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Response Matching for generating materials and molecules (2405.09057v1)

Published 15 May 2024 in cs.LG, cond-mat.mtrl-sci, and physics.comp-ph

Abstract: Machine learning has recently emerged as a powerful tool for generating new molecular and material structures. The success of state-of-the-art models stems from their ability to incorporate physical symmetries, such as translation, rotation, and periodicity. Here, we present a novel generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Consequently, any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching to such response is closely related to score matching in diffusion models. By employing the combination of a machine learning interatomic potential and random structure search as the denoising model, RM exploits the locality of atomic interactions, and inherently respects permutation, translation, rotation, and periodic invariances. RM is the first model to handle both molecules and bulk materials under the same framework. We demonstrate the efficiency and generalization of RM across three systems: a small organic molecular dataset, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration.

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References (47)
  1. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL materials, 1(1), 2013.
  2. Generative models for molecular discovery: Recent advances and challenges. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5):e1608, 2022.
  3. Generative models as an emerging paradigm in the chemical sciences. Journal of the American Chemical Society, 145(16):8736–8750, 2023.
  4. Mattergen: a generative model for inorganic materials design. arXiv preprint arXiv:2312.03687, 2023.
  5. Crystal diffusion variational autoencoder for periodic material generation. In International Conference on Learning Representations, 2021.
  6. Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical review letters, 98(14):146401, 2007.
  7. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Physical review letters, 104(13):136403, 2010.
  8. Alexander V Shapeev. Moment tensor potentials: A class of systematically improvable interatomic potentials. Multiscale Modeling & Simulation, 14(3):1153–1173, 2016.
  9. Ralf Drautz. Atomic cluster expansion for accurate and transferable interatomic potentials. Physical Review B, 99(1):014104, 2019.
  10. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature communications, 13(1):2453, 2022.
  11. Mace: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems, 35:11423–11436, 2022.
  12. Bingqing Cheng. Cartesian atomic cluster expansion for machine learning interatomic potentials. arXiv preprint arXiv:2402.07472, 2024.
  13. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  14. Improved denoising diffusion probabilistic models. In International conference on machine learning, pages 8162–8171. PMLR, 2021.
  15. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. Advances in neural information processing systems, 32, 2019.
  16. E (n) equivariant normalizing flows. Advances in Neural Information Processing Systems, 34:4181–4192, 2021.
  17. Equivariant diffusion for molecule generation in 3d. In International conference on machine learning, pages 8867–8887. PMLR, 2022.
  18. Diffusion-based molecule generation with informative prior bridges. Advances in Neural Information Processing Systems, 35:36533–36545, 2022.
  19. Geometric latent diffusion models for 3d molecule generation. In International Conference on Machine Learning, pages 38592–38610. PMLR, 2023.
  20. Moldiff: addressing the atom-bond inconsistency problem in 3d molecule diffusion generation. arXiv preprint arXiv:2305.07508, 2023.
  21. Mdm: Molecular diffusion model for 3d molecule generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 5105–5112, 2023.
  22. Geometry-complete diffusion for 3d molecule generation and optimization. ArXiv, 2023.
  23. Generating 3d molecules conditional on receptor binding sites with deep generative models. Chemical science, 13(9):2701–2713, 2022.
  24. Inverse design of 3d molecular structures with conditional generative neural networks. Nature communications, 13(1):973, 2022.
  25. Diffdock: Diffusion steps, twists, and turns for molecular docking. arXiv preprint arXiv:2210.01776, 2022.
  26. Chris J Pickard. Tbd. Personal Communication. Personal Communication, 2024.
  27. Data-driven score-based models for generating stable structures with adaptive crystal cells. Journal of Chemical Information and Modeling, 63(22):6986–6997, 2023.
  28. Crystal structure prediction by joint equivariant diffusion. Advances in Neural Information Processing Systems, 36, 2024.
  29. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chemical reviews, 121(16):9816–9872, 2021.
  30. Machine learning force fields. Chemical Reviews, 121(16):10142–10186, 2021.
  31. Physically motivated recursively embedded atom neural networks: Incorporating local completeness and nonlocality. Physical Review Letters, 127(15), October 2021.
  32. Crystal structure prediction using ab initio evolutionary techniques: Principles and applications. The Journal of chemical physics, 124(24), 2006.
  33. Ab initio random structure searching. 23(5):053201, 2011.
  34. Crystal structure prediction by combining graph network and optimization algorithm. Nature communications, 13(1):1492, 2022.
  35. Developments and further applications of ephemeral data derived potentials. The Journal of Chemical Physics, 159(14), 2023.
  36. Scaling deep learning for materials discovery. Nature, 624(7990):80–85, 2023.
  37. Structural relaxation made simple. Physical review letters, 97(17):170201, 2006.
  38. Simulated annealing. Statistical science, 8(1):10–15, 1993.
  39. Machine learning of molecular electronic properties in chemical compound space. New Journal of Physics, 15(9):095003, 2013.
  40. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc., 131:8732, 2009.
  41. Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science, 2024.
  42. E (n) equivariant graph neural networks. In International conference on machine learning, pages 9323–9332. PMLR, 2021.
  43. Graphvae: Towards generation of small graphs using variational autoencoders. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27, pages 412–422. Springer, 2018.
  44. An open source chemical structure curation pipeline using rdkit. Journal of Cheminformatics, 12:1–16, 2020.
  45. Midi: Mixed graph and 3d denoising diffusion for molecule generation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 560–576. Springer, 2023.
  46. Python materials genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science, 68:314–319, 2013.
  47. Ab initio structure search and in situ 7li nmr studies of discharge products in the li–s battery system. Journal of the American Chemical Society, 136(46):16368–16377, 2014.

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