For the class of compounds studied here, we find that both B3LYP and eDMFT reproduce the experiments quite well, with eDMFT doing best, in particular when comparing with the ARPES data. We also compare with available experimental angle-resolved photoemission spectroscopy (ARPES), inverse-photoemission spectroscopy, and with optical absorption. For a first application, we choose the target materials to be the binary transition metal oxides FeO, CoO, MnO, and NiO in their antiferromagnetic phase and present a head-to-head comparison of spectral properties as computed using the various methods. Here we introduce a novel paradigm in which a chosen set of beyond-DFT methods is systematically and uniformly tested on a chosen class of materials. It is thus of pressing interest to compare their accuracy as they apply to different categories of materials. Various methods going beyond density functional theory (DFT), such as DFT+U, hybrid functionals, meta-GGAs, GW, and DFT-embedded dynamical mean field theory (eDMFT), have been developed to describe the electronic structure of correlated materials, but it is unclear how accurate these methods can be expected to be when applied to a given strongly correlated solid. The acquisition and deployment of the Advanced Compute & Data at the Rutgers Discovery Informatics Institute was strategically made in three phases as described in this technical report. This project is a statewide resource that has far-reaching benefits to Rutgers and to the entire state impacting higher education institutions, industry and state government. To expand the capabilities and benefits that RDI2 can offer to students and researchers at Rutgers and at other institutions in the state, as well as industry statewide, RDI2 was awarded with a $10 million from the New Jersey Higher Education Equipment Leasing Fund to establish the Advanced Compute & Data at the Rutgers Discovery Informatics Institute. RDI2 uses advanced computation to address today's compute- and data-intensive grand challenges in science, technology, engineering, and mathematics. The Rutgers Discovery Informatics Institute (RDI2), New Jersey's Center for Advanced Computation, broadens access to state-of-the-art computing technology that enables large-scale "Big Data" analytics, computational modeling, and visualization, all of which are playing increasingly important roles in education, research and innovation.
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