No Arabic abstract
Coupled-cluster theory with single and double excitations (CCSD) is a promising ab initio method for the electronic structure of three-dimensional metals, for which second-order perturbation theory (MP2) diverges in the thermodynamic limit. However, due to the high cost and poor convergence of CCSD with respect to basis size, applying CCSD to periodic systems often leads to large basis set errors. In a common composite method, MP2 is used to recover the missing dynamical correlation energy through a focal-point correction, but the inadequacy of MP2 for metals raises questions about this approach. Here we describe how high-energy excitations treated by MP2 can be downfolded into a low-energy active space to be treated by CCSD. Comparing how the composite and downfolding approaches perform for the uniform electron gas, we find that the latter converges more quickly with respect to the basis set size. Nonetheless, the composite approach is surprisingly accurate because it removes the problematic MP2 treatment of double excitations near the Fermi surface. Using the method to estimate the CCSD correlation energy in the combined complete basis set and thermodynamic limits, we find CCSD recovers over 90% of the exact correlation energy at $r_s=4$. We also test the composite and downfolding approaches with the random-phase approximation used in place of MP2, yielding a method that is more effective but more expensive.
Electronic correlation energies from the random-phase approximation converge slowly with respect to the plane wave basis set size. We study the conditions, under which a short-range local density functional can be used to account for the basis set incompleteness error. Furthermore, we propose a one-shot extrapolation scheme based on the Lindhard response function of the homogeneous electron gas. The different basis set correction methods are used to calculate equilibrium lattice constants for prototypical solids of different bonding types.
Given the widespread use of density functional theory (DFT), there is an increasing need for the ability to model large systems (beyond 1,000 atoms). We present a brief overview of the large-scale DFT code Conquest, which is capable of modelling such large systems, and discuss approaches to the generation of consistent, well-converged pseudo-atomic basis sets which will allow such large scale calculations. We present tests of these basis sets for a variety of materials, comparing to fully converged plane wave results using the same pseudopotentials and grids.
A curious behavior of electron correlation energy is explored. Namely, the correlation energy is the energy that tends to drive the system toward that of the uniform electron gas. As such, the energy assumes its maximum value when a gradient of density is zero. As the gradient increases, the energy is diminished by a gradient suppressing factor, designed to attenuate the energy from its maximum value similar to the shape of a bell curve. Based on this behavior, we constructed a very simple mathematical formula that predicted the correlation energy of atoms and molecules. Combined with our proposed exchange energy functional, we calculated the correlation energies, the total energies, and the ionization energies of test atoms and molecules; and despite the unique simplicities, the functionals accuracies are in the top tier performance, competitive to the B3LYP, BLYP, PBE, TPSS, and M11. Therefore, we propose that, as guided by the simplicities and supported by the accuracies, the correlation energy is the energy that locally tends to drive the system toward the uniform electron gas.
Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to tackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys (SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by training the properties of transition metals (TMs). Furthermore, this model builds the structure-activity relationship of the adsorption energies on alloys from the perspective of machine learning, which reveals the role of the surface atoms valence, electronegativity and coordination and the adsorbates valence in determining the adsorption energies. This transferable scheme advances the understanding of the adsorption mechanism on alloys and the rapid design of alloy catalysts.
Liquid metals at extreme pressures and temperatures are widely interested in the high-pressure community. Based on density functional theory molecular dynamics, we conduct first-principles investigations on the equation of state (EOS) and structures of four metals (Cu, Fe, Pb, and Sn) at 1.5--5 megabar conditions and 5$times10^3$--4$times10^4$ K. Our first-principles EOS data enable evaluating the performance of four EOS models in predicting Hugoniot densities and temperatures of the four systems. We find the melting temperature of Cu is 1000--2000 K higher and shows a similar Clapeyron slope, in comparison to those of Fe. Our structure, coordination number, and diffusivity analysis indicates all the four liquid metals form similar simple close-packed structures. Our results set theoretical benchmarks for EOS development and structures of metals in their liquid states and under dynamic compression.