Publications
For a complete list of publications that includes papers on the arXiv, please see Google Scholar
2024
22. Kwon, H., Hsu, T., Sun, W., Jeong, W., Aydin, F., Chapman, J., Chen, X., Lordi, V., Carbone, M., Lu, D., “Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models“, Machine Learning Science and Technology, October, 2024 PDF
21. Hsu, T., Sadigh, B., Bertin, N., Park, C., Chapman, J., Bulatov, V., Zhou, F., “Score-based denoising for atomic structure identification“, npj Computational Materials, July, 2024 PDF
20. 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
19. 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
18. 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
17. 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
16. 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
15. 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
14. Chapman, J., Goldman, N., “Characterizing the Atomistic Free-volume Morphology of Materials with Graph Theory”, Computational Materials Science, vol. 213, July 2022 PDF
13. 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
12. 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
11. 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
10. 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
9. 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
8. 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
7. 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
6. 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
5. 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
3. 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
2. 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
1. 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