Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory. - Manuel Aldegunde; James R. Kermode; Nicholas Zabaras (J. Comput. Phys., 2016)

Imeall: A computational framework for the calculation of the atomistic properties of grain boundaries. - Henry Lambert; Ádám Fekete; James R. Kermode; A. De Vita (Comput. Phys. Commun., 2018)

A Posteriori Error Estimate and Adaptivity for QM/MM Models of Crystalline Defects. - Yangshuai Wang; James R. Kermode; Christoph Ortner; Lei Zhang 027 (CoRR, 2022)

Massively parallel fitting of Gaussian approximation potentials. - Sascha Klawohn; James R. Kermode; Albert P. Bartók (Mach. Learn. Sci. Technol., 2023)

matscipy: materials science at the atomic scale with Python. - Petr Grigorev; Lucas Frérot; Fraser Birks; Adrien Gola; Jacek Golebiowski; Jan Grießer; Johannes L. Hörmann; Andreas Klemenz; Gianpietro Moras; Wolfram G. Nöhring; Jonas A. Oldenstaedt; Punit Patel; et al. (Zenodo, 2024)

matscipy: materials science at the atomic scale with Python. - Petr Grigorev; Lucas Frérot; Fraser Birks; Adrien Gola; Jacek Golebiowski; Jan Grießer; Johannes L. Hörmann; Andreas Klemenz; Gianpietro Moras; Wolfram G. Nöhring; Jonas A. Oldenstaedt; Punit Patel; et al. (J. Open Source Softw., 2024)

A DSMC-CFD coupling method using surrogate modelling for low-speed rarefied gas flows. - Giorgos Tatsios; Arun K. Chinnappan; Arshad Kamal; Nikos Vasileiadis; Stephanie Y. Docherty; Craig White; Livio Gibelli; Matthew K. Borg; James R. Kermode; Duncan A. Lockerby (J. Comput. Phys., 2025)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science. - Fraser Birks; Thomas D. Swinburne; James R. Kermode (CoRR, 2025)