{"id":17,"date":"2022-07-20T11:10:43","date_gmt":"2022-07-20T15:10:43","guid":{"rendered":"https:\/\/sites.bu.edu\/mil\/?page_id=17"},"modified":"2026-03-27T09:37:47","modified_gmt":"2026-03-27T13:37:47","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/mil\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

For a complete list of publications that includes papers on the arXiv, please see Google Scholar<\/a><\/p>\n

23.<\/strong> Tenorio, M., Md, H. R., Mannodi-Kanakkithodi, A., Chapman, J., “Out-of-distribution machine learning for materials discovery: challenges and opportunities<\/em><\/a>“, Chemical Physics Reviews, March, 2026 PDF<\/a><\/span><\/p>\n

22.<\/strong> 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<\/em><\/a>“, Machine Learning Science and Technology, October, 2024 PDF<\/a>
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21.<\/strong> Hsu, T., Sadigh, B., Bertin, N., Park, C., Chapman, J., Bulatov, V., Zhou, F., “Score-based denoising for atomic structure identification<\/em><\/a>“, npj Computational Materials, July, 2024 PDF<\/a>
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20.<\/strong> 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<\/em><\/a>“, Journal of Physical Chemistry C, May, 2024 PDF<\/a><\/span><\/p>\n

19.<\/strong> 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<\/em><\/a>“, Applied Physics Letters, September 2023 PDF<\/a><\/span><\/span><\/p>\n

18.<\/strong> 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<\/em><\/a>“, Nature Communications, July 2023 PDF<\/a><\/span><\/p>\n

17.<\/strong> 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<\/em><\/a>“, Journal of Materials Chemistry A, February 2023 <\/span>PDF<\/a><\/span><\/span><\/p>\n

16.<\/strong> 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., \u201cHydriding of titanium: Recent trends and perspectives in advanced characterization and multiscale modeling<\/i><\/a>\u201d, Current Opinion in Solid State & Materials Science, 101020, July 2022 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

15.<\/strong> Hsu, T., Weitzner, S., Keilbart, N., Chapman, J., Xiao, P., Pham, T. A., Chen, X., Qiu, R., Wood, B., \u201cAn Efficient, Interpretable Atomistic Graph Neural Network Representation for Angle-dependent Properties and its Applications to Optical-Spectroscopy Prediction<\/em><\/a>\u201d, npj Computational Materials, vol. 8, no. 151, July 2022 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

14.<\/strong> Chapman, J., Goldman, N., \u201cCharacterizing the Atomistic Free-volume Morphology of Materials with Graph Theory<\/em><\/a>\u201d, Computational Materials Science, vol. 213, July 2022 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

13.<\/strong> Chapman, J., Goldman, N., Wood, B., \u201cEfficient and Universal Characterization of Atomic Structures Through a Topological Graph Order Parameter<\/em><\/a>\u201d, npj Computational Materials, vol. 8, no. 37, March 2022 PDF<\/a><\/span><\/p>\n

12.<\/strong> Bergh, W., Wechsler, S., Lokupitiya, H., Jarocha, L., Kim, K., Chapman, J., Kweon, K. E., Wood., B., Heald, S., Stefik, M., \u201cAmorphization of T-Nb2O5 Accelerates Intercalation Pseudocapacitance via Faster Lithium Diffusivity Revealed using Tunable Isomorphic Architectures<\/a>\u201d<\/i>, Batteries and Supercaps, February 2022 <\/span>PDF<\/a><\/span><\/span><\/p>\n

11.<\/strong> Chapman, J., Ramprasad, R., \u201cMulti-scale Modelling of Defect Phenomena in Platinum Using Machine Learning Force Fields<\/em><\/a>\u201d, The Journal of the Minerals, Metals & Materials Society, vol. 72, no. 12, October 2020 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

10.<\/strong> Chapman, J., Ramprasad, R., \u201cNanoscale Modelling of Surface Phenomena in Aluminum Using Machine Learning Force Fields<\/em><\/a>\u201d, Journal of Physical Chemistry C, vol. 124, no. 40, September 2020 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

9.<\/strong> Chapman, J., Ramprasad, R., \u201cPredicting the Dynamic Behavior of the Mechanical Properties of Platinum with Machine Learning<\/em><\/a>\u201d, Journal of Chemical Physics, vol. 152, no. 22, June 2020 PDF<\/a><\/span><\/p>\n

8.<\/strong> Chapman, J., Batra, R., Ramprasad, R., \u201cMachine Learning Models for the Prediction of Energy, Forces, and Stresses for Platinum<\/em><\/a>\u201d, Computational Materials Science, vol. 174, March 2020 <\/span>PDF<\/a><\/span><\/span><\/p>\n

7.<\/strong> Huan, T.D., Batra, R., Chapman, J., Kim, C., Chandrasekaran, A., Ramprasad, R., \u201cIterative-learning Strategy for the Development of Application-specific Atomistic Force Fields<\/em><\/a>\u201d, Journal of Physical Chemistry C, vol. 123, no. 34, August 2019 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

6.<\/strong> Batra, R., Huan, T.D., Kim, C., Chapman, J., Chen, L., Chandrasekaran, A., Ramprasad, R., \u201cGeneral Atomic Neighborhood Fingerprint for Machine Learning-based Methods<\/em><\/a>\u201d, Journal of Physical Chemistry C, vol. 123, no. 25, June 2019 PDF<\/a><\/o:p><\/span><\/span><\/p>\n

5.<\/strong> Chapman, J., Batra, R., Uberuaga, B.P., Pilania, G., Ramprasad, R., \u201cA Comprehensive Computational Study of Adatom Diffusion on the Aluminum (1 0 0) Surface<\/em><\/a>\u201d, Computational Materials Science, vol. 158, February 2019 PDF<\/a><\/span><\/p>\n

<\/a><\/o:p><\/span><\/strong><\/strong><\/strong>4.<\/strong> Chapman, J., Foos, J., Nelson, A., Hartung, E., Williams, A., \u201cPairwise disagreements of Kekul\u00e9, Clar, and Fries Numbers for Benzenoids: a Mathematical and Computational Investigation<\/em><\/a>\u201d, Communications in Mathematical and Computer Chemistry, vol. 80, no. 1, February 2018 PDF<\/a><\/span><\/p>\n

3.<\/strong> Huan, T.D., Batra, R., Chapman, J., Krishnan, S., Chen, L., Chandrasekaran, A., Ramprasad, R., \u201cA Universal Strategy for the Creation of Machine Learning-based Atomistic Force Fields<\/em><\/a>\u201d, npj Computational Materials, vol. 3, no. 1, September 2017 PDF<\/a><\/span><\/span><\/p>\n

2.<\/strong> Botu, V., Chapman, J., Ramprasad, R., \u201cA Study of Adatom Ripening on an Al (1 1 1) Surface with Machine Learning Force Fields<\/em><\/a>\u201d, Computational Materials Science, vol. 129, March 2017 PDF<\/a><\/span><\/span><\/span><\/p>\n

1.<\/strong> Botu, V., Batra, R., Chapman, J., Ramprasad, R., \u201cMachine Learning Force Fields: Construction, Validation, and Outlook<\/em><\/a>\u201d, Journal of Physical Chemistry C, vol. 121, no. 1, December 2016 PDF<\/a><\/o:p><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

For a complete list of publications that includes papers on the arXiv, please see Google Scholar 23. Tenorio, M., Md, H. R., Mannodi-Kanakkithodi, A., Chapman, J., “Out-of-distribution machine learning for materials discovery: challenges and opportunities”, Chemical Physics Reviews, March, 2026 PDF 22. Kwon, H., Hsu, T., Sun, W., Jeong, W., Aydin, F., Chapman, J., Chen, […]<\/p>\n","protected":false},"author":21170,"featured_media":0,"parent":0,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/pages\/17"}],"collection":[{"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/users\/21170"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/comments?post=17"}],"version-history":[{"count":50,"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/pages\/17\/revisions"}],"predecessor-version":[{"id":498,"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/pages\/17\/revisions\/498"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/mil\/wp-json\/wp\/v2\/media?parent=17"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}