. Nat. Commun. 10, 4855 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Cheng, H., Yang, N., Lu, Q., Zhang, Z. & Zhang, H. Syntheses and properties of metal nanomaterials with novel crystal phases. Adv. Mater. 30, 1707189 (2018).

    Article  Google Scholar 

  198. Griffin, J. M. et al. In situ NMR and electrochemical quartz crystal microbalance techniques reveal the structure of the electrical double layer in supercapacitors. Nat. Mater. 14, 812–819 (2015).

    Article  CAS  PubMed  Google Scholar 

  199. Pecher, O., Carretero-González, J., Griffith, K. J. & Grey, C. P. Materials’ methods: NMR in battery research. Chem. Mater. 29, 213–242 (2017).

    Article  CAS  Google Scholar 

  200. Cao, C., Li, Z.-B., Wang, X.-L., Zhao, X.-B. & Han, W.-Q. Recent advances in inorganic solid electrolytes for lithium batteries. Front. Energy Res. 2, 388–416 (2014).

    Article  Google Scholar 

  201. Xu, Z. & Xia, Y. Progress, challenges and perspectives of computational studies on glassy superionic conductors for solid-state batteries. J. Mater. Chem. A 10, 11854–11880 (2022).

    Article  CAS  Google Scholar 

  202. Schulmeister, K. & Mader, W. TEM investigation on the structure of amorphous silicon monoxide. J. Non Cryst. Solids 320, 143–150 (2003).

    Article  CAS  Google Scholar 

  203. Hirata, A. et al. Atomic-scale disproportionation in amorphous silicon monoxide. Nat. Commun. 7, 11591 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  204. Heremans, P. et al. Mechanical and electronic properties of thin-film transistors on plastic, and their integration in flexible electronic applications. Adv. Mater. 28, 4266–4282 (2016).

    Article  CAS  PubMed  Google Scholar 

  205. Liu, L. & Zhang, C. Fe-based amorphous coatings: structures and properties. Thin Solid Films 561, 70–86 (2014).

    Article  CAS  Google Scholar 

  206. Calin, M. et al. Designing biocompatible Ti-based metallic glasses for implant applications. Mater. Sci. Eng. C 33, 875–883 (2013).

    Article  CAS  Google Scholar 

  207. Zhong, L., Wang, J., Sheng, H., Zhang, Z. & Mao, S. X. Formation of monatomic metallic glasses through ultrafast liquid quenching. Nature 512, 177–180 (2014).

    Article  CAS  PubMed  Google Scholar 

  208. Sohrabi, N., Jhabvala, J. & Logé, R. E. Additive manufacturing of bulk metallic glasses — process, challenges and properties: a review. Metals 11, 1279 (2021).

    Article  CAS  Google Scholar 

  209. Cheng, H. et al. Ligand-exchange-induced amorphization of Pd nanomaterials for highly efficient electrocatalytic hydrogen evolution reaction. Adv. Mater. 32, 1902964 (2020).

    Article  CAS  Google Scholar 

  210. Billinge, S. J. L. & Levin, I. The problem with determining atomic structure at the nanoscale. Science 316, 561–565 (2007).

    Article  CAS  PubMed  Google Scholar 

  211. Momma, K. & Izumi, F. VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Crystallogr. 44, 1272–1276 (2011).

    Article  CAS  Google Scholar 

  212. Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO — the open visualization tool. Model. Simul. Mater. Sci. Eng. 18, 015012 (2010).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by UK Research and Innovation (grant number EP/X016188/1). The work presented in this article is supported by Novo Nordisk Foundation grant number NNF23OC0081359. Structures were visualized with the help of VESTA211 and OVITO212.

Author information

Authors and Affiliations

  1. Department of Chemistry, University of Oxford, Oxford, UK

    Yuanbin Liu, Andy S. Anker & Volker L. Deringer

  2. Department of Chemistry, McGill University, Montreal, Québec, Canada

    Ata Madanchi & Lena Simine

  3. Department of Energy Conversion and Storage, Technical University of Denmark, Kongens Lyngby, Copenhagen, Denmark

    Andy S. Anker

Authors
  1. Yuanbin Liu
    View author publications

    Search author on:PubMed Google Scholar

  2. Ata Madanchi
    View author publications

    Search author on:PubMed Google Scholar

  3. Andy S. Anker
    View author publications

    Search author on:PubMed Google Scholar

  4. Lena Simine
    View author publications

    Search author on:PubMed Google Scholar

  5. Volker L. Deringer
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.L. and V.L.D. prepared the initial draft. All authors contributed to the writing of the paper.

Corresponding authors

Correspondence to Lena Simine or Volker L. Deringer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Materials thanks Kedar Hippalgaonkar, Kiran Prasai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Madanchi, A., Anker, A.S. et al. The amorphous state as a frontier in computational materials design. Nat Rev Mater 10, 228–241 (2025). https://doi.org/10.1038/s41578-024-00754-2

Download citation

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41578-024-00754-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing