Publications

For a complete list of publications that includes papers on the arXiv, please see Google Scholar

2024
- Grieder, A., Kim, K., Wan, L., Chapman, J., Wood, B. C., Adelstein, N., “Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study“, Journal of Physical Chemistry C, May, 2024 PDF
2023
- Aroboto, B., Chen, S., Hsu, T., Wood, B. C., Jiao, Y., Chapman, J., “Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning“, Applied Physics Letters, September 2023 PDF
- Chapman, J., Hsu, T., Chen, X., Heo, T. W., Wood, B. C., “Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm“, Nature Communications, July 2023 PDF
- Chapman, J., Kweon, K. E., Zhu, Y., Bushick, K., Bayu, L., Colla, C., Mason, H., Goldman, N., Keilbart, N., Qui, R., Heo, T. W., Rodriguez, J., Wood., B. C., “Hydrogen in Disordered Titania: Connecting Local Chemistry, Structure, and Stoichiometry through Accelerated Exploration“, Journal of Materials Chemistry A, February 2023 PDF
2022
- Zhu, Y., Heo, T. W., Rodriguez, J., Weber, P., Shi, R., Baer, B., Morgado, F., Antonov, S., Kweon, K., Watkins, E., Savage, D., Chapman, J., Keilbart, N., Song, Y., Zhen, Q., Gault, B., Vogel, S., Sen-Britain, S., Shalloo, M., Orme, C., Hansen, M., Hahn, C., Pham, T. A., Macdonald, D., Qui, S. R., Wood, B. C., “Hydriding of titanium: Recent trends and perspectives in advanced characterization and multiscale modeling”, Current Opinion in Solid State & Materials Science, 101020, July 2022 PDF
- Hsu, T., Weitzner, S., Keilbart, N., Chapman, J., Xiao, P., Pham, T. A., Chen, X., Qiu, R., Wood, B., “An Efficient, Interpretable Atomistic Graph Neural Network Representation for Angle-dependent Properties and its Applications to Optical-Spectroscopy Prediction”, npj Computational Materials, vol. 8, no. 151, July 2022 PDF
- Chapman, J., Goldman, N., “Characterizing the Atomistic Free-volume Morphology of Materials with Graph Theory”, Computational Materials Science, vol. 213, July 2022 PDF
- Chapman, J., Goldman, N., Wood, B., “Efficient and Universal Characterization of Atomic Structures Through a Topological Graph Order Parameter”, npj Computational Materials, vol. 8, no. 37, March 2022 PDF
- Bergh, W., Wechsler, S., Lokupitiya, H., Jarocha, L., Kim, K., Chapman, J., Kweon, K. E., Wood., B., Heald, S., Stefik, M., “Amorphization of T-Nb2O5 Accelerates Intercalation Pseudocapacitance via Faster Lithium Diffusivity Revealed using Tunable Isomorphic Architectures”, Batteries and Supercaps, February 2022 PDF
2020
- Chapman, J., Ramprasad, R., “Multi-scale Modelling of Defect Phenomena in Platinum Using Machine Learning Force Fields”, The Journal of the Minerals, Metals & Materials Society, vol. 72, no. 12, October 2020 PDF
- Chapman, J., Ramprasad, R., “Nanoscale Modelling of Surface Phenomena in Aluminum Using Machine Learning Force Fields”, Journal of Physical Chemistry C, vol. 124, no. 40, September 2020 PDF
- Chapman, J., Ramprasad, R., “Predicting the Dynamic Behavior of the Mechanical Properties of Platinum with Machine Learning”, Journal of Chemical Physics, vol. 152, no. 22, June 2020 PDF
- Chapman, J., Batra, R., Ramprasad, R., “Machine Learning Models for the Prediction of Energy, Forces, and Stresses for Platinum”, Computational Materials Science, vol. 174, March 2020 PDF
2019
- Huan, T.D., Batra, R., Chapman, J., Kim, C., Chandrasekaran, A., Ramprasad, R., “Iterative-learning Strategy for the Development of Application-specific Atomistic Force Fields”, Journal of Physical Chemistry C, vol. 123, no. 34, August 2019 PDF
- Batra, R., Huan, T.D., Kim, C., Chapman, J., Chen, L., Chandrasekaran, A., Ramprasad, R., “General Atomic Neighborhood Fingerprint for Machine Learning-based Methods”, Journal of Physical Chemistry C, vol. 123, no. 25, June 2019 PDF
- Chapman, J., Batra, R., Uberuaga, B.P., Pilania, G., Ramprasad, R., “A Comprehensive Computational Study of Adatom Diffusion on the Aluminum (1 0 0) Surface”, Computational Materials Science, vol. 158, February 2019 PDF
2018
- Chapman, J., Foos, J., Nelson, A., Hartung, E., Williams, A., “Pairwise disagreements of Kekulé, Clar, and Fries Numbers for Benzenoids: a Mathematical and Computational Investigation”, Communications in Mathematical and Computer Chemistry, vol. 80, no. 1, February 2018 PDF
2017
- Huan, T.D., Batra, R., Chapman, J., Krishnan, S., Chen, L., Chandrasekaran, A., Ramprasad, R., “A Universal Strategy for the Creation of Machine Learning-based Atomistic Force Fields”, npj Computational Materials, vol. 3, no. 1, September 2017 PDF
- Botu, V., Chapman, J., Ramprasad, R., “A Study of Adatom Ripening on an Al (1 1 1) Surface with Machine Learning Force Fields”, Computational Materials Science, vol. 129, March 2017 PDF
2016
- Botu, V., Batra, R., Chapman, J., Ramprasad, R., “Machine Learning Force Fields: Construction, Validation, and Outlook”, Journal of Physical Chemistry C, vol. 121, no. 1, December 2016 PDF