Further reading

Review articles and tutorials on machine-learned interatomic potentials, from general introductions to foundation models and fine-tuning.

  • A. J. Medford, D. S. Sholl. Replacing Quantum Chemistry With Machine-Learned Interatomic Potentials: Revolution or Evolution? ACS Cent. Sci. 12, 891-894 (2026). doi:10.1021/acscentsci.6c00615 Short, balanced perspective on the prospects and limits of MLIPs replacing DFT.

  • J. Behler. Four Generations of High-Dimensional Neural Network Potentials. Chem. Rev. 121, 10037-10072 (2021). doi:10.1021/acs.chemrev.0c00868 Classic taxonomy of neural-network potentials: local, long-range and charge-aware generations.

  • O. T. Unke, S. Chmiela, H. E. Sauceda, … K.-R. Müller. Machine Learning Force Fields. Chem. Rev. 121, 10142-10186 (2021). doi:10.1021/acs.chemrev.0c01111 Foundational overview of ML force fields, descriptors, kernels and neural networks.

  • V. L. Deringer, M. A. Caro, G. Csányi. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Adv. Mater. 31, 1902765 (2019). doi:10.1002/adma.201902765 Accessible materials-science introduction to MLIPs and their applications.

  • E. C.-Y. Yuan, Y. Liu, J. Chen, … T. Head-Gordon. Foundation models for atomistic simulation of chemistry and materials. Nat. Rev. Chem. 10, 212-230 (2026). doi:10.1038/s41570-025-00793-5 Universal MLIPs: datasets, architectures, fine-tuning and limitations.

  • J. Kim, H. Cho, H. Jeon, J. Jung, S. Han. Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science. Chem. Rev. 126, 4467-4510 (2026). doi:10.1021/acs.chemrev.5c00728 MLIPs for bond breaking, reactions, barriers and reactive sampling.

  • R. Jacobs, D. Morgan, S. Attarian, J. Meng, et al. A practical guide to machine learning interatomic potentials – Status and future. Curr. Opin. Solid State Mater. Sci. 35, 101214 (2025). doi:10.1016/j.cossms.2025.101214 Practical guide to choosing, training and validating MLIPs.

  • X. Liu, K. Zeng, Z. Luo, Y. Wang, et al. Fine-tuning universal machine-learned interatomic potentials: A Tutorial on methods and applications. J. Appl. Phys. 139, 041101 (2026). doi:10.1063/5.0299305 Hands-on tutorial on fine-tuning universal MLIPs for specific systems.