From the 1 of 6 linked papers with an AI index.
6 papers
Physics-informed Machine Learning Prediction of Hubbard Interaction Parameters
Jiyeon Kim, Indukuru Ramesh Reddy, Bongjae Kim +1
The paper develops machine‑learning models that predict cRPA‑derived Hubbard interaction parameters (U_eff, V, and J) for transition‑metal oxides, providing both accurate predictio…
Constrained Random Phase Approximation: the spectral method
Merzuk Kaltak, Alexander Hampel, Martin Schlipf +3
We present a constrained Random Phase Approximation (cRPA) method, termed spectral cRPA (s-cRPA), and compare it to established cRPA approaches for Scandium and Copper by varying t…
Correlation Effects on Magnetic Structure and Lattice Dynamics of LaMnO: A First-Principles Study
Haeyoon Jung, Indukuru Ramesh Reddy, Bongjae Kim +2
LaMnO, a quadruple perovskite oxide (AA'BO-type), has attracted attention for its notable bifunctional activity in oxygen evolution and reduction reaction…
Role of On-site and Inter-site Coulomb Interactions in KVSb: A first-principles DFT++ study
Indukuru Ramesh Reddy, Sayandeep Ghosh, Bongjae Kim +1
Nonlocal Coulomb interactions play a crucial role in stabilizing distinct electronic phases in kagome materials. In this work, we systematically investigate the effects of on-site…
Comparative analysis of methods for calculating Hubbard parameters using cRPA
Indukuru Ramesh Reddy, M. Kaltak, Bongjae Kim
In this study, we present a systematic comparison of various approaches within the constrained random-phase approximation (cRPA) for calculating the Coulomb interaction parameter $…
Graphene Straintronics by Molecular Trapping
Pawan Kumar Srivastava, Vedanki Khandelwal, Ramesh Reddy +2
Here, we report on controlling strain in graphene by trapping molecules at the graphene-substrate interface, leveraging molecular dipole moments. Spectroscopic and transport measur…