Tuning Surface-Electron Spins on Fe3 O4 (111) through Chemisorption of Carbon Monoxide.

The adsorption-induced flip of electron spin at interfaces is an important but poorly understood phenomenon for magnetic devices, sensors, and heterogeneous catalysis, due to the difficulties in determining the surface spins at atomic resolution. We present an evolutionary magnetic order searching method that allows efficient identification of the ground state spin configuration of magnetic bulk and surfaces. Using this approach, we have discovered for the first time a set of adsorption-induced near-degenerate surface magnetic states on the Fe3 O4 (111) surface. Molecular adsorption of CO causes a destabilization of the magnetic states of the clean surface leading to a set of near-degenerate surface magnetic states at medium coverage, which causes an abrupt increase of the magnetic entropy on the surface. The empty 2π* orbital of CO, which could accommodate the back donation of the spin density in the Fe d orbitals, plays a key role for the CO adsorption-induced spin transition.

Artificial intelligence: A powerful paradigm for scientific research.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning.

Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict two-dimensional octahedral oxyhalides with improved optoelectronic properties. We develop an effective machine-learning model based on an expansive data set generated from density functional calculations including the geometric and electronic properties of 300 two-dimensional octahedral oxyhalides. Our model accelerates the screening of potential optoelectronic materials of 5000 two-dimensional octahedral oxyhalides. The distorted stacked octahedral factors proposed in our model play essential roles in the machine-learning prediction. Several potential two-dimensional optoelectronic octahedral oxyhalides with moderate band gaps, high electron mobilities, and ultrahigh absorbance coefficients are successfully hypothesized.

Boosting Photocatalytic Hydrogen Production by Modulating Recombination Modes and Proton Adsorption Energy.

Solar-driven production of renewable energy (e.g., H2) has been investigated for decades. To date, the applications are limited by low efficiency due to rapid charge recombination (both radiative and nonradiative modes) and slow reaction rates. Tremendous efforts have been focused on reducing the radiative recombination and enhancing the interfacial charge transfer by engineering the geometric and electronic structure of the photocatalysts. However, fine-tuning of nonradiative recombination processes and optimization of target reaction paths still lack effective control. Here we show that minimizing the nonradiative relaxation and the adsorption energy of photogenerated surface-adsorbed hydrogen atoms are essential to achieve a longer lifetime of the charge carriers and a faster reaction rate, respectively. Such control results in a 16-fold enhancement in photocatalytic H2 evolution and a 15-fold increase in photocurrent of the crystalline g-C3N4 compared to that of the amorphous g-C3N4.

Solid Base Bi24 O31 Br10 (OH)δ with Active Lattice Oxygen for the Efficient Photo-Oxidation of Primary Alcohols to Aldehydes.

The selective oxidation of primary alcohols to aldehydes by O2 instead of stoichiometric oxidants (for example, MnVII , CrVI , and OsIV ) is an important but challenging process. Most heterogeneous catalytic systems (thermal and photocatalysis) require noble metals or harsh reaction conditions. Here we show that the Bi24 O31 Br10 (OH)δ photocatalyst is very efficient in the selective oxidation of a series of aliphatic (carbon chain from C1 to C10 ) and aromatic alcohols to their corresponding aldehydes/ketones under visible-light irradiation in air at room temperature, which would be challenging for conventional thermal and light-driven processes. High quantum efficiencies (71 % and 55 % under 410 and 450 nm irradiation) are reached in a representative reaction, the oxidation of isopropanol. We propose that the outstanding performance of the Bi24 O31 Br10 (OH)δ photocatalyst is associated with basic surface sites and active lattice oxygen that boost the dehydrogenation step in the photo-oxidation of alcohols.

Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters.

We propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au25 nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au25, we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au36 and Au133 nanoclusters.

Structural and catalytic properties of the Au25-xAgx(SCH3)18 (x = 6, 7, 8) nanocluster.

In this study, we explore the structural, electronic and catalytic properties of bimetallic nanoparticles of the form Au25-xAgx(SR)18 (for x = 6, 7, 8). Due to the combinatorial enormity of the number of different alloyed structures, we choose 500 random configurations corresponding to each alloying level and energetically optimize their structures. Here we report the properties of the lowest energy structures and determine the most favorable Ag alloying sites for these systems. We also show that nanoalloys with one Ag at the center and the rest in the outer shell of the Au13 kernel are less energetically favorable than the ones with all the Ag atoms occupying the surface of the Au13 kernel. We further present experimental results showing that catalytic oxidation of CO is adversely affected due to Ag alloying. We provide qualitative and quantitative evidence to explain this reduction of the catalytic activity using Fukui functions and average adsorption energies respectively.

A Machine-Driven Hunt for Global Reaction Coordinates of Azobenzene Photoisomerization.

Azobenzene is a very important system that is often studied for better understanding light-activated mechanical transformations via photoisomerization. The central C-N═N-C dihedral angle is widely recognized as the primary reaction coordinate for changing cis- to trans-azobenzene and vice versa. We report on a global reaction coordinate (containing all internal coordinates) to thoroughly describe the reaction mechanism for azobenzene photoisomerization. Our global reaction coordinate includes all of the internal coordinates of azobenzene contributing to the photoisomerization reaction coordinate. We quantify the contribution of each internal coordinate of azobenzene to the overall reaction mechanism. Finally, we provide a detailed mapping on how each significantly contributing internal coordinate changes throughout the energy profile (from trans to transition state and subsequently to cis). In our results, the central C-N═N-C dihedral remains the primary internal coordinate responsible for the reaction coordinate; however, we also conclude that the disputed inversion-assisted rotation is half as important to the overall reaction mechanism and the inversion-assisted rotation is driven by four adjacent dihedral angles C-C-N═N with very little change to the adjacent C-C-N angles.

Controlling Ag-doping in [AgxAu25-x(SC6H11)18]- nanoclusters: cryogenic optical, electronic and electrocatalytic properties.

Doping metal nanoclusters with a second type of metal is a powerful method for tuning the physicochemical properties of nanoclusters at the atomic level and it also provides opportunities for a fundamental understanding of alloying rules as well as new applications. Herein, we have devised a new, one-phase strategy for achieving heavy Ag-doping in Au25(SR)18 nanoclusters. This strategy overcomes the light doping of silver by previous methods. X-ray crystallography together with ESI-MS determined the composition of the product to be [AgxAu25-x(SC6H11)18]- with x ∼ 21. Cryogenic optical spectroscopy (80-300 K) revealed fine features in optical absorption peaks. Interestingly, the heavy doping of silver does not significantly change the electron-phonon coupling strength and the surface phonon frequency. DFT simulations reproduced the experimentally observed trend of electronic structure evolution with Ag doping. We further investigated the electrocatalytic performance of such heavily Ag-doped nanoclusters for oxygen reduction in alkaline solutions. The mass activity of ligand-off [AgxAu25-x(SC6H11)18]- nanoclusters (217.4 A g-1metal) was determined to be higher than that of ligand-on nanoclusters (29.6 A g-1metal) at a potential of -0.3 V (vs. Ag/AgCl). The rotating disk electrode (RDE) studies revealed the tunable kinetic features of the nanoclusters by ligand removal.

Quantum Mechanics/Molecular Mechanics Free Energy Maps and Nonadiabatic Simulations for a Photochemical Reaction in DNA: Cyclobutane Thymine Dimer.

The absorption of ultraviolet radiation by DNA may result in harmful genetic lesions that affect DNA replication and transcription, ultimately causing mutations, cancer, and/or cell death. We analyze the most abundant photochemical reaction in DNA, the cyclobutane thymine dimer, using hybrid quantum mechanics/molecular mechanics (QM/MM) techniques and QM/MM nonadiabatic molecular dynamics. We find that, due to its double helix structure, DNA presents a free energy barrier between nonreactive and reactive conformations leading to the photolesion. Moreover, our nonadiabatic simulations show that most of the photoexcited reactive conformations return to standard B-DNA conformations after an ultrafast nonradiative decay to the ground state. This work highlights the importance of dynamical effects (free energy, excited-state dynamics) for the study of photochemical reactions in biological systems.

Non-adiabatic molecular dynamic simulations of opening reaction of molecular junctions.

We report non-adiabatic molecular dynamic simulations of the ring opening reaction of diarylethene (DAE) derivative molecules, both free standing and embedded between gold electrodes. Simulations are performed by the surface hopping method employing density functional theory. Typically, the free-standing molecules exhibit large quantum yields to open and close; however the process is quenched for the molecules embedded between electrodes. Our simulations reveal the importance of the DAE side chemical groups, which explain the efficiency of the quenching process. Namely, delocalization of the LUMO state contributes to electronic coupling between the molecule and electrodes, suppressing or enhancing the reaction process. The simulations indicate that a proper choice of the chemical side group, which provides the strong localization of the LUMO state, can substantially diminish the quenching mechanism. Additionally, we analyze a strong dependency of the quantum yield of the opening reaction coming from the mechanical strength of the molecules.

Product Selectivity Controlled by Zeolite Crystals in Biomass Hydrogenation over a Palladium Catalyst.

This work delineates the first example for controlling product selectivity in metal-catalyzed hydrogenation of biomass by zeolite crystals. The key to this success is to combine the advantages of both Pd nanoparticles (highly active sites) and zeolite micropores (controllable diffusion of reactants and products), which was achieved from encapsulation of the Pd nanoparticles inside of silicalite-I zeolite crystals as a core-shell structure (Pd@S-1). In the hydrogenation of biomass-derived furfural, the furan selectivity over the Pd@S-1 is as high as 98.7%, outperforming the furan selectivity (5.6%) over conventional Pd nanoparticles impregnated with S-1 zeolite crystals (Pd/S-1). The extraordinary furan selectivity in the hydrogenation over the Pd@S-1 is reasonably attributed to the distinguishable mass transfer of the hydrogenated products in the zeolite micropores.

Slow Relaxation of Surface Plasmon Excitations in Au55: The Key to Efficient Plasmonic Heating in Au/TiO2.

Gold nanoparticles distinguish themselves from other nanoparticles due to their unique surface plasmon resonance properties that can be exploited for a multiplicity of applications. The promise of plasmonic heating in systems of Au nanoparticles on transition metal oxide supports, for example, Au/TiO2, rests with the ability of the surface plasmon in Au nanoparticles to effectively transfer energy into the transition metal oxide. Here, we report a critical observation regarding Au nanoparticle (Au55) surface plasmon excitations, that is, the relaxation of the surface plasmon excitation is very slow, on the order of several picoseconds. Starting from five plasmon states in Au55 nanoparticles using nonadiabatic molecular dynamics simulations, we find that the relaxation time constant resulting from these simulations is ∼6.8 ps, mainly resulting from a long-lived intermediate state found at around -0.8 eV. This long-lived intermediate state aligns with the conduction band edge of TiO2, thereby facilitating energy transfer injection from the Au55 nanoparticle into the TiO2. The current results rule out the previously reported molecular-like relaxation dynamics for Au55.

Design and Preparation of Supported Au Catalyst with Enhanced Catalytic Activities by Rationally Positioning Au Nanoparticles on Anatase.

A synergistic effect between individual components is crucial for increasing the activity of metal/metal oxide catalysts. The greatest challenge is how to control the synergistic effect to obtain enhanced catalytic performance. Through density functional theory calculations of model Au/TiO2 catalysts, it is suggested that there is strong interaction between Au nanoparticles and Ti species at the edge/corner sites of anatase, which is favorable for the formation of stable oxygen vacancies. Motivated by this theoretical analysis, we have rationally prepared Au nanoparticles attached to edge/corner sites of anatase support (Au/TiO2-EC), confirmed by their HR-TEM images. As expected, this strong interaction is well characterized by Raman, UV-visible, and XPS techniques. Very interestingly, compared with conventional Au catalysts, Au/TiO2-EC exhibits superior catalytic activity in the oxidations using O2. Our approach to controlling Au nanoparticle positioning on anatase to obtain enhanced catalytic activity offers an efficient strategy for developing more novel supported metal catalysts.

Tripeptides on Gold Nanoparticles: Structural Differences between Two Reverse Sequences as Determined by Solid-State NMR and DFT Calculations.

The reverse-sequence peptides CysAlaAla and AlaAlaCys may attach to gold nanoparticles through the thiol group, and they differ primarily by whether the charged amino or the carboxylate group is proximal to the sulfur. Alanine residues in these peptides are not expected to interact significantly with the gold surface and serve to place a large separation between the amino and carboxylate groups. Solid-state NMR experiments and DFT calculations were performed to explore the structural differences between CysAlaAla on gold nanoparticles and AlaAlaCys on gold nanoparticles. It is found that the relative positions between the thiol, amino, and carboxylate groups strongly influences the structures of the peptide-gold systems. CysAlaAla orients parallel to the gold surface in a monolayer fashion, whereas AlaAlaCys forms an interdigitating bilayer-like structure that is oriented upright relative to the gold surface.

Photo-induced reactions from efficient molecular dynamics with electronic transitions using the FIREBALL local-orbital density functional theory formalism.

The computational simulation of photo-induced processes in large molecular systems is a very challenging problem. Firstly, to properly simulate photo-induced reactions the potential energy surfaces corresponding to excited states must be appropriately accessed; secondly, understanding the mechanisms of these processes requires the exploration of complex configurational spaces and the localization of conical intersections; finally, photo-induced reactions are probability events, that require the simulation of hundreds of trajectories to obtain the statistical information for the analysis of the reaction profiles. Here, we present a detailed description of our implementation of a molecular dynamics with electronic transitions algorithm within the local-orbital density functional theory code FIREBALL, suitable for the computational study of these problems. As an example of the application of this approach, we also report results on the [2 + 2] cycloaddition of ethylene with maleic anhydride and on the [2 + 2] photo-induced polymerization reaction of two C60 molecules. We identify different deactivation channels of the initial electron excitation, depending on the time of the electronic transition from LUMO to HOMO, and the character of the HOMO after the transition.

Calculated photo-isomerization efficiencies of functionalized azobenzene derivatives in solar energy materials: azo-functional organic linkers for porous coordinated polymers.

Recently, we used a local orbital density functional theory code called FIREBALL, to study the photoisomerization process in azobenzene derivatives for solar energy materials. Azobenzene functional groups undergo photoisomerization upon light irradiation or application of heat. Zhou et al (2012 J. Am. Chem. Soc. 134 99-102) showed that these azobenzenes can then be introduced into metal-organic frameworks via an organic linker in order to create a reversible switch for CO2 adsorption. In this manuscript, we examined how the addition of organic linkers (isophthalic acid) changes the relaxation times, isomerization mechanism, and quantum yield for both the cis↔trans pathways. We then tuned these properties by substituting functional groups, finding an increase in quantum yield as well as improved optical properties.

Selective catalytic production of 5-hydroxymethylfurfural from glucose by adjusting catalyst wettability.

The development of highly-efficient catalysts for conversion of glucose and fructose to 5-hydroxymethylfurfural (HMF) is of great importance. In this work, theoretical simulations form the basis for rational design and synthesis of a superhydrophobic mesoporous acid, that can completely prevent HMF hydration, giving HMF as sole product from full conversion of fructose. Interestingly, the combined superhydrophobic solid acid and superhydrophilic solid base catalysts are very efficient for one-pot conversion of glucose to HMF, giving a yield as high as 95.4 %. The excellent catalytic data in the conversion of glucose to HMF is attributed to the unique wettabilities of the solid acid and base catalysts.