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At least 217 records · Page 12

Density Functional Theory Study of Iron–Oxygen Divacancies in Magnetite (Fe 3 O 4 ) and Hematite (Fe 2 O 3 )

Density functional theory (DFT) calculations are employed to investigate the formation energies, charge redistribution, and binding energies of iron–oxygen divacancies in magnetite (Fe 3 O 4 ) and hematite (Fe 2 O 3 ). For magnetite, we focus on the low-temperature phase to explore variations with local environments. Building on previous DFT calculations of the variations in formation energies for oxygen vacancies with local charge and spin order in magnetite, we extend this analysis to include octahedral iron vacancies before analyzing the iron–oxygen divacancies. We also assessed the relative stability of iron–oxygen divacancies by comparing their formation energies with those of individual vacancies. Our findings reveal a significant energetic driving force for the formation of divacancy clusters, particularly in magnetite, where divacancies in the +1 charge state exhibit formation energies comparable to those of neutral iron vacancies under oxidizing conditions. In hematite, the results indicate a strong tendency for oxygen vacancies to bind to iron vacancies. These results highlight the significance of iron–oxygen vacancy complexes in the transport properties of iron oxides, with particular relevance to diffusion mechanisms under irradiation conditions.

36 MATERIALS SCIENCE↗

Electron dynamics in extended systems within real-time time-dependent density-functional theory

Due to a beneficial balance of computational cost and accuracy, real-time time-dependent density-functional theory has emerged as a promising first-principles framework to describe electron real-time dynamics. Here we discuss recent implementations around this approach, in particular in the context of complex, extended systems. Results include an analysis of the computational cost associated with numerical propagation and when using absorbing boundary conditions. Here we extensively explore the shortcomings for describing electron–electron scattering in real time and compare to many-body perturbation theory. Modern improvements of the description of exchange and correlation are reviewed. In this work, we specifically focus on the Qb@ll code, which we have mainly used for these types of simulations over the last years, and we conclude by pointing to further progress needed going forward.

2D materials↗

Using density functional theory to construct multiphase equations of state: tin as an example

We present a general methodology for using density functional theory (DFT) calculations as a basis for constructing multiphase equations of state (EOS). Focusing on tin as an example, we discuss the full process of generating a tabular EOS, starting from first-principles calculations and arriving at a full EOS in agreement with existing experimental data. We begin by describing DFT calculations for the five solid phases and liquid phase of tin, including cold curve, phonon, and DFT-based molecular dynamics calculations. We then discuss methods for incorporating DFT results into materials models used in OpenSesame, the program used to create a full tabular multiphase EOS. Next, we outline a general strategy for adjusting model parameters in OpenSesame to fit to a variety of experimental data, including isobaric data, isothermal data, shock data, solid-solid phase boundary measurements, and measurements of the melt curve. We end with a discussion of the advantages of using DFT data as a foundation for EOS generation and discuss potential strategies for automated EOS generation based on the fitting process highlighted in this work.

74 ATOMIC AND MOLECULAR PHYSICS↗

Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex

The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity.

3DRISM↗

Connecting relativistic density functional theory to microscopic calculations

The development of systematic effective field theories (EFTs) for nuclear forces and advances in solving the nuclear many-body problem have greatly improved our understanding of dense nuclear matter and the structure of finite nuclei. For global nuclear calculations, density functional theories (DFTs) have been developed to reduce the complexity and computational cost required in describing nuclear systems. However, DFT often makes approximations and assumptions about terms included in the functional, which may introduce systematic uncertainties compared to microscopic calculations using EFTs. In this work, we investigate possible avenues of improving nuclear DFT using nonlinear relativistic mean-field (RMF) theory. We explore the impact of RMF model extensions by fitting the nonlinear RMF model to predictions of nuclear matter and selected closed-shell nuclei using four successful chiral EFT Hamiltonians. We find that these model extensions are impactful and important in capturing the physics present within chiral Hamiltonians, particularly for charge radii and neutron skins of closed-shell nuclei. However, there are additional effects that are not captured within the RMF model, particularly within the isoscalar sector of RMF theory. Additional model extensions and the reliability of the nonlinear RMF model are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Competitive effects of glucan’s main hydrolysates on biochar formation: A combined experiment and density functional theory analysis

We report the complexity of polysaccharide hydrothermal products increases the difficulty of exploring the formation of biochar, limiting the development of biochar. This work clarifies the completive effects of glucan’s main hydrolysates on biochar formation from three aspects: experimental, thermodynamic, and kinetic. The products distribution illustrates that 5-HMF, FA, and LA are mainly involved in the formation of biochar. Biochar mainly includes furan ring, ether group, and ester group by the analysis of magic-angle-spinning nuclear magnetic resonance, X-ray photoelectron spectroscopy, and elemental analysis. Combined experiments and density functional theory analysis, the etherification reaction of 5-HMF itself is most likely to occur and is key to form biochar, followed by the esterification of FA with 5-HMF, and then the etherification of 5-HMF and LA. The further verified experiments also manifest these results. This work will develop a foundation for exploring the complex formation mechanism of cellulose-based biochar.

59 BASIC BIOLOGICAL SCIENCES↗

Design guidance for ferrites: Insights from density functional theory on magnetic properties

A grand challenge in materials research is identifying the relationship between composition and performance. Herein, we explore this relationship for magnetic properties, specifically magnetic saturation (M s ) and magnetocrystalline anisotropy energy (K) of ferrites. Ferrites are materials derived from magnetite (chemical formula = Fe 3 O 4 ) that comprise metallic elements such as Fe, Mn, Ni, Co, Cu and Zn. Experimentally, synthesizing and characterizing ferrites is time consuming. Further, selection of compositions to achieve particular magnetic properties currently relies on intuition. To address this, in this work, density functional theory (DFT) is used to predict M s and K for 571 ferrite structures. These structures are primarily double-substituted non-stoichiometric ferrites with formulae M1 x M2 y Fe 3–x–y O 4 , where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn and 0 ≤ x ≤ 1 and y = 1–x. Calculated magnetic properties for the structures in this study vary from 0.04 × 10 5 to 9.6 × 10 5 A m −1 for M s and from 0.02 × 10 5 to 14.08 × 10 5 J m −3 for K. All structures are made publicly available in a FAIR database.

36 MATERIALS SCIENCE↗

Covariant Density Functional Theory in Nuclear Physics and Astrophysics

How does subatomic matter organize itself? Neutron stars are cosmic laboratories uniquely poised to answer this fundamental question that lies at the heart of nuclear science. Newly commissioned rare isotope facilities, telescopes operating across the entire electromagnetic spectrum, and ever more sensitive gravitational wave detectors will probe the properties of neutron-rich matter with unprecedented precision over an enormous range of densities. A coordinated effort between observation, experiment, and theoretical research is of paramount importance for realizing the full potential of these investments. Theoretical nuclear physics provides valuable insights into the properties of neutron-rich matter in regimes that are not presently accessible to experiment or observation. In particular, nuclear density functional theory is likely the only tractable framework that can bridge the entire nuclear landscape by connecting finite nuclei to neutron stars. This compelling connection is the main scope of the present review.

79 ASTRONOMY AND ASTROPHYSICS↗

Position-specific isotope effects during alkaline hydrolysis of 2,4-dinitroanisole resolved by compound-specific isotope analysis, 13 C NMR, and density-functional theory

Compound-specific isotope analysis (CSIA), position-specific isotope analysis (PSIA), and computational modeling (e.g., quantum mechanical models; reactive-transport models) are increasingly being used to monitor and predict biotic and abiotic transformations of organic contaminants in the field. However, identifying the isotope effect(s) associated with a specific transformation remains challenging in many cases. We describe and interpret the position-specific isotope effects of C and N associated with a SN 2 Ar reaction mechanism by a combination of CSIA and PSIA using quantitative 13 C nuclear magnetic resonance spectrometry, and density-functional theory, using 2,4-dinitroanisole (DNAN) as a model compound. The position-specific 13 C enrichment factor of O–C 1 bond at the methoxy group attachment site (ε C1 ) was found to be approximately -41‰, a diagnostic value for transformation of DNAN to its reaction products 2,4-dinitrophenol and methanol. Theoretical kinetic isotope effects calculated for DNAN isotopologues agreed well with the position-specific isotope effects measured by CSIA and PSIA. This combination of measurements and theoretical predictions demonstrates a useful tool for evaluating degradation efficiencies and/or mechanisms of organic contaminants and may promote new and improved applications of isotope analysis in laboratory and field investigations.

13C NMR↗

Radical Coupling Reactions of Hydroxystilbene Glucosides and Coniferyl Alcohol: A Density Functional Theory Study

The monolignols, p-coumaryl, coniferyl, and sinapyl alcohol, arise from the general phenylpropanoid biosynthetic pathway. Increasingly, however, authentic lignin monomers derived from outside this process are being identified and found to be fully incorporated into the lignin polymer. Among them, hydroxystilbene glucosides, which are produced through a hybrid process that combines the phenylpropanoid and acetate/malonate pathways, have been experimentally detected in the bark lignin of Norway spruce (Picea abies). Several interunit linkages have been identified and proposed to occur through homo-coupling of the hydroxystilbene glucosides and their cross-coupling with coniferyl alcohol. In the current work, the thermodynamics of these coupling modes and subsequent rearomatization reactions have been evaluated by the application of density functional theory (DFT) calculations. The objective of this paper is to determine favorable coupling and cross-coupling modes to help explain the experimental observations and attempt to predict other favorable pathways that might be further elucidated via in vitro polymerization aided by synthetic models and detailed structural studies.

59 BASIC BIOLOGICAL SCIENCES↗

Time-dependent density-functional theory of high-order harmonic generation from noble-gas atoms driven by orthogonally polarized two-color laser fields

An orthogonally polarized two-color (OTC) field of frequency ratio 1 : 2 is known to generate both odd and even harmonic spectra in the mutually orthogonal directions of the fundamental and the second-harmonic fields, respectively. Recollision angles of an ionized electron driven back to its parent ion by an OTC field are inferred from the intensity ratio of odd and even harmonics, where the relative phase delays between the two-color fields can be used in time-resolved measurements on a subcycle time scale. Here, we present the ab initio, many-electron calculation of time-resolved recollision angles of noble-gas atoms driven by an OTC field, based on the time-dependent density-functional theory. Our results agree well with experimental data by Shafir et al. in Nat. Phys. 5, 412 (2009) of helium atom and New J. Phys. 12, 073032 (2010) of neon atom.

74 ATOMIC AND MOLECULAR PHYSICS↗

Discovery of single-atom alloy catalysts for CO 2 -to-methanol reaction by density functional theory calculations

The transformations of CO 2 molecules into valuable products are of increasing interest due to the negative impact of anthropogenic CO 2 emissions on global warming. The CO 2 -to-methanol hydrogenation is an economically profitable reaction of carbon fixation, but it still steps away from widespread industrialization because of the lack of efficient and selective catalysts. Recently, single-atom alloy (SAA) catalysts have been developed to work remarkably in CO 2 hydrogenation reactions. Doping isolated single atoms into metallic catalyst can dramatically alter the catalytic performance of the host. Here, we have performed a screening discovery on Ru and 6 RuX (X = Fe, Co, Ni, Cu, Ir and Pt) SAAs using density functional theory (DFT) computations. We considered 13 possible elementary reactions in 4 possible reaction pathways on Ru and all RuX surfaces. In the computed mechanisms, we found that the formation of *H 2 COOH and *HCOO intermediates plays a critical role in determining catalysts’ activities. Doping Co and Pt isolated single atoms into Ru surface can thermodynamically and kinetically facilitate these intermediates formation processes, eventually promoting the production of methanol. The combination of weak binding and enhanced charge redistribution on RuCo and RuPt surfaces give them improved catalytic activities over pure Ru. This work will ultimately facilitate the discovery and development of SAAs for CO 2 to methanol, serving as guidance to experiments and theoreticians alike.

25 ENERGY STORAGE↗

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Time-dependent density-functional theory study on nonlocal electron stopping for inertial confinement fusion

Understanding laser–target coupling is of the utmost importance for achieving high performance in laser-direct-drive (LDD) inertial confinement fusion (ICF) experiments. Thus, accurate modeling of electron transport and deposition through ICF-relevant materials and conditions is necessary to quantify the total thermal conduction and ablation. The stopping range is a key transport quantity used in thermal conduction models; in this work, we review the overall role that the electron mean free path (MFP) plays in thermal conduction and hydrodynamic simulations. The currently used modified Lee–More model employs various physics approximations. We discuss a recent model that uses time-dependent density functional theory (TD-DFT) to eliminate these approximations in both the calculation of the electron stopping power and corresponding MFP in conduction zone polystyrene (CH) plasma. In general, the TD-DFT calculations showed a larger MFP (lower stopping power) than the standard modified Lee–More model. Using the TD-DFT results, an analytical model for the electron deposition range, λTD−DFT(ρ,T,K), was devised for CH plasmas between ρ=[0.05−1.05] g/cm3, kBT=[100−1000] eV. We implemented this model into LILAC, for simulations of a National Ignition Facility-scale LDD implosion and compared key physics quantities to ones obtained by simulations using the standard model. The implications of the obtained results and the path moving forward to calculate this same quantity in conduction-zone deuterium–tritium plasmas are further discussed, to hopefully close the understanding gap for laser target coupling in LDD-ICF simulations.

36 MATERIALS SCIENCE↗

In-situ Synthesis of PuCl 3 and Corresponding Raman and Density Functional Theory Vibrational Modes

The experimental and calculated Raman spectrum of PuCl 3 has been reported for the first time. PuCl 3 is a primary species found in plutonium metal refinement, specifically in pyrochemical salt processes including multicycle direct oxide reduction, metal chlorination, and electrorefining. As such, Raman signatures of PuCl 3 could serve as potential forensic indicators of material process history. A novel technique for synthesizing PuCl 3 from the in situ chlorination of plutonium metal with HCl was developed to establish these signatures. Cerium metal surrogates were utilized to ensure optimization of the plutonium experiments and to minimize personnel exposure, and all experiments were carried out in a Raman reaction chamber designed for air-tight, high vacuum environments. In situ Raman spectroscopy was employed in conjunction with density functional theory (DFT) to investigate the vibrational modes of PuCl 3 . Associated mixed oxy and hydroxyl phases are also reported. The combined Raman and DFT results have eliminated inconsistencies in Raman mode assignments for the MX 3 family of metal chlorides having P6 3 /m symmetry, and IR modes derived from the DFT calculations are additionally presented. The data observed in this study are of potential interest to nuclear forensic analyses, nuclear sample aging, nuclear energy, plutonium processing, and stockpile stewardship.

36 MATERIALS SCIENCE↗

Time-Dependent Density Functional Theory Description of 238 U⁡(n,f), 240,242 Pu⁢(n,f), and 237 Np(n,f) Reactions

In nuclei with an odd nucleon number the nonvanishing spin number density is the source of a pseudomagnetic field, which favors the splitting of the nucleon Cooper pairs. Such a pseudomagnetic field is generated always in the dynamics of any nucleus, but its effects on Cooper pairs are significantly enhanced in the dynamic evolution of nuclei with an odd number of nucleons. We present for the first time a microscopic study of the induced fission of the odd neutron compound nuclei 239 U, 241,243 Pu, and the odd proton, odd neutron compound nucleus 238 Np, performed within the time-dependent density functional theory extended to superfluid fermion systems, without any simplifying assumptions, with controlled numerical approximations, and for a very large number of initial conditions. Because of the presence of the unpaired odd nucleon(s), the time-reversal symmetry of the fission compound nucleus is spontaneously broken, an aspect routinely neglected in the most advanced microscopic approaches of the past. The emerging fission fragment properties are quite similar to the properties of fission fragments of neighboring even-even nuclei. The time from saddle-to-scission is often significantly longer in odd-odd or odd-mass nuclei than for even-even nuclei, since systems with unpaired nucleons are easier to excite and the potential energy surfaces of these nuclei have more structure, often resembling a very complicated obstacle course, rather than a more direct evolution of the nuclear shape from the top of the outer fission barrier to the scission configuration. The Pauli blocking approximation, often invoked in the literature, expected to inhibit the fission of nuclei with unpaired nucleons, is surprisingly strongly violated during the fission dynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗