Advances in Machine-Learning Methodologies for Atomistic Simulations

FOCUS · MAR-A45 · ID: 3984616

Presentations

  • ORAL

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    Publication: C. W. Tan, M. L. Descoteaux et al. High-performance training and inference for deep equivariant interatomic potentials. arXiv preprint arXiv:2504.16068 (2025).

    Presenters

    • Chuin Wei Tan

      • Harvard University

    Authors

    • Chuin Wei Tan

      • Harvard University

    • Marc L Descoteaux

      • Harvard University

    • Mit Kotak

      • MIT

    • Gabriel de Miranda Nascimento

      • MIT

    • Seán R Kavanagh

      • University of Cambridge
      • Cambridge University

    • Laura Zichi

      • Harvard University

    • Menghang Wang

      • Harvard University

    • Aadit Saluja

      • Harvard University

    • Yizhong Hu

      • Harvard University

    • Tess E Smidt

      • Massachusetts Institute of Technology

    • Anders Johansson

      • Sandia National Labs
      • Harvard University

    • William C Witt

      • Harvard University

    • Boris Kozinsky

      • Harvard University
      • Harvard University, Robert Bosch Research and Technology Center

    • Albert Musaelian

      • Harvard University

    View abstract →

  • ORAL

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    Presenters

    • Matthias Scheffler

      • The NOMAD Laboratory at the FHI of the Max Planck Society, Berlin
      • The NOMAD Laboratory at FHI, Max Planck Society

    Authors

    • Matthias Scheffler

      • The NOMAD Laboratory at the FHI of the Max Planck Society, Berlin
      • The NOMAD Laboratory at FHI, Max Planck Society

    • AKHIL S NAIR

      • The NOMAD Laboratory at FHI, Max Planck Society

    • Lucas Foppa

      • The NOMAD Laboratory at the FHI, Max Planck Society

    View abstract →

  • ORAL

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    Publication: [1] P. Chandra and P. B. Littlewood, "A landau primer for ferroelectrics," in Physics of Ferroelectrics: A Modern Perspective (Springer Berlin Heidelberg, Berlin, Heidelberg, 2007) pp. 69–116.
    [2] L.-Q. Chen, Annual review of materials research 32, 113 (2002).
    [3] F. Xue, T. Yang, and L.-Q. Chen, Phys. Rev. B 103, 064202 (2021).
    [4] A. Yadav, C. Nelson, S. Hsu, Z. Hong, J. Clarkson, C. Schlep¨utz, A. Damodaran, P. Shafer, E. Arenholz, L. Dedon, et al., Nature 530, 198 (2016).
    [5] P. Kumar, M. Hoffmann, A. Nonaka, S. Salahuddin, and Z. Yao, Advanced Electronic Materials , 2400085 (2024).
    [6] P. Kumar, A. Nonaka, R. Jambunathan, G. Pahwa, S. Salahuddin, and Z. Yao, Computer Physics Communications 290, 108757 (2023).
    [7] A. K. Saha and S. K. Gupta, Scientific reports 10, 10207 (2020).
    [8] A. K. Yadav, K. X. Nguyen, Z. Hong, P. Garc´ıaFern´andez, P. Aguado-Puente, C. T. Nelson, S. Das, B. Prasad, D. Kwon, S. Cheema, et al., Nature 565, 468 (2019).
    [9] A. F. Devonshire, Philos. Mag. 40, 1040 (1949).
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    [12] D. Vanderbilt, Berry phases in electronic structure theory: electric polarization, orbital magnetization and topological insulators (Cambridge University Press, 2018).
    [13] R. Resta and D. Vanderbilt, "Theory of polarization: A modern approach," in Physics of Ferroelectrics: A Modern Perspective (Springer Berlin Heidelberg, Berlin, Heidelberg, 2007) pp. 31–68.
    [14] The redundancy is eliminated when a reference structure is chosen for the zero of polarization.
    [15] The periodicity of the polarization is associated with the polarization quantum [12, 13]. In practice, the change of polarization typically does not exceed the quantum of polarization. Thus, polarization can often be treated as a single-value function, as we do in this paper.
    [16] L. Zhang, J. Han, H. Wang, R. Car, and W. E, Phys. Rev. Lett. 120, 143001 (2018).
    [17] L. Zhang, M. Chen, X. Wu, H. Wang, W. E, and R. Car, Phys. Rev. B 102, 041121 (2020).
    [18] A. Barducci, M. Bonomi, and M. Parrinello, Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 826 (2011).
    [19] O. Valsson and M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014).
    [20] M. Invernizzi, P. M. Piaggi, and M. Parrinello, Phys. Rev. X 10, 041034 (2020).
    [21] A. Barducci, G. Bussi, and M. Parrinello, Phys. Rev. Lett. 100, 020603 (2008).
    [22] J. ´I˜niguez, S. Ivantchev, J. M. Perez-Mato, and A. Garc´ıa, Phys. Rev. B 63, 144103 (2001).
    [23] G. Geneste, Phys. Rev. B 79, 064101 (2009).
    [24] A. Kumar and U. V. Waghmare, Phys. Rev. B 82, 054117 (2010).
    [25] L. Zhang, J. Han, H. Wang, W. Saidi, R. Car, et al., Advances in Neural Information Processing Systems 31 (2018).
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    [27] L. Zhang, D.-Y. Lin, H. Wang, R. Car, and W. E, Phys. Rev. Materials 3, 023804 (2019).
    [28] Y. Zhang, H. Wang, W. Chen, J. Zeng, L. Zhang, H. Wang, and W. E, Comput. Phys. Commun. 253, 107206 (2020).
    [29] P. Xie, Y. Chen, W. E, and R. Car, Physical Review B 111, 094113 (2025).
    [30] M. J. Haun, E. Furman, S. Jang, H. McKinstry, and L. Cross, J. Appl. Phys. 62, 3331 (1987).
    [31] L.-Q. Chen, in Physics of ferroelectrics: a modern perspective (Springer, 2007) pp. 363–372.
    [32] O. Eriksson, A. Bergman, L. Bergqvist, and J. Hellsvik, Atomistic spin dynamics: foundations and applications (Oxford university press, 2017).
    [33] "OpenFerro," https://github.com/salinelake/ OpenFerro, accessed: 2025-05-10.
    [34] "Supplemental Material," https://github.com/salinelake/ab_initio_PbTiO3, accessed: 2025-05-10.
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    [36] J. Zeng, D. Zhang, D. Lu, P. Mo, Z. Li, Y. Chen, M. Rynik, L. Huang, Z. Li, S. Shi, et al., The Journal of Chemical Physics 159 (2023).
    [37] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. In't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, et al., Computer physics communications 271, 108171 (2022).
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    Presenters

    • Xinyu Xu

      • Lawrence Berkeley National Laboratory

    Authors

    • Xinyu Xu

      • Lawrence Berkeley National Laboratory

    • Pinchen Xie

      • Lawrence Berkeley National Lab

    • Yixiao Chen

      • Princeton University

    • Zhi (Jackie) Yao

      • Lawrence Berkeley National Laboratory

    • Weinan E

      • Princeton University

    • Roberto Car

      • Princeton University

    View abstract →

  • ORAL

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    Publication: Ding, J., Zichi, L., Carli, M., Wang, M., Musaelian, A., Xie, Y., & Kozinsky, B. (2025). Coupled reaction and diffusion governing interface evolution in solid-state batteries. arXiv preprint arXiv:2506.10944.

    Presenters

    • Laura Zichi

      • Harvard University

    Authors

    • Laura Zichi

      • Harvard University

    • Matteo Carli

      • Harvard University

    • Jingxuan Ding

      • Harvard University

    • Menghang Wang

      • Harvard University

    • Yu Xie

      • Harvard University

    • Boris Kozinsky

      • Harvard University

    View abstract →

  • ORAL

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    Publication: [1] Y. Liu, X. He, and Y. Mo, Discrepancies and error evaluation metrics for machine learning interatomic potentials, npj Computational Materials 9, 174 (2023).
    [2] J. George, G. Hautier, A. P. Bart´ok, G. Cs´anyi, and V. L. Deringer, Combining phonon accuracy with high transferability in gaussian approximation potential models, The Journal of Chemical Physics 153 (2020).
    [3] Y.-J. Choi and S.-H. Jhi, Efficient training of machine learning potentials by a randomized atomic-system generator, The Journal of Physical Chemistry B 124, 8704 (2020).
    [4] P. Pernot, B. Huang, and A. Savin, Impact of non-normal error distributions on the bench- marking and ranking of quantum machine learning models, Machine Learning: Science and Technology 1, 035011 (2020).
    [5] S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials, Nature communications 13, 2453 (2022).

    Presenters

    • Young-Jae Choi

      • University of Illinois at Urbana-Champaign

    Authors

    • Young-Jae Choi

      • University of Illinois at Urbana-Champaign

    • Lucas Kyle Wagner

      • University of Illinois at Urbana-Champaign

    View abstract →

  • ORAL

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    Presenters

    • Francesco Ricci

      • Lawrence Berkeley National Laboratory
      • Universite catholique de Louvain / Matgenix

    Authors

    • Francesco Ricci

      • Lawrence Berkeley National Laboratory
      • Universite catholique de Louvain / Matgenix

    • Shangbo Li

      • Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University

    • Xiaotong Liu

      • Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University

    • David Waroquiers

      • Matgenix

    • Gian-Marco Rignanese

      • Universite catholique de Louvain

    View abstract →