Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session A17: Density Functional Theory in Chemical Physics IFocus Session
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Sponsoring Units: DCP Chair: Aurora Pribram-Jones, University of California, Merced Room: Room 209 |
Monday, March 6, 2023 8:00AM - 8:36AM |
A17.00001: Meta-generalized gradient approximations for quantum materials Invited Speaker: Adrienn Ruzsinszky Although meta-generalized gradient approximations (meta-GGAs) [1,2] have been available for the materials science community for years, these approximations have not been harnessed for electronic structure to the extent that their potential indicates. Some of these meta-GGAs like SCAN and r2SCAN have undoubtedly become very popular for ground state applications [3]. Meta-GGAs have the potential to bridge the gap between GGAs and hybrid density functionals. This opportunity has not been recognized and exploited far enough in materials science. Meta-GGA approximations exhibit an extra flexibility via the kinetic energy density ingredient and functions built upon this ingredient. Meta-GGAs are also implicit functionals of the electron density and explicit functionals of Kohn-Sham orbitals. Increasing spatial nonlocality was shown to be associated with the kinetic energy density ingredient [4]. Quantum materials span a broad platform for a theorist with challenges for both structure and electronic properties. The physical situations that occur in these materials go far beyond the reach of any GGA-level-only density functional. Phenomena such as charge density waves, band gaps and phase changes of topological materials require a framework with enough flexibility to capture all these physical situations. Within this talk I will present the recent evolution of some meta-GGA density functionals and highlight the relevance of their ingredients through tests and applications [5] involving quantum materials. |
Monday, March 6, 2023 8:36AM - 8:48AM |
A17.00002: Scaling up accurate density functional theory calculations with the embedded cluster density approximation Chen Huang The computational cost of Kohn-Sham density functional theory (KS-DFT) increases rapidly when advanced, orbital-based exchange-correlation (XC) energy functionals are used. To scale up such simulations, we have developed the embedded cluster density approximation (ECDA), which is a local correlation method formulated in the framework of DFT. In ECDA, for each atom, we select its nearby atoms to form a cluster. The rest of the system is the environment. The system's electron density is partitioned among the cluster and the environment, and the cluster's XC energy is calculated with an advanced XC energy functional. The cluster's XC energy is later projected onto its central atom. This procedure is performed for every atom in the system, and the total system's XC energy is obtained as the sum of these atomic XC energies. Since the clusters are defined by partitioning the electron density, rather than localizing the orbitals, ECDA is a nearly black-box local correlation method and is applicable to systems having various bond types, such as ionic, metallic, and covalent bonds. We demonstrate ECDA's performance on molecules, metals, and oxides. In these examples, the exact exchange is employed as the advanced XC energy functional. Another appealing feature of ECDA is that it is a variational method and the analytical forces can be derived. We expect ECDA to be a simple, yet effective local correlation method for scaling up advanced DFT simulations in the future. |
Monday, March 6, 2023 8:48AM - 9:00AM |
A17.00003: Performance of Dispersion-Inclusive Density Functional Theory Methods for Energetic Materials Dana O'Connor Molecular crystals of energetic materials (EMs) are denser than typical molecular crystals and are characterized by distinct intermolecular interactions. To assess the performance of dispersion-inclusive density functional theory (DFT) methods, we have compiled a data set of experimental sublimation enthalpies of 31 energetic materials. We evaluate the performance of three methods: the semilocal Perdew−Burke−Ernzerhof (PBE) functional coupled with the pairwise Tkatchenko-Scheffler (TS) dispersion correction, PBE with the many-body dispersion (MBD) method, and the PBE-based hybrid functional (PBE0) with MBD. Zero-point energy contributions and thermal effects are described using the quasi-harmonic approximation (QHA), including explicit treatment of thermal expansion, which we find to be non-negligible for EMs. The lattice energies obtained with PBE0+MBD are the closest to experimental sublimation enthalpies with a mean absolute error of 9.89 kJ/mol. However, the state-of-the-art treatment of vibrational and thermal contributions makes the agreement with experiment worse. Pressure−volume curves are also examined for six representative materials. For pressure−volume curves, all three methods provide reasonable agreement with experimental data with mean absolute relative errors of 3% or less. Most of the intermolecular interactions typical of EMs, namely nitro-amine, nitro−nitro, and nitro-hydrogen interactions, are more sensitive to the choice of the dispersion method than to the choice of the exchange-correlation functional. The exception is π−π stacking interactions, which are also very sensitive to the choice of the functional. Overall, we find that PBE+TS, PBE+MBD, and PBE0+MBD do not perform as well for energetic materials as previously reported for other classes of molecular crystals. This highlights the importance of testing dispersion-inclusive DFT methods for diverse classes of materials and the need for further method development. |
Monday, March 6, 2023 9:00AM - 9:12AM |
A17.00004: Symmetry Breaking with the SCAN Density Functional Describes Strong Correlation in the Singlet Carbon Dimer John P. P Perdew, Shah Tanvir ur Rahman Chowdhury, Chandra Shahi, Aaron D Kaplan, Duo Song, Eric J Bylaska Abstract: The SCAN (strongly constrained and appropriately normed) meta-generalized gradient approximation (meta-GGA), which satisfies all 17 exact constraints that a meta-GGA can satisfy, accurately describes equilibrium bonds that are normally correlated. With symmetry breaking, it also accurately describes some sd equilibrium bonds that are strongly correlated. While sp equilibrium bonds are nearly always normally correlated, the C2 singlet ground state is known to be a rare case of strong correlation in an sp equilibrium bond. Earlier work that calculated atomization energies of the molecular sequence B2, C2, O2, and F2 in the local spin density approximation (LSDA), the Perdew-Burke-Ernzerhof (PBE) GGA, and the SCAN meta-GGA, without symmetry breaking in the molecule, found that only SCAN was accurate enough to reveal an anomalous under-binding for C2. This work shows that spin symmetry breaking in singlet C2, the appearance of net up- and down-spin densities on opposite sides (not ends) of the bond, corrects that under-binding, with a small SCAN atomization-energy error more like that of the other three molecules, suggesting that symmetry-breaking with an advanced density functional might reliably describe strong correlation. This talk also discusses some general aspects of symmetry breaking, and the insights into strong correlation that symmetry-breaking can bring. |
Monday, March 6, 2023 9:12AM - 9:24AM |
A17.00005: Thermal and phase transition behavior of 2D quantum materials, enabled by machine-learned interatomic potentials Juan M Marmolejo-Tejada, Salvador Barraza-Lopez, Martin A Mosquera Two-dimensional (2D) quantum materials are expected to transform conventional electronics for a wide spectrum of applications. Here, we explore the combination of density functional theory (DFT) and machine-learned algorithmic training for the generation of moment-tensor potentials (MTPs) to model single-layer (1L) or bi-layer (2L) transition metal dichalcogenides (TMDs). First, we use the trained MTPs for predicting the thermal transport properties of 1L-MoS2/WS2 lateral heterostructures, showing that the thermal conductivity of 2D alloys is highly resilient to sulfur vacancies, and enabling the fine-tuning of material's thermal properties for heat management and energy storage and conversion applications. Furthermore, we employ our trained MTPs for studying the temperature-dependent phase transition dynamics of R-stacked 2L-TMDs, aiming to understand their paraelectric switching behavior. This is useful for modeling the ferroelectric properties of quantum systems that will be crucial components in the design and implementation of advanced electronic circuitry. |
Monday, March 6, 2023 9:24AM - 9:36AM |
A17.00006: Ab-initio Adaptive Density Embedding for Mesoscale Systems Xuecheng Shao, Michele Pavanello Density embedding [1] relies on a divide-and-conquer description of the electronic structure of large systems splitting them into smaller interacting subsystems. It is emerging as a powerful electronic structure method for large-scale simulations of molecular condensed phases and interfaces [2]. However, due to limitations of the employed non-additive density functionals, to date density embedding has been limited to approach weakly interacting subsystems. Additionally and more severely, when a single subsystem is very large (as in the case of interfaces of mesoscopic size), the computational cost is dominated by one of the large subsystems resulting in little overall gain compared to a full-fledged Kohn-Sham DFT simulation. We will show that these problems can be elegantly resolved. We devised an adaptive density embedding method prescribing subsystem merging/splitting events whenever subsystems interact too strongly/weakly redistributing work and data in an efficient way[3]. We will also show that by making judicious use of orbital-free DFT as a solver for metallic subsystems, mesoscopic molecule-metal interfaces can be modeled with an accuracy that is virtually identical to a Kohn-Sham DFT simulation of the supersystem[4]. The resulting object-oriented Python implementations[5] constitute a black-box, flexible and efficient electronic structure software for mesoscale systems. |
Monday, March 6, 2023 9:36AM - 10:12AM |
A17.00007: Simulating Raman spectroscopy of doped 2D materials Invited Speaker: David A Strubbe Doping 2D materials can tune their electronic, optical, magnetic, catalytic, and mechanical properties, but it is difficult to get conclusive evidence from experiments about where the dopants are located, particularly in multilayer or bulk materials. Raman spectroscopy can be a powerful tool for characterization, but help from simulations is needed to interpret the spectra in terms of dopant location in substitutional or intercalation sites. I will present our studies of Raman spectroscopy in Ni- and Re-doped MoS2, with density-functional perturbation theory. In Ni-doped MoS2, important for catalysis and lubrication, we find distinct fingerprints of the different doping sites, and analyze their origin in terms of activation of Raman-inactive modes, creation of new Ni-related modes, and shifts of existing modes [Guerrero et al., J. Phys. Chem. C 125, 13401 (2021)]. In Re-doped MoS2, important for electronics, we again find distinct characteristics of the doping sites, mostly in terms of shifts of the pristine Raman peaks. We show however that this peak shifts have a more complex origin than the simple ideas of strengthening or weakening of bonds as commonly used. Re-doped MoS2 is n-type and therefore has a metallic density of states, preventing usual approaches for Raman calculations based on the static dielectric constant. We overcome this problem by using atomic Raman tensors from the pristine material. Our benchmarks show this is a generally applicable method for Raman calculations of metallic doped materials [Guerrero and Strubbe, J. Phys. Chem. C https://doi.org/10.1021/acs.jpcc.2c03999 (2022)]. In an experimental collaboration, we used these results to identify the location of dopants in samples of Re-doped MoS2 that showed an unusual increase in nanoscale friction with the number of layers [Acikgoz et al., Nanotechnology 34, 015706 (2023)]. Finally, I will show some results from an undergraduate/graduate class project in which students calculated Raman spectra of MoS2xSe2(1-x) monolayer alloys. |
Monday, March 6, 2023 10:12AM - 10:24AM |
A17.00008: Quasiparticle band structures of halide double perovskites using Wannier-localized optimally tuned screened range separated hybrid functionals Francisca Sagredo, Stephen E Gant, Guy Ohad, Jonah B Haber, Marina R Filip, Leeor Kronik, Jeffrey B Neaton Halide double perovskites are a promising new class of materials that offer an alternative to lead halide perovskites as suitable materials to use for solar cell applications, due to their greater stability and reduced susceptibility to environmental factors. Previous calculations of the band gaps using semilocal density functionals and the GW approximation, in conjunction with the lack of experimental data available for these class of materials, has left room for ambiguity in predicting the correct fundamental band gaps of these systems. Here we use the new state of the art, Wannier-localized optimally tuned screened range separated hybrid (WOT-SRSH) functional which has recently been shown to be a promising approach for a range of standard semiconductors, insulators, and lead halide perovskites. We compare and discuss the band gaps, band structures, and optical absorption spectra for double perovskites we obtain with this method with ab initio many-body perturbation theory, prior calculations, and experiment. We also discuss the use of WOT-SRSH on other indirect gap materials. |
Monday, March 6, 2023 10:24AM - 10:36AM |
A17.00009: Explore semi-local non-interacting kinetic energy functional and non-additive non-interacting kinetic energy functional with neural network models Yuming Shi, Adam Wasserman The non-interacting kinetic energy (KE) functional of Density Functional Theory (DFT) has been for many decades an object of study for orbital-free DFT and density embedding methods. Due to its comparatively large magnitude and to its highly non-local dependence on the density, the non-interacting KE functional remains extremely challenging to approximate accurately as an explicit functional of the densities. One of the most successful approximations, especially for modeling solid metals and semiconductors, is non-local functional. However, recent calculations show that the meta-GGA level of approximation seems to be capable of yielding comparable accuracy. We explore the full potential of the meta-GGA form for the non-interacting KE functional by using neural network models with exact conditions enforced. Moreover, the non-additive non-interacting kinetic energy (NAKE), defined as the difference between the non-interacting KE of the entire system and the sum of the fragment kinetic energies, can be approximated as one separate quantity. We explore NAKE functionals designed for covalently-bonded fragments and fractional electrons in Partition-DFT. |
Monday, March 6, 2023 10:36AM - 10:48AM |
A17.00010: A van der Waals Density Functional for Molecular Crystals Trevor Jenkins, Timo Thonhauser, Kristian Berland Since the development of the first van der Waals density functional by Dion et. al., the modeling of non-local correlation has evolved to more accurately describe larger and more varied types of structures. The newest generation of the vdW-DF family, i.e. vdW-DF3, constructed new forms of the non-local correlation functional and exchange and achieved improved accuracy over past van der Waals density functionals in modeling molecular dimers, layered structures, and adsorption systems. However due to competing interests within the parametrization of the functional, only limited accuracy was achieved for molecular crystals. Here we offer a new, highly accurate molecular crystal functional that is the result of a twofold solution to vdW-DF3's shortcomings. To obtain accurate binding energies we make use of vdW-DF3's flexible form of the non-local correlation, vital for the effective modeling of long-range dispersion interactions. In order to achieve accurate geometries we have also created a new variety of exchange in the generalized gradient approximation, one that allows for close fitting with experimental data. This new functional, which we name vdW-DF3-mc, outperforms all previously designed van der Waals density functionals and even the dispersion correction DFT-D3 in tests on molecular crystal data sets such as the X23. We also discuss how our optimization procedure can be applied to other types of systems, offering a broad range of applications. |
Monday, March 6, 2023 10:48AM - 11:00AM |
A17.00011: Machine learning modeling of the self-assembly of one-dimensional nanostructures from two-dimensional MoS2 monolayers with defect and strain engineering Akram Ibrahim, Can Ataca The chalcogen point vacancies, ubiquitous in a wide range of two-dimensional (2D) transition-metal dichalcogenides (TMDs), are experimentally observed to agglomerate forming extended line defects. We show that a discrepancy in the density of defects between the two chalcogen sides of MoS2 monolayers can lead to spontaneous curling and further self-assembly of various 1D nanostructures such as nanotubes and nanoscrolls. The large length and time scales needed to simulate this process make the usage of density functional theory (DFT) unfeasible. Instead of empirical potentials which suffer from their low accuracies, we develop a neural network potential (NNP) to drive our simulations at a comparable cost to empirical potentials while retaining the quantum-mechanical accuracy of DFT. The NNP model is first used to run Monte Carlo (MC) simulations to identify the long-scale arrangements of vacancy defects at various vacancy concentrations. Then, NNP is utilized to run molecular dynamics (MD) simulations to model the self-assembly process. We provide a meticulous investigation of the effects of vacancy concentration and degree of strain on the self-assembly process. The usage of a machine learning potential helps to accurately approximate the experimental reality of the self-assembly process, which leads to a more accurate geometry of the formed 1D nanostructures to study their electronic and magnetic properties. |
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