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Disambiguation of "Srinivasan, Prashanth"

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Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems

, , , , , and . Dataset, (2023)Related to: Performance of two complementary machine-learned potentials in modelling chemically complex systems. npj. Comp. Mat.
DOI: 10.18419/darus-3516

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Ab initio vibrational free energies including anharmonicity for multicomponent alloys, , , , , , , and . NPJ Computational Materials, (2019)Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, , , and . Dataset, (2023)Related to: A. Forslund, J. H. Jung, P. Srinivasan, and B. Grabowski, "Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals" (submitted).High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, , , and . npj computational materials, (2023)Ab initio vibrational free energies including anharmonicity for multicomponent alloys, , , , , , , and . npj Comput. Mater., 5 (1): 80 (July 2019)Li5Sn, the Most Lithium-Rich Binary Stannide : A Combined Experimental and Computational Study, , , , , and . Journal of the American Chemical Society, 144 (16): 7096-7110 (2022)Performance of two complementary machine-learned potentials in modelling chemically complex systems, , , , , and . Npj Comput. Mater., (2023)Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems, , , , , and . Dataset, (2023)Related to: Performance of two complementary machine-learned potentials in modelling chemically complex systems. npj. Comp. Mat.Anharmonicity in bcc refractory elements : A detailed ab initio analysis, , , , and . Physical review. B, Condensed matter and materials physics, 107 (1): 014301 (2023)Frontiers in atomistic simulations of high entropy alloys, , , , , , and . Journal of applied physics, 128 (15): 150901 (2020)Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, , , and . Dataset, (2023)Related to: J. H. Jung, P. Srinivasan, A. Forslund, and B. Grabowski, "High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", npj Comput. Mater. 9, 3 (2023). doi: 10.1038/s41524-022-00956-8.