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                    calculation. Nat. Comput. Sci. 2022, 2, 367-77.  DOI  PubMed  PMC
               5.       Kochkov, D.; Pfaff, T.; Sanchez-Gonzalez, A.; Battaglia, P.; Clark, B. K. Learning ground states of quantum Hamiltonians with
                    graph networks. arXiv 2021, arXiv:2110.16390. https://doi.org/10.48550/arXiv.2110.06390. (accessed 30 Jun 2025)
               6.       Behler, J.; Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett.
                    2007, 98, 146401.  DOI  PubMed
               7.       Bartók, A. P.; Payne, M. C.; Kondor, R.; Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without
                    the electrons. Phys. Rev. Lett. 2010, 104, 136403.  DOI  PubMed
               8.       Zhang, L.; Han, J.; Wang, H.; Car, R.; E, W. Deep potential molecular dynamics: a scalable model with the accuracy of quantum
                    mechanics. Phys. Rev. Lett. 2018, 120, 143001.  DOI
               9.       Chan, H.; Narayanan, B.; Cherukara, M. J.; et al. Machine learning classical interatomic potentials for molecular dynamics from first-
                    principles training data. J. Phys. Chem. C. 2019, 123, 6941-57.  DOI
               10.       Ko, T. W.; Ong, S. P. Recent advances and outstanding challenges for machine learning interatomic potentials. Nat. Comput. Sci.
                    2023, 3, 998-1000.  DOI  PubMed
               11.       Yang, Z.; Wang, X.; Li, Y.; Lv, Q.; Chen, C. Y.; Shen, L. Efficient equivariant model for machine learning interatomic potentials.
                    npj. Comput. Mater. 2025, 11, 1535.  DOI
               12.       Ahmad, W.; Simon, E.; Chithrananda, S.; Grand, G.; Ramsundar, B. ChemBERTa-2: towards chemical foundation models. arXiv
                    2022, arXiv:2209.01712. https://doi.org/10.48550/arXiv.2209.01712. (accessed 30 Jun 2025)
               13.       Li, J.; Jiang, X.; Wang, Y. Mol BERT: an effective molecular representation with BERT for molecular property prediction. Wirel.
                    Commun. Mob. Comput. 2021, 2021, 7181815.  DOI
               14.       Batzner, S.; Musaelian, A.; Sun, L.; et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic
                    potentials. Nat. Commun. 2022, 13, 2453.  DOI  PubMed  PMC
               15.       Duignan, T. T. The potential of neural network potentials. ACS. Phys. Chem. Au. 2024, 4, 232-41.  DOI  PubMed  PMC
               16.       Dong, L.; Zhang, X.; Yang, Z.; Shen, L.; Lu, Y. Accurate piezoelectric tensor prediction with equivariant attention tensor graph
                    neural network. npj. Comput. Mater. 2025, 11, 1546.  DOI
               17.       Zitnick, C. L.; Das, A.; Kolluru, A.; et al. Spherical channels for modeling atomic interactions. arXiv 2022, arXiv:2206.14331. https://
                    doi.org/10.48550/arXiv.2206.14331. (accessed 30 Jun 2025)
               18.       Frank, J. T.; Unke, O. T.; Müller, K. R.; Chmiela, S. A Euclidean transformer for fast and stable machine learned force fields. Nat.
                    Commun. 2024, 15, 6539.  DOI  PubMed  PMC
               19.       Yuan, Z.; Xu, Z.; Li, H.; et al. Equivariant neural network force fields for magnetic materials. Quantum. Front. 2024, 3, 55.  DOI
               20.       Yu, H.; Zhong, Y.; Hong, L.; et al. Spin-dependent graph neural network potential for magnetic materials. Phys. Rev. B. 2024, 109,
                    144426.  DOI
               21.       Wang, H.; Zhang, L.; Han, J.; E, W. DeePMD-kit: a deep learning package for many-body potential energy representation and
                    molecular dynamics. Comput. Phys. Commun. 2018, 228, 178-84.  DOI
               22.       Sokolovskiy, V.; Baigutlin, D.; Miroshkina, O.; Buchelnikov, V. Meta-GGA SCAN functional in the prediction of ground state
                    properties of magnetic materials: review of the current state. Metals 2023, 13, 728.  DOI
               23.       Kirklin, S.; Saal, J. E.; Meredig, B.; et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation
                    energies. npj. Comput. Mater. 2015, 1, BFnpjcompumats201510.  DOI
               24.       Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von, L. O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci.
                    Data. 2014, 1, 140022.  DOI
               25.       Chmiela, S.; Tkatchenko, A.; Sauceda, H. E.; Poltavsky, I.; Schütt, K. T.; Müller, K. R. Machine learning of accurate energy-
                    conserving molecular force fields. Sci. Adv. 2017, 3, e1603015.  DOI  PubMed  PMC
               26.       Chmiela, S.; Vassilev-Galindo, V.; Unke, O. T.; et al. Accurate global machine learning force fields for molecules with hundreds of
                    atoms. arXiv 2022, arXiv 2209.14865. https://doi.org/10.48550/arXiv.2209.14865. (accessed 30 Jun 2025)
               27.       Smith, J. S.; Isayev, O.; Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field
                    computational cost. Chem. Sci. 2017, 8, 3192-203.  DOI  PubMed  PMC
               28.       Smith, J. S.; Nebgen, B.; Lubbers, N.; Isayev, O.; Roitberg, A. E. Less is more: sampling chemical space with active learning. J.
                    Chem. Phys. 2018, 148, 241733.  DOI  PubMed
               29.       Smith, J. S.; Nebgen, B. T.; Zubatyuk, R.; et al. Approaching coupled cluster accuracy with a general-purpose neural network
                    potential through transfer learning. Nat. Commun. 2019, 10, 2903.  DOI  PubMed  PMC
               30.       Devereux, C.; Smith, J. S.; Huddleston, K. K.; et al. Extending the applicability of the ANI Deep learning molecular potential to
                    sulfur and halogens. J. Chem. Theory. Comput. 2020, 16, 4192-202.  DOI
               31.       Schütt, K. T.; Sauceda, H. E.; Kindermans, P. J.; Tkatchenko, A.; Müller, K. R. SchNet - a deep learning architecture for molecules
                    and materials. J. Chem. Phys. 2018, 148, 241722.  DOI  PubMed
               32.       Eastman, P.; Behara, P. K.; Dotson, D. L.; et al. SPICE, a dataset of drug-like molecules and peptides for training machine learning
                    potentials. Sci. Data. 2023, 10, 11.  DOI  PubMed  PMC
               33.       Chanussot, L.; Das, A.; Goyal, S.; et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS. Catal. 2021, 11, 6059-
                    72.  DOI
               34.       Tran, R.; Lan, J.; Shuaibi, M.; et al. The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts. ACS. Catal.
                    2023, 13, 3066-84.  DOI
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