Accurate prediction of the properties of materials using the CAM-B3LYP Density Functional
Musen Li, Jeffrey R. Reimers, Michael J. Ford, Rika Kobayashi, and, Roger D. Amos

TL;DR
This paper introduces an implementation of the CAM-B3LYP density functional within VASP, demonstrating improved accuracy in predicting material properties and showing competitiveness with advanced ab initio methods.
Contribution
The authors developed and validated a CAM-B3LYP implementation in VASP, enhancing predictive accuracy for materials and enabling comparisons with G0W0 and BSE approaches.
Findings
CAM-B3LYP reduces mean-absolute deviations significantly for lattice parameters and band gaps.
The implementation shows comparable results to G0W0 and BSE methods.
Validated against molecular data with low errors in reaction energies and bond lengths.
Abstract
Density functionals with asymptotic corrections to the long-range potential provide entry-level methods for calculations on molecules that can sustain charge transfer, but similar applications in Materials Science are rare. We describe an implementation of the CAM-B3LYP range-separated functional within the Vienna Ab-initio Simulation Package (VASP) framework, together with its analytical functional derivatives. Results obtained for eight representative materials: aluminum, diamond, graphene, silicon, NaCl, MgO, 2D h-BN and 3D h-BN, indicate that CAM-B3LYP predictions embody mean-absolute deviations (MAD) compared to HSE06 that are reduced by a factor of 6 for lattice parameters, 4 for quasiparticle band gaps, 3 for the lowest optical excitation energies, and 6 for exciton binding energies. Further, CAM-B3LYP appears competitive compared to ab initio G0W0 and Bethe-Salpeter equation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Quantum Dots Synthesis And Properties
