اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDFTpy: An efficient and object-oriented platform for orbital-free DFT simulations
466
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In silico materials design is hampered by the computational complexity of Kohn-Sham DFT, which scales cubically with the system size. Owing to the development of new-generation kinetic energy density functionals (KEDFs), orbital-free DFT (OFDFT, a linear-scaling method) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters. In this work, we present DFTpy, an open source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third-party modules, such as NumPy and SciPy. When fast simulations are in order, DFTpy exploits the fast Fourier transforms (FFTs) from PyFFTW. New-generation, nonlocal and density-dependent-kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds. We showcase DFTpy by solving for the electronic structure of a million-atom system of aluminum metal which was computed on a single CPU. The Python 3 implementation is object-oriented, opening the door to easy implementation of new features. As an example, we present a time-dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal cluster recovering qualitatively the time-dependent Kohn-Sham DFT result. The Python code base allows for easy implementation of APIs. We showcase the combination of DFTpy and ASE for molecular dynamics simulations (NVT) of liquid metals. DFTpy is released under the MIT license.
قيم البحث
اقرأ أيضاً
We present the One-orbital Ensemble Self-Consistent Field (OE-SCF) method, an {alternative} orbital-free DFT solver that extends the applicability of DFT to system sizes beyond the nanoscale while retaining the accuracy required to be predictive. OE-
SCF is an iterative solver where the (typically computationally expensive) Pauli potential is treated as an external potential and updated after each iteration. Because only up to a dozen iterations are needed to reach convergence, OE-SCF dramatically outperforms current orbital-free DFT solvers. Employing merely a single CPU, we carried out the largest ab initio simulation for silicon-based materials to date. OE-SCF is able to converge the energy of bulk-cut Si nanoparticles as a function of their diameter up to 16 nm, for the first time reproducing known empirical results. We model polarization and interface charge transfer when a Si slab is sandwiched between two metal slabs where lattice matching mandates a very large slab size. Additionally, OE-SCF opens the door to adopt even more accurate functionals in orbital-free DFT simulations while still tackling systems sizes beyond the nanoscale.
Classical density-functional theory provides an efficient alternative to molecular dynamics simulations for understanding the equilibrium properties of inhomogeneous fluids. However, application of density-functional theory to multi-site molecular fl
uids has so far been limited by complications due to the implicit molecular geometry constraints on the site densities, whose resolution typically requires expensive Monte Carlo methods. Here, we present a general scheme of circumventing this so-called inversion problem: compressed representations of the orientation density. This approach allows us to combine the superior iterative convergence properties of multipole representations of the fluid configuration with the improved accuracy of site-density functionals. Next, from a computational perspective, we show how to extend the DFT++ algebraic formulation of electronic density-functional theory to the classical fluid case and present a basis-independent discretization of our formulation for molecular classical density-functional theory. Finally, armed with the above general framework, we construct a simplified free-energy functional for water which captures the radial distributions, cavitation energies, and the linear and non-linear dielectric response of liquid water. The resulting approach will enable efficient and reliable first-principles studies of atomic-scale processes in contact with solution or other liquid environments.
The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of migration barriers in atomistic Kinetic Monte Carlo simulations. Accurat
e (high-fidelity) methods are often available, but since they are usually computationally expensive, they must be replaced by less accurate (low-fidelity) models that introduce some degrees of approximation. Machine-learning techniques such as artificial neural networks are usually employed to work around this limitation and extract the needed parameters from large databases of high-fidelity data, but the latter are often computationally expensive to produce. This work introduces an alternative method based on the multifidelity approach, where correlations between high-fidelity and low-fidelity outputs are exploited to make an educated guess of the high-fidelity outcome based only on quick low-fidelity estimations, hence without the need of running full expensive high-fidelity calculations. With respect to neural networks, this approach is expected to require less training data because of the lower amount of fitting parameters involved. The method is tested on the prediction of ab initio formation and migration energies of vacancy diffusion in iron-copper alloys, and compared with the neural networks trained on the same database.
Recent developments in path integral methodology have significantly reduced the computational expense of including quantum mechanical effects in the nuclear motion in ab initio molecular dynamics simulations. However, the implementation of these deve
lopments requires a considerable programming effort, which has hindered their adoption. Here we describe i-PI, an interface written in Python that has been designed to minimise the effort required to bring state-of-the-art path integral techniques to an electronic structure program. While it is best suited to first principles calculations and path integral molecular dynamics, i-PI can also be used to perform classical molecular dynamics simulations, and can just as easily be interfaced with an empirical forcefield code. To give just one example of the many potential applications of the interface, we use it in conjunction with the CP2K electronic structure package to showcase the importance of nuclear quantum effects in high pressure water.
Orbital-free density functional theory (OF-DFT) is a promising method for large-scale quantum mechanics simulation as it provides a good balance of accuracy and computational cost. Its applicability to large-scale simulations has been aided by progre
ss in constructing kinetic energy functionals and local pseudopotentials. However, the widespread adoption of OF-DFT requires further improvement in its efficiency and robustly implemented software. Here we develop a real-space finite-difference method for the numerical solution of OF-DFT in periodic systems. Instead of the traditional self-consistent method, a powerful scheme for energy minimization is introduced to solve the Euler--Lagrange equation. Our approach engages both the real-space finite-difference method and a direct energy-minimization scheme for the OF-DFT calculations. The method is coded into the ATLAS software package and benchmarked using periodic systems of solid Mg, Al, and Al$_{3}$Mg. The test results show that our implementation can achieve high accuracy, efficiency, and numerical stability for large-scale simulations.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
