2,374 research outputs found
Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening
Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting
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COMPUTATIONAL STUDIES OF STRUCTURE–FUNCTION RELATIONSHIPS OF SUPPORTED AND UNSUPPORTED METAL NANOCLUSTERS
Fuel cells have been demonstrated to be promising power generation devices to address the current global energy and environmental challenges. One of the many barriers to commercialization is the cost of precious catalysts needed to achieve sufficient power output. Platinum-based materials play an important role as electrocatalysts in energy conversion technologies. In order to improve catalytic efficiency and facilitate rational design and development of new catalysts, structure–function relationships that underpin catalytic activity must be understood at a fundamental level.
First, we present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2-1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based Genetic Algorithm (GA) and DFT modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly non-monotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement and we propose the use of an additional descriptor – the fully-frozen adsorption energy – that is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general.
Thereafter, to understand support effects on the activity of Pt nanoclusters, we employ a combination of empirical potential simulations and DFT calculations to investigate structure–function relationships of small PtN (N = 2-80) clusters on model carbon (graphene) supports. A bond-order empirical potential is employed within a GA to go beyond local optimizations in obtaining minimum-energy structures of PtN clusters on pristine as well as defective graphene supports. Point defects in graphene strongly anchor Pt clusters and also appreciably affect the morphologies of small clusters, which are characterized via various structural metrics such as the radius of gyration, average bond length, and average coordination number. A key finding from the structural analysis is that the fraction of potentially active surface sites in supported clusters is maximized for stable Pt clusters in the size range of 20-30 atoms, which provides a useful design criterion for optimal utilization of the precious metal. Through selected ab initio studies, we find a consistent trend for charge transfer from small Pt clusters to defective graphene supports resulting in the lowering of the cluster d-band center, which has implications for the overall activity and poisoning of the catalyst. The combination of a robust empirical potential-based GA for structural optimization with ab initio calculations opens up avenues for systematic studies of supported catalyst clusters at much larger system sizes than are accessible to purely ab initio approaches.
Finally, we present a self-consistent charge density-functional tight-binding (SCC-DFTB) parameterization for PtRu alloys, which is developed by employing a training set of alloy cluster energies and forces obtained from Kohn-Sham DFT calculations. Extensive simulations of a testing set of PtRu alloy nanoclusters show that this SCC-DFTB scheme is capable of capturing cluster formation energies with high accuracy relative to DFT calculations. The new SCC-DFTB parameterization is employed within a GA to search for global minima of PtRu clusters in the range of 13-81 atoms and the emergence of Ru-core/Pt-shell structures at intermediate alloy compositions is systematically demonstrated. Our new SCC-DFTB parameterization enables computationally inexpensive modeling and exploration of structure–function relationships for Pt-Ru clusters that are among the best-performing catalysts in numerous energy applications
Structure and Catalytic Properties of Ultra-Small Ceria Nanoparticles
Cerium dioxide (ceria) is an excellent catalytic material due to its ability to both facilitate oxidation/reduction reactions as well as store/release oxygen as an oxygen buffer. The traditional approach to assess and improve ceria\u27s catalytic behavior focuses on how efficiently O-vacancies can be generated and/or annihilated within the material, and how to extend established understandings of bulk ceria to further explain the greatly enhanced catalytic behavior of ultra-small ceria nanoparticles (uCNPs) with sizes less than 10 nm. Here, using density functional theory (DFT) calculations, we reexamine the atomic and electronic structures of uCNPs, especially their surface configurations. A unique picture dissimilar to the traditional point of view emerges from these calculations for the surface structure of uCNPs. uCNPs similar to those obtained by experimental synthesis and applied in catalytic environments exhibit core-shell like structures overall, with under-stoichiometric, reduced CNP cores and over-stoichiometric, oxidized surface shell constituted by various surface functional groups, e.g.,-Ox and/or -OH surface groups. Therefore, their catalytic behavior is dominated by surface chemistry rather than O-vacancies. Based on this finding, reaction pathways of two prevalent catalytic reactions, namely CO oxidation and the water-gas shift reaction over uCNPs are systematically investigated. Combined, these results demonstrate an alternative understanding of the surface structure of uCNPs, and provide new avenues to explore and enhance their catalytic behavior, which is likely applicable to other transition metal oxide nanoparticles with multivalent ions and very small sizes
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Atomic structures and properties of oxide interfaces
This thesis uses computational approaches, mainly first-principles methods, to study interfaces in oxide thin films. One of the difficulties in interface studies is the lack of definitive atomistic models, yet they are essential input for any calculations. Here, this problem is tackled by ab initio random structure searching (AIRSS), or more broadly speaking, random structure searching (RSS). The initial work studies the interfaces in vertically aligned nanocomposites (VANs) that consist of CeO₂ pillars embedded in a SrTiO₃ matrix. Enhanced ionic conductivity has been found in these VANs in prior studies, but the role of vertical interfaces is not explained. The initial interface searches are performed with interatomic potentials due to the large size of the interface, followed by refinement first-principles calculations. Based on the obtained structures, it is shown that the majority interfaces are unlikely to directly enhance ionic conductivity. However, a parallel solid-state O¹⁷ NMR study by our collaborators later obtained interface signals that suggest fast ionic conduction. First-principles NMR calculations show the observed signals are not consistent with the majority interface initially studied; instead, they can be assigned to the minority interfaces that are in different orientations.
The following work studies the planar interfaces between epitaxial films of CeO₂ and STO substrates. A significant amount of research has been devoted to fluorite-perovskite interfaces since the controversial report of colossal ionic conductivity enhancement in YSZ/STO heterostructures. However, the exact atomic structures of these interfaces are not well understood. AIRSS is used for finding stable CeO₂/STO planar interfaces taking account of different terminations and local stoichiometries. When the STO terminates with a TiO₂ layer, a rock salt structured CeO layer emerges at the interface. On the other hand, with SrO termination, the stable structure contains a partially occupied anion lattice, which gives rise to lateral diffusion of oxygen anions in molecular dynamics simulations. In both cases, the interfaces are found to attract oxygen vacancies, which hinders ionic transport in the perpendicular direction.
The subsequent work starts with addressing the perovskite-perovskite interfaces between La₀.₉Ba₀.₁MnO₃ (LBMO) and STO. LBMO is a ferromagnetic insulator with a relatively high ferromagnetic transition temperature, which makes it an ideal material for spintronics applications. However, thin films of LBMO are conductive except when the thickness is less than eight unit cells. This has been attributed to the octahedral proximity effects, as electron microscopy reveals that octahedral tilting in LBMO is suppressed near the interfaces. Whist some experimental observations are successfully accounted for by the first-principles calculations, the predicted tilt angle suppression is much weaker than that observed. By studying the response of octahedral networks to corner perturbations, it is shown that a competing LBMO phase with an alternative tilt configuration is stable as a result of interface coupling.Cambridge Commonwealth, European and International Trust
China Scholarship Counci
A global optimization approach for searching low energy conformations of proteins
De novo protein structure prediction and understanding the protein folding mechanism is an outstanding challenge of Biological Physics. Relying on the thermodynamic hypothesis of protein folding it is expected that the native state of a protein can be found out if the global minimum of the free energy surface is found. To understand the energy landscape or the free energy surface is challenging. The structure and dynamics of proteins are the manifestations of the underlying potential energy surface. Here the potential energy function stands on a framework of all-atom representation and uses purely physics-based interactions. For the solvated proteins the effective free energy is defined as an implicit solvation model which includes the solvation free energy, along with a standard all-atom biomolecular forcefield. A major challenge is to search for the global minimum on this effective free energy surface. In this work the Minima Hopping Algorithm (MHOP) to find global minima on potential energy surfaces has been used for protein structure prediction or in general finding the lowest energy conformations of proteins. Here proteins have been studied both in vacuo and in the aqueous medium. For short peptides starting from a completely extended conformation we could find conformational minima which are very close to the experimentally observed structures
Theoretical methods for studying charge and spin separation in excited states of large molecules and condensed phase
In recent years the GW/BSE approach as a sophisticated many-body method gained considerable attention for ab-initio calculations of a range of properties in finite and infinite systems. For instance, several benchmarks exist for ionization potentials, electron affinities, (band) gaps, and electronically excited states demonstrating an overall good performance of the GW/BSE approach at a computational cost comparable to time-dependent density functional theory (TD-DFT) which is a widely applied method in quantum chemistry. The GW/BSE method outperforms TD-DFT for accurate description of charge-transfer states due to explicit capture of non-local electron-hole interaction mediated by the screened Coulomb potential . Furthermore, dynamical correlation is properly described through explicit frequency dependency of . Long-range dispersion effects are accounted for by infinite summation of non-local electron correlation contributions; the so-called ring diagrams within the random-phase approximation (RPA). Therefore, the GW/BSE method provides a reliable theoretical tool with a satisfactory prediction power for electronic and optical properties of materials at different phases, and hence is consistently used in this thesis for different types of problems. In the first part of this thesis, the effect of electron-electron correlation, electron-phonon coupling and vertex corrections on the electronic band structure of ice and liquid water within the many-body Green's function formalism (the GW method) is investigated. Furthermore, within the same methodology and based on the Bethe-Salpeter equation (BSE) linear optical absorption spectra of antiferromagnetic zinc ferrite, water and ammonia in the condensed phase are calculated and analyzed in detail. Here, the electron-hole correlation which is responsible for the observed red-shift of absorption peaks and spectral weight redistributions is explicitly taken into account. The electron-hole effects are also of extreme importance for the non-linear absorption spectrum of liquid water (two-photon spectrum) in combination with quasi-particle (QP) effects. The good performance of the GW/BSE methodology is also shown on large donor-acceptor-type molecules, demonstrating its reliability for finite systems where the screening effects are much lower than in periodic systems and a correct description of the long-range behaviour of the exchange-correlation functional is essential. In order to enhance the predictive power of the GW/BSE theory for molecular systems starting from self-interaction free orbitals, a many-body based screening mixing scheme is introduced which remarkably improves the agreement of calculated excitation energies with reference data. In the second part, non-adiabatic excited-state dynamics of condensed water is studied. A combination of ab-initio Born-Oppenheimer molecular dynamics and time-dependent density functional theory is applied. The complex proton dynamics is investigated by large-scale excited-state calculations. It is found that instantaneous concerted hops of protons to the neighboring water molecules (Grotthuss mechanism) are highly unlikely. Furthermore, the solvated electron formed upon proton transfer in the excited state is not fully localized within a cavity-like environment as a consequence of attractive interaction with the surrounding water molecules
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