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).
Google Scholar
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).
Google Scholar
Pellizzer, F., Pirovano, A., Bez, R. & Meyer, R. L. Status and perspectives of chalcogenide-based crosspoint memories. In 2023 International Electron Devices Meeting (IEDM) 1–4. https://doi.org/10.1109/IEDM45741.2023.10413669 (2023).
Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768–775 (2023).
Google Scholar
Le Gallo, M. et al. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nat. Electron. 6, 680–693 (2023).
Google Scholar
Zhou, W., Shen, X., Yang, X., Wang, J. & Zhang, W. Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing. Int. J. Extrem. Manuf. 6, 022001 (2024).
Google Scholar
Wuttig, M. & Yamada, N. Phase-change materials for rewriteable data storage. Nat. Mater. 6, 824–832 (2007).
Google Scholar
Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).
Google Scholar
Akola, J. & Jones, R. Structural phase transitions on the nanoscale: the crucial pattern in the phase-change materials Ge2Sb2Te5 and GeTe. Phys. Rev. B 76, 235201 (2007).
Google Scholar
Caravati, S., Bernasconi, M., Kühne, T. D., Krack, M. & Parrinello, M. Coexistence of tetrahedral- and octahedral-like sites in amorphous phase change materials. Appl. Phys. Lett. 91, 171906 (2007).
Google Scholar
Xu, M., Cheng, Y., Sheng, H. & Ma, E. Nature of atomic bonding and atomic structure in the phase-change Ge2Sb2Te5 Glass. Phys. Rev. Lett. 103, 195502 (2009).
Google Scholar
Huang, B. & Robertson, J. Bonding origin of optical contrast in phase-change memory materials. Phys. Rev. B 81, 081204 (2010).
Google Scholar
Raty, J.-Y. et al. A quantum-mechanical map for bonding and properties in solids. Adv. Mater. 31, 1806280 (2019).
Google Scholar
Wang, X.-D. et al. Multiscale simulations of growth-dominated Sb2Te phase-change material for non-volatile photonic applications. npj Comput. Mater. 9, 136 (2023).
Google Scholar
Shen, X., Chu, R., Jiang, Y. & Zhang, W. Progress on materials design and multiscale simulations for phase-change memory. Acta Metall. Sin. 60, 1362–1378 (2024).
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).
Google Scholar
Kalikka, J., Akola, J. & Jones, R. O. Crystallization processes in the phase change material Ge2Sb2Te5: Unbiased density functional/molecular dynamics simulations. Phys. Rev. B 94, 134105 (2016).
Google Scholar
Xu, Y. et al. Unraveling crystallization mechanisms and electronic structure of phase-change materials by large-scale ab initio simulations. Adv. Mater. 34, 2109139 (2022).
Google Scholar
Behler, J. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angew. Chem. Int. Ed. 56, 12828–12840 (2017).
Google Scholar
Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).
Google Scholar
Friederich, P., Häse, F., Proppe, J. & Aspuru-Guzik, A. Machine-learned potentials for next-generation matter simulations. Nat. Mater. 20, 750–761 (2021).
Google Scholar
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).
Google Scholar
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Google Scholar
Sosso, G. C., Salvalaglio, M., Behler, J., Bernasconi, M. & Parrinello, M. Heterogeneous crystallization of the phase change material GeTe via atomistic simulations. J. Phys. Chem. C 119, 6428–6434 (2015).
Google Scholar
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).
Google Scholar
Gabardi, S. et al. Atomistic simulations of the crystallization and aging of GeTe nanowires. J. Phys. Chem. C 121, 23827–23838 (2017).
Google Scholar
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).
Google Scholar
Dragoni, D., Behler, J. & Bernasconi, M. Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material. Nanoscale 13, 16146–16155 (2021).
Google Scholar
Mo, P. et al. Accurate and efficient molecular dynamics based on machine learning and non-Von Neumann architecture. npj Comput. Mater. 8, 107 (2022).
Google Scholar
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).
Google Scholar
Zhou, Y., Zhang, W., Ma, E. & Deringer, V. L. Device-scale atomistic modelling of phase-change memory materials. Nat. Electron. 6, 746–754 (2023).
Google Scholar
Li, K., Liu, B., Zhou, J. & Sun, Z. Revealing the crystallization dynamics of Sb–Te phase change materials by large-scale simulations. J. Mater. Chem. C 12, 3897–3906 (2024).
Google Scholar
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).
Google Scholar
Batatia, I., Kovacs, D. P., Simm, G., Ortner, C. & Csanyi, G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems. Vol. 35, 11423–11436 (Curran Associates, Inc., 2022).
Wang, G., Sun, Y., Zhou, J. & Sun, Z. PotentialMind: graph convolutional machine learning potential for Sb–Te binary compounds of multiple stoichiometries. J. Phys. Chem. C 127, 24724–24733 (2023).
Google Scholar
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).
Google Scholar
Drautz, R. Atomic cluster expansion for accurate and transferable interatomic potentials. Phys. Rev. B 99, 014104 (2019).
Google Scholar
Dunton, O. R., Arbaugh, T. & Starr, F. W. Computationally efficient machine-learned model for GST phase change materials via direct and indirect learning. J. Chem. Phys. 162, 034501 (2025).
Google Scholar
Lysogorskiy, Y. et al. Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon. npj Comput. Mater. 7, 97 (2021).
Google Scholar
Bochkarev, A. et al. Efficient parametrization of the atomic cluster expansion. Phys. Rev. Mater. 6, 013804 (2022).
Google Scholar
Dusson, G. et al. Atomic cluster expansion: completeness, efficiency and stability. J. Comput. Phys. 454, 110946 (2022).
Google Scholar
Qamar, M., Mrovec, M., Lysogorskiy, Y., Bochkarev, A. & Drautz, R. Atomic cluster expansion for quantum-accurate large-scale simulations of carbon. J. Chem. Theory Comput. 19, 5151–5167 (2023).
Google Scholar
Deringer, V. L. et al. Gaussian process regression for materials and molecules. Chem. Rev. 121, 10073–10141 (2021).
Google Scholar
Zhou, Y., Elliott, S. R., Toit, D. F. T. Du, Z, W. & Deringer, V. L. The pathway to chirality in elemental tellurium. Preprint at arXiv:2409.03860 (2024).
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).
Google Scholar
Morrow, J. D. & Deringer, V. L. Indirect learning and physically guided validation of interatomic potential models. J. Chem. Phys. 157, 104105 (2022).
Google Scholar
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
Google Scholar
Thomas du Toit, D. F. & Deringer, V. L. Cross-platform hyperparameter optimization for machine learning interatomic potentials. J. Chem. Phys. 159, 024803 (2023).
Google Scholar
Thomas du Toit, D. F., Zhou, Y. & Deringer, V. L. Hyperparameter optimization for atomic cluster expansion potentials. J. Chem. Theory Comput. 20, 10103–10113 (2024).
Google Scholar
Thomas du Toit, D. F. dft-dutoit/XPOT: ACE Release, Zenodo, https://doi.org/10.5281/zenodo.15853809 (2025).
Pickard, C. J. & Needs, R. J. High-pressure phases of silane. Phys. Rev. Lett. 97, 045504 (2006).
Google Scholar
Pickard, C. J. & Needs, R. J. Ab initio random structure searching. J. Phys. Condens. Matter 23, 053201 (2011).
Google Scholar
Deringer, V. L., Proserpio, D. M., Csányi, G. & Pickard, C. J. Data-driven learning and prediction of inorganic crystal structures. Faraday Discuss. 211, 45–59 (2018).
Google Scholar
Deringer, V. L., Pickard, C. J. & Csányi, G. Data-driven learning of total and local energies in elemental boron. Phys. Rev. Lett. 120, 156001 (2018).
Google Scholar
Bernstein, N., Csányi, G. & Deringer, V. L. De novo exploration and self-guided learning of potential-energy surfaces. npj Comput. Mater. 5, 99 (2019).
Google Scholar
Batatia, I. et al. The design space of E(3)-equivariant atom-centred interatomic potentials. Nat. Mach. Intell. 7, 56–67 (2025).
Google Scholar
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).
Google Scholar
Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021).
Google Scholar
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).
Google Scholar
Allen, A. E. A. et al. Learning together: towards foundation models for machine learning interatomic potentials with meta-learning. npj Comput. Mater. 10, 154 (2024).
Google Scholar
Liu, Y. et al. An automated framework for exploring and learning potential-energy surfaces. Nat. Commun. 16, 7666 (2025).
Deringer, V. L., Caro, M. A. & Csányi, G. A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nat. Commun. 11, 5461 (2020).
Google Scholar
Pickard, C. J. Ephemeral data derived potentials for random structure search. Phys. Rev. B 106, 014102 (2022).
Google Scholar
Pickard, C. J. Beyond theory-driven discovery: introducing hot random search and datum-derived structures. Faraday Discuss. 256, 61–84 (2025).
Google Scholar
Waldecker, L. et al. Time-domain separation of optical properties from structural transitions in resonantly bonded materials. Nat. Mater. 14, 991–995 (2015).
Google Scholar
Feldmann, J. et al. Calculating with light using a chip-scale all-optical abacus. Nat. Commun. 8, 1256 (2017).
Google Scholar
Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87, 184115 (2013).
Google Scholar
Welnic, W. & Wuttig, M. Reversible switching in phase-change materials. Mater. Today 11, 20–27 (2008).
Google Scholar
Loke, D. et al. Breaking the speed limits of phase-change memory. Science 336, 1566–1569 (2012).
Google Scholar
Loke, D. K. et al. Ultrafast nanoscale phase-change memory enabled by single-pulse conditioning. ACS Appl. Mater. Interfaces 10, 41855–41860 (2018).
Google Scholar
Li, Z., Si, C., Zhou, J., Xu, H. & Sun, Z. Yttrium-doped Sb2Te3: a promising material for phase-change memory. ACS Appl. Mater. Interfaces 8, 26126–26134 (2016).
Google Scholar
Rao, F. et al. Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing. Science 358, 1423–1427 (2017).
Google Scholar
Wang, Y. et al. Scandium doped Ge2Sb2Te5 for high-speed and low-power-consumption phase change memory. Appl. Phys. Lett. 112, 133104 (2018).
Google Scholar
Hu, S., Xiao, J., Zhou, J., Elliott, S. R. & Sun, Z. Synergy effect of co-doping Sc and Y in Sb2Te3 for phase-change memory. J. Mater. Chem. C 8, 6672–6679 (2020).
Google Scholar
Wang, X.-P. et al. Time-dependent density-functional theory molecular-dynamics study on amorphization of Sc-Sb-Te alloy under optical excitation. npj Comput. Mater. 6, 31 (2020).
Google Scholar
Ronneberger, I., Zhang, W., Eshet, H. & Mazzarello, R. Crystallization properties of the Ge2Sb2Te5 phase-change compound from advanced simulations. Adv. Funct. Mater. 25, 6407–6413 (2015).
Google Scholar
Laio, A. & Parrinello, M. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 99, 12562–12566 (2002).
Google Scholar
Kalikka, J., Akola, J., Larrucea, J. & Jones, R. O. Nucleus-driven crystallization of amorphous Ge2Sb2Te5: a density functional study. Phys. Rev. B 86, 144113 (2012).
Google Scholar
Cheng, H. Y. et al. Atomic-level engineering of phase change material for novel fast-switching and high-endurance PCM for storage class memory application. In 2013 IEEE International Electron Devices Meeting 30.6.1–30.6.4. https://doi.org/10.1109/IEDM.2013.6724726 (2013).
Cheng, H.-Y., Carta, F., Chien, W.-C., Lung, H.-L. & BrightSky, M. J. 3D cross-point phase-change memory for storage-class memory. J. Phys. D Appl. Phys. 52, 473002 (2019).
Google Scholar
Lysogorskiy, Y., Bochkarev, A., Mrovec, M. & Drautz, R. Active learning strategies for atomic cluster expansion models. Phys. Rev. Mater. 7, 043801 (2023).
Google Scholar
Park, Y. J., Lee, J. Y. & Kim, Y. T. In situ transmission electron microscopy study of the nucleation and grain growth of Ge2Sb2Te5 thin films. Appl. Surf. Sci. 252, 8102–8106 (2006).
Google Scholar
Zhang, B. et al. Vacancy structures and melting behavior in rock-salt GeSbTe. Sci. Rep. 6, 25453 (2016).
Google Scholar
Energy use and emissions – ARCHER2 user guide. https://docs.archer2.ac.uk/user-guide/energy/.
Song, Z. et al. 12-state multi-level cell storage implemented in a 128 Mb phase change memory chip. Nanoscale 13, 10455–10461 (2021).
Google Scholar
Abou El Kheir, O. & Bernasconi, M. Million-atom simulation of the set process in phase change memories at the real device scale. Adv. Electron. Mater. 11, e2500110 (2025).
Nandakumar, S. R. et al. A phase-change memory model for neuromorphic computing. J. Appl. Phys. 124, 152135 (2018).
Google Scholar
Sebastian, A. et al. Tutorial: Brain-inspired computing using phase-change memory devices. J. Appl. Phys. 124, 111101 (2018).
Google Scholar
Zhang, W. & Ma, E. Unveiling the structural origin to control resistance drift in phase-change memory materials. Mater. Today 41, 156–176 (2020).
Google Scholar
Raty, J. Y. et al. Aging mechanisms in amorphous phase-change materials. Nat. Commun. 6, 7467 (2015).
Google Scholar
Hosseini, P., Wright, C. D. & Bhaskaran, H. An optoelectronic framework enabled by low-dimensional phase-change films. Nature 511, 206–211 (2014).
Google Scholar
Du, K.-K. et al. Control over emissivity of zero-static-power thermal emitters based on phase-changing material GST. Light Sci. Appl. 6, e16194 (2017).
Google Scholar
Dong, W. et al. Tunable mid-infrared phase-change metasurface. Adv. Opt. Mater. 6, 1701346 (2018).
Google Scholar
Wang, D. et al. Non-volatile tunable optics by design: from chalcogenide phase-change materials to device structures. Mater. Today 68, 334–355 (2023).
Google Scholar
El-Machachi, Z. et al. Accelerated first-principles exploration of structure and reactivity in graphene oxide. Angew. Chem. Int. Ed. 63, e202410088 (2024).
Google Scholar
Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).
Google Scholar
Cheng, B., Mazzola, G., Pickard, C. J. & Ceriotti, M. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature 585, 217–220 (2020).
Google Scholar
Wang, T. et al. Ab initio characterization of protein molecular dynamics with AI2BMD. Nature 635, 1019–1027 (2024).
Google Scholar
Liu, Y., Madanchi, A., Anker, A. S., Simine, L. & Deringer, V. L. The amorphous state as a frontier in computational materials design. Nat. Rev. Mater. 10, 228–241 (2024).
Google Scholar
Miret, S., Lee, K. L. K., Gonzales, C., Mannan, S. & Krishnan, N. M. A. Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials. Preprint at arXiv:2502.03660 (2025).
Frequently asked questions (FAQ). https://pacemaker.readthedocs.io/en/latest/pacemaker/faq (2025).
Beckett, G. et al. ARCHER2 Service Description. https://zenodo.org/records/14507040, https://doi.org/10.5281/zenodo.14507040 (2024).
Kühne, T. D. et al. CP2K: an electronic structure and molecular dynamics software package – Quickstep: Efficient and accurate electronic structure calculations. J. Chem. Phys. 152, 194103 (2020).
Google Scholar
Goedecker, S., Teter, M. & Hutter, J. Separable dual-space Gaussian pseudopotentials. Phys. Rev. B 54, 1703 (1996).
Google Scholar
Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47, 558–561 (1993).
Google Scholar
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).
Google Scholar
Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).
Google Scholar
Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758 (1999).
Google Scholar
Thompson, A. P. et al. LAMMPS – a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).
Google Scholar
Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. Model. Simul. Mater. Sci. Eng. 18, 015012 (2010).
Google Scholar
Zhou, Y., Thomas du Toit, D. F., Elliott, S. R., Zhang, W. & Deringer, V. L. Research data for “Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential”. Zenodo https://doi.org/10.5281/zenodo.14755074 (2025).
Larsen, P. M., Schmidt, S. & Schiøtz, J. Robust structural identification via polyhedral template matching. Modelling Simul. Mater. Sci. Eng. 24, 055007 (2016).
Google Scholar
