MSU Publications

  1. Kalita, B., Zubatyuk, R., Anstine, D. M., Bergeler, M., Settels, V., Stork, C., Spicher, S., Isayev, O., AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry, under review, https://doi.org/10.26434/chemrxiv-2025-kdg6n
  2. Nayal, K. S., O'Connor, D., Zubatyuk, R., Anstine, D. M., Yang, Y., Tom, R., Deng, W., Tang, K., Marom, N., Isayev, O., Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials, under review, https://doi.org/10.26434/chemrxiv-2025-ksn4n
  3. Anstine, D. M., Zhao, Q., Zubatyuk, R., Zhang, S., Singla, V., Nikitin, F., Savoie, B. M., Isayev, O., AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale, under review, https://doi.org/10.26434/chemrxiv-2025-hpdmg
  4. Rapp, J., Anstine, D. M., Gusev, F., Nikitin, F., Yun, K., Borden, M., Bhat, V., Isayev, O., Leibfarth, F., Design of Tough 3D Printable Elastomers with Human-in-the-Loop Reinforcement Learning, Angewandte Chemie, accepted https://doi.org/10.1002/ange.202513147
  5. Anstine, D. M., Zubatyuk, R., Gallegos, L., Paton, R., Weist, O., Nebgen B., Jones, T., Gomes, G., Tretiak, S., Isayev, O., Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions, under review, https://doi.org/10.26434/chemrxiv-2025-n36r6

PreMSU Publications

  1. Casetti, N., Anstine, D. M., Isayev, O., Coley, C., Anticipating the Selectivity of Cyclization Reaction Pathways with Neural Network Potentials, under review, https://doi.org/10.48550/arXiv.2507.10400
  2. Hunnisett et al. The Seventh Blind Test of Crystal Structure Prediction: Structure Generation Methods, Acta Crystallogr., Sect. B: Struct. Sci., 2024, 80(6), 517–547. https://doi.org/10.1107/S2052520624007492
  3. Hunnisett et al. The Seventh Blind Test of Crystal Structure Prediction: Structure Ranking Methods, Acta Crystallogr., Sect. B: Struct. Sci., 2024, 80(6), 548-574. https://doi.org/10.1107/S2052520624008679
  4. Anstine*, D. M., Zubatyuk*, R., Isayev, O., AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs, ChemRxiv, 2024. https://doi.org/10.26434/chemrxiv-2023-296ch
  5. Yang, A., Bukowski, B. C., Anstine, D. M., Colina, C. M., Snurr, R. Q., and Dichtel, W. R., Defect Engineering of Porous Aromatic Frameworks via End Capping Improves Dioxane Removal from Water, Matter, 2023, 6(7), 2263-2273. https://doi.org/10.1016/j.matt.2023.06.013
  6. Zhao, Q., Anstine, D. M., Isayev, O., and Savoie, B. M., ∆2 Machine Learning for Reaction Property Prediction, Chem. Sci., 2023, in press. https://doi.org/10.1039/D3SC02408C
  7. Anstine, D. M., and Isayev, O., Generative Models as an Emerging Paradigm in the Chemical Sciences, J. Am. Chem. Soc., 2023, 145(16), 8736-8750. https://doi.org/10.1021/jacs.2c13467
  8. Anstine, D. M., and Isayev, O., Machine Learning Interatomic Potentials and Long-Range Physics, J. Phys. Chem. A, 2023, 127(11), 2417-2431. https://doi.org/10.1021/acs.jpca.2c06778
  9. Shi, K., Li, Z., Anstine, D. M., Tang, D., Colina, C. M., Sholl, D. S., Siepmann, J. I., and Snurr, R. Q., Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials, J. Chem. Theory Comput., 2023, 19(14), 4568-4583. https://doi.org/10.1021/acs.jctc.2c00798
  10. Morgan, W. J., Anstine, D. M., and Colina, C. M., Temperature Effects in Flexible Adsorption Processes for Amorphous Microporous Polymers, J. Phys. Chem. B, 2022, 126(33), 6354-6365. https://doi.org/10.1021/acs.jpcb.2c04543
  11. Anstine, D. M., Sholl, D. S., Siepmann, J. I., Snurr, R. Q., Aspuru-Guzik, A., and Colina, C. M., In Silico Design and Analysis of Microporous Polymers for Chemical Separations and Gas Storage, Curr. Opin. Chem. Eng., 2022, 36, 100795. https://doi.org/10.1016/j.coche.2022.100795
  12. Mercado-Montijo*, J., Anstine*, D. M., Colina, C. M., and Andrew, J. S., Polyethylene Glycol Diacrylate Hydrogel Structure from Semi-Dilute Concentrations: Insights from Experiments and Molecular Simulations, Soft Matter, 2022, 18(18), 3565-3574. https://doi.org/10.1039/D1SM01708J
  13. Yu, Z., Anstine, D. M., Boulfelfel, S. E., Gu, C., Colina, C. M., and Sholl, D. S., Incorporating Flexibility Effects into Metal–Organic Framework Adsorption Simulations Using Different Models, ACS Appl. Mater. Interfaces, 2021, 13(51), 61305-61315. https://doi.org/10.1021/acsami.1c20583
  14. Anstine, D. M., Tang, D., Sholl, D. S., and Colina, C. M., Adsorption Space for Microporous Polymers with Diverse Adsorbate Species, npj Comput. Mater., 2021, 7, 53. https://doi.org/10.1038/s41524-021-00522-8
  15. Anstine*, D. M., Mendez*, N. F., and Colina, C. M., Sulfonyl PIM-1: A Diverse Separation Membrane with Dilation Resistance, AIChE Journal, 2021, 67(3), e17006. https://doi.org/10.1002/aic.17006
  16. Anstine, D. M. and Colina, C. M., Sorption-Induced Polymer Rearrangement: Approaches from Molecular Modeling, Polymer International, 2021, 70(7), 984-989. https://doi.org/10.1002/pi.6124
  17. Anstine, D. M., Wu, C., Larentzos, J. P., and Brennan, J. K., Computational Model Builder and Analysis Toolkit (COMBAT): Demonstrating Capabilities through Practical Examples, ARL Technical Report, 2020, ARL-TR-8914. https://apps.dtic.mil/sti/citations/AD1092611
  18. Anstine, D. M., Demidov, A. G., Mendez, N. F., Morgan, W. J., and Colina, C. M., Screening PIM-1 Performance as a Membrane for Binary Mixture Separation of Gaseous Organic Compounds, J. Membr. Sci., 2020, 599, 117798. https://doi.org/10.1016/j.memsci.2019.117798
  19. Anstine, D. M., Strachan, A., and Colina, C. M., Effects of an Atomistic Modeling Approach on Predicted Mechanical Properties of Glassy Polymers via Molecular Dynamics, Model. Simul. Mater. Sci., 2020, 28(2), 025006. https://doi.org/10.1088/1361-651X/ab615c
  20. Rukmani, S. J., Anstine, D. M., Munasinghe, A., and Colina, C. M., An Insight into Structural and Mechanical Properties of Ideal-Networked Poly(Ethylene Glycol)-Peptide Hydrogels from Molecular Dynamics Simulations, Macromol. Chem. Phys., 2020, 221(3), 1900326. https://doi.org/10.1002/macp.201900326
  21. Rukmani, S. J., Kupgan, G., Anstine, D. M., and Colina, C. M., A Molecular Dynamics Study of Water-Soluble Polymers: Analysis of Force Fields from Atomistic Simulations, Mol. Simulat., 2019, 45(4-5), 310-231. https://doi.org/10.1080/08927022.2018.1531401
  22. Choi, J., Chang, E. H., Anstine, D. M., and Chakraborty, H. S., Effects of Exchange-Correlation Potentials on the Density-Functional Description of C60 versus C240 Photoionization, Phys. Rev. A., 2017, 95(2), 023404. https://doi.org/10.1103/PhysRevA.95.023404
  23. Werlé, C., Anstine, D. M., Karmazin, L., Bailly, C., Ricard, L., and Djukic, J., New PD(ii) Hemichelates Devoid of Incipient Bridging CO…Pd Interactions, Dalton Trans., 2016, 45(2), 607-617. https://doi.org/10.1039/C5DT03648H
  24. Magrakvelidze, M., Anstine, D. M., Dixit, G., Madjet, M. E. A., and Chakraborty, H. S., Attosecond Structures from the Molecular Cavity in Fullerene Photoemission Time Delay, Phys. Rev. A., 2015, 91(5), 053407. https://doi.org/10.1103/PhysRevA.91.053407
  25. Shi, K., Magrakvelidze, M., Anstine, D. M., Madjet, M. E. A., and Chakraborty, H. S., Attosecond Time Delay in the Valence Photoionization of C240 and C60, J. Phys.: Conf. Ser., 2015, 635, 112025. https://doi.org/10.1088/1742-6596/635/11/112025