Juarez L. F. Da Silva

Bio: Juarez L. F. Da Silva is an academic researcher from University of São Paulo. The author has contributed to research in topics: Density functional theory & Adsorption. The author has an hindex of 41, co-authored 186 publications receiving 6436 citations. Previous affiliations of Juarez L. F. Da Silva include National Renewable Energy Laboratory & Forschungszentrum Jülich.

Hybrid functionals applied to rare-earth oxides: The example of ceria

Abstract: We report periodic density functional theory (DFT) calculations for ${\mathrm{CeO}}_{2}$ and ${\mathrm{Ce}}_{2}{\mathrm{O}}_{3}$ using the Perdew-Burke-Ernzerhof (PBE0) and Heyd-Scuseria-Ernzerhof (HSE) hybrid functionals that include nonlocal Fock exchange. We study structural, electronic, and magnetic ground state properties. Hybrid functionals correctly predict ${\mathrm{Ce}}_{2}{\mathrm{O}}_{3}$ to be an insulator as opposed to the ferromagnetic metal predicted by the local spin density (LDA) and generalized gradient (GGA) approximations. The equilibrium volumes of both structures are in very good agreement with experiments, improving upon the description of the LDA and GGA. The calculated ${\mathrm{CeO}}_{2}$ (O $2p$--Ce $5d$) and ${\mathrm{Ce}}_{2}{\mathrm{O}}_{3}$ $(\mathrm{Ce}\phantom{\rule{0.3em}{0ex}}4f\text{\ensuremath{-}}5d4f)$ band gaps are larger by up to 45% (PBE0) and 15% (HSE) than found in experiments. Furthermore, we calculate atomization energies, heats of formation, and the reduction energy of $2{\mathrm{CeO}}_{2}\ensuremath{\rightarrow}{\mathrm{Ce}}_{2}{\mathrm{O}}_{3}+(1∕2){\mathrm{O}}_{2}$. The latter is underestimated by $\ensuremath{\sim}0.4--0.9\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ with respect to available experimental data at room temperature. We compare our results with the more traditional DFT+$U$ (LDA$+U$ and PBE$+U$) approach and discuss the role played by the Hubbard $U$ parameter.

Nature of the Band Gap of In2O3 Revealed by First-Principles Calculations and X-Ray Spectroscopy

Abstract: Bulk and surface sensitive x-ray spectroscopic techniques are applied in tandem to show that the valence band edge for In2O3 is found significantly closer to the bottom of the conduction band than expected on the basis of the widely quoted bulk band gap of 3.75 eV. First-principles theory shows that the upper valence bands of In2O3 exhibit a small dispersion and the conduction band minimum is positioned at Gamma. However, direct optical transitions give a minimal dipole intensity until 0.8 eV below the valence band maximum. The results set an upper limit on the fundamental band gap of 2.9 eV.

Density-functional calculations of the structure of near-surface oxygen vacancies and electron localization on CeO2(111).

Abstract: One of the most topical issues surrounding oxygen vacancies on CeO2(111) is the relative stability of surface and subsurface defects. Using density-functional theory (DFT) with the HSE06 (Heyd-Scuseria-Ernzerhof) hybrid functional as well as the DFT+U approach (where U is a Hubbard-like term describing the on-site Coulomb interactions), we find subsurface vacancies with (2x2) periodicity to be energetically more favorable by 0.45 (HSE06), 0.47 [PBE+U (Perdew-Burke-Ernzerhof functional)], and 0.22 eV [LDA+U (local density approximation)]. The excess electrons localize not on Ce ions which are the nearest neighbor to the defect as priorly suggested, but instead on those that are next-nearest neighbors. The excess-electron distribution and the preference for subsurface vacancies are explained in terms of defect-induced lattice relaxation effects.

Origins of band-gap renormalization in degenerately doped semiconductors

Abstract: Degenerate n-type doping of semiconductors results in optical band-gap widening through occupation of the conduction band, which is partially offset by the so-called band-gap renormalization From investigation of the magnitude and origin of these shifts through density-functional band-structure theory, we demonstrate that the key contribution to renormalization arises from the nonparabolic nature of the host conduction band but not the rigid shift of the band edges, as is the current paradigm Furthermore, the carrier dependence of the band-gap widening is highly sensitive to the electronic states of the dopant ion, which can be involved in a significant reconstruction of the lower conduction band

Theoretical description of carrier mediated magnetism in cobalt doped ZnO.

Abstract: Substitutional cobalt in ZnO has a weak preference for antiferromagnetic ordering Stabilization of ferromagnetism is achieved through $n$-type doping, which can be understood through a band coupling model However, the description of the transition to a ferromagnetic ground state varies within different levels of band theory; issues arise due to the density functional theory underestimation of the band gap of ZnO, and the relative position of the nominally unfilled Co ${t}_{2d}$ states We examine these limitations, including approaches to overcome them, and explain the contradictions in previous studies, which drastically overestimate the doping threshold for magnetic ordering

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Recent Advances in Electrocatalysts for Oxygen Reduction Reaction

Abstract: The recent advances in electrocatalysis for oxygen reduction reaction (ORR) for proton exchange membrane fuel cells (PEMFCs) are thoroughly reviewed. This comprehensive Review focuses on the low- and non-platinum electrocatalysts including advanced platinum alloys, core–shell structures, palladium-based catalysts, metal oxides and chalcogenides, carbon-based non-noble metal catalysts, and metal-free catalysts. The recent development of ORR electrocatalysts with novel structures and compositions is highlighted. The understandings of the correlation between the activity and the shape, size, composition, and synthesis method are summarized. For the carbon-based materials, their performance and stability in fuel cells and comparisons with those of platinum are documented. The research directions as well as perspectives on the further development of more active and less expensive electrocatalysts are provided.

Ab-initio simulations of materials using VASP: Density-functional theory and beyond.

Abstract: During the past decade, computer simulations based on a quantum-mechanical description of the interactions between electrons and between electrons and atomic nuclei have developed an increasingly important impact on solid-state physics and chemistry and on materials science—promoting not only a deeper understanding, but also the possibility to contribute significantly to materials design for future technologies. This development is based on two important columns: (i) The improved description of electronic many-body effects within density-functional theory (DFT) and the upcoming post-DFT methods. (ii) The implementation of the new functionals and many-body techniques within highly efficient, stable, and versatile computer codes, which allow to exploit the potential of modern computer architectures. In this review, I discuss the implementation of various DFT functionals [local-density approximation (LDA), generalized gradient approximation (GGA), meta-GGA, hybrid functional mixing DFT, and exact (Hartree-Fock) exchange] and post-DFT approaches [DFT + U for strong electronic correlations in narrow bands, many-body perturbation theory (GW) for quasiparticle spectra, dynamical correlation effects via the adiabatic-connection fluctuation-dissipation theorem (AC-FDT)] in the Vienna ab initio simulation package VASP. VASP is a plane-wave all-electron code using the projector-augmented wave method to describe the electron-core interaction. The code uses fast iterative techniques for the diagonalization of the DFT Hamiltonian and allows to perform total-energy calculations and structural optimizations for systems with thousands of atoms and ab initio molecular dynamics simulations for ensembles with a few hundred atoms extending over several tens of ps. Applications in many different areas (structure and phase stability, mechanical and dynamical properties, liquids, glasses and quasicrystals, magnetism and magnetic nanostructures, semiconductors and insulators, surfaces, interfaces and thin films, chemical reactions, and catalysis) are reviewed. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008

World energy outlook

Abstract: This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.