arXiv subjects

Zhong-Kang Han

Publications and source records attributed to Zhong-Kang Han.

7 recordsLinked to original sources

arXiv2025

Orbital Hybridization-Driven Stabilization and Reactivity on an Asymmetrically Reconstructed Polar CeO2(100) Surface

Understanding and controlling the atomic structure of polar oxide surfaces is essential for unraveling surface reactivilty and designing advanced catalytic materials. Among these, the polar CeO2(100) surface is a prototypical and industrially important system in heterogeneous catalysis. However, due to the vast complexity of the surface configurations, its reconstruction behavior remains an open question. Here, we report a previously unidentified asymmetric (1x2) reconstructed structure on the CeO2(100) surface, discovered through an integrated approach that combines global structure search algorithms, machine learning-based atomic potential models, density functional theory (DFT) calculations, and in situ scanning transmission electron microscopy (STEM). The reconstructed surface is both thermodynamically and kinetically stable, characterized by an alternating arrangement of Ce3+ and Ce4+ ions, increased interlayer spacing, and reconfigured surface oxygen atoms. Importantly, the formation of localized Ce3+ polarons introduces occupied 4f states that strongly hybridize with surface O 2p orbitals, resulting in new occupied electronic states below the Fermi level. This orbital hybridization activates the O 2p states, enhances their electron-donating capacity, and facilitates the dissociation of adsorbed molecules such as H2O. These findings reveal a fundamental orbital-mediated mechanism by which surface reconstruction governs electronic structure and reactivity, offering critical insights and a new design strategy for tuning catalytic performance on polar oxide surfaces.

cond-mat.mtrl-sci↗

arXiv2024

Two Distinct Oxidation Dispersion Mechanisms in Pd-CeO2 Mediated by Thermodynamic and Kinetic Behaviors of Single Pd Species

Understanding the dispersion process of supported catalysts is crucial for synthesizing atomic-level dispersed catalysts and precisely manipulating their chemical state. However, the underlying dispersion mechanism remains elusive due to the lack of atomic-level evidence during the dispersion process. Herein, by employing spherical aberration-corrected environmental scanning transmission electron microscopy (ESTEM), first-principles calculations, and a global optimization algorithm, we unraveled the pre-oxidation dispersion and direct dispersion mechanisms in the Pd/CeO2 (100) system, mediated by the thermodynamic and kinetic behaviors of single Pd species. We discovered that at lower temperatures, the Pd nanoparticles first undergo oxidation followed by the dispersion of PdO, while at higher temperatures, the entire dispersion process of Pd remains in a metallic state. The distinct dispersion mechanisms at different temperatures are driven by the thermodynamic and kinetic differences of environment-dependent single Pd species. The nonmobile Pd1O4 species stabilized at lower temperatures obstructs the direct dispersion of Pd nanoparticles, instead triggering a sequence of pre-oxidation followed by limited dispersion. In contrast, the highly mobile Pd1O2 species at higher temperatures facilitates the complete and direct dispersion of Pd nanoparticles. This research illuminates the essential physical mechanisms of oxidative dispersion from both thermodynamic and kinetic perspectives, potentially enabling strategies for precisely controlling the state of highly dispersed catalysts.

cond-mat.mtrl-sci↗

arXiv2024

Material Hardness Descriptor Derived by Symbolic Regression

Hardness is a materials' property with implications in several industrial fields, including oil and gas, manufacturing, and others. However, the relationship between this macroscale property and atomic (i.e., microscale) properties is unknown and in the last decade several models have unsuccessfully tried to correlate them in a wide range of chemical space. The understanding of such relationship is of fundamental importance for discovery of harder materials with specific characteristics to be employed in a wide range of fields. In this work, we have found a physical descriptor for Vickers hardness using a symbolic-regression artificial-intelligence approach based on compressed sensing. SISSO (Sure Independence Screening plus Sparsifying Operator) is an artificial-intelligence algorithm used for discovering simple and interpretable predictive models. It performs feature selection from up to billions of candidates obtained from several primary features by applying a set of mathematical operators. The resulting sparse SISSO model accurately describes the target property (i.e., Vickers hardness) with minimal complexity. We have considered the experimental values of hardness for binary, ternary, and quaternary transition-metal borides, carbides, nitrides, carbonitrides, carboborides, and boronitrides of 61 materials, on which the fitting was performed. The found descriptor is a non-linear function of the microscopic properties, with the most significant contribution being from a combination of Voigt-averaged bulk modulus, Poisson's ratio, and Reuss-averaged shear modulus. Results of high-throughput screening of 635 candidate materials using the found descriptor suggest the enhancement of material's hardness through mixing with harder yet metastable structures (e.g., metastable VN, TaN, ReN$_2$, Cr$_3$N$_4$, and ZrB$_6$ all exhibit high hardness).

cond-mat.mtrl-sci↗

arXiv2024

Decoupling Many-Body Interactions in CeO2 (111) Oxygen Vacancy Structure: Insights from Machine-Learning and Cluster Expansion

Oxygen vacancies (VO's) are of paramount importance in influencing the properties and applications of ceria (CeO2). Yet, comprehending the distribution and nature of the VO's poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among VO's and polarons (Ce3+'s). In this study, we employed a combination of LASSO regression in machine learning, in conjunction with a cluster expansion model and first-principles calculations to decouple the interactions among the Ce3+'s and VO's, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable VO-Ce3+ attractions and the least Ce3+-Ce3+/VO-VO repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10^8 Metropolis Monte Carlo samplings of Vo's and Ce3+ in the near-surface of CeO2(111), we explored potential configurations within an 8x8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are most abundant in the third oxygen layer, primarily due to extensive geometric relaxation-an aspect previously overlooked. This behavior is notably dependent on the concentration of Vo. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry.

cond-mat.mtrl-sci↗

arXiv2022

A benchmark of first-principles methods for accurate prediction of semiconductor band gaps

The band gap is an important parameter of semiconductor materials that influences several functional properties, in particular optical properties. However, a fast and reliable first-principles prediction of band gaps remains a challenging problem. Standard DFT approximations tend to strongly underestimate band gaps, while the more accurate $GW$ and hybrid functionals are much more computationally demanding and unsuitable for high-throughput screening. In this work, we have performed an extensive benchmark of several approximations with different computational complexity ($G_{0}W_{0}$@PBEsol, HSE06, PBEsol, mBJ, PBEsol$-1/2$, and ACBN0) to evaluate and compare their performance in predicting the band gap of semiconductors. The benchmark is based on 114 binary semiconductors of different compositions and crystal structures, where about half of them have experimental band gaps. We find that, as expected, $G_{0}W_{0}$@PBEsol performs well relative to the experiment, with a noticeable underestimation of the band gaps by about 14% on average. Surprisingly, $G_{0}W_{0}$@PBEsol is followed closely by the much computationally cheaper pseudo-hybrid ACBN0 functional, showing an excellent performance with respect to experimental data. The meta-GGA mBJ functional also performs well relative to the experiment, even slightly better than $G_{0}W_{0}$@PBEsol in terms of mean absolute (percentage) error. The HSE06 and PBEsol$-1/2$ schemes perform overall worse than ACBN0 and mBJ schemes but much better than PBEsol. Comparing the calculated band gaps on the whole data set (including the samples with no experimental band gap), we find that HSE06 and mBJ have excellent agreement with respect to the reference $G_{0}W_{0}$@PBEsol band gaps. Thus, we propose the mBJ band gaps as economic descriptors when developing artificial intelligence models for high-throughput screening of semiconductor band gaps.

cond-mat.mtrl-sci↗

arXiv2021

Data analytics accelerates the experimental discovery of new thermoelectric materials with extremely high figure of merit

Thermoelectric (TE) materials are among very few sustainable yet feasible energy solutions of present time. This huge promise of energy harvesting is contingent on identifying/designing materials having higher efficiency than presently available ones. However, due to the vastness of the chemical space of materials, only its small fraction was scanned experimentally and/or computationally so far. Employing a compressed-sensing based symbolic regression in an active-learning framework, we have not only identified a trend in materials' compositions for superior TE performance, but have also predicted and experimentally synthesized several extremely high performing novel TE materials. Among these, we found Ag$_{0.55}$Cu$_{0.45}$GaTe$_2$ to possess an experimental figure of merit as high as ~2.8 at 827 K, which is a breakthrough in the field. The presented methodology demonstrates the importance and tremendous potential of physically informed descriptors in material science, in particular for relatively small data sets typically available from experiments at well-controlled conditions.

cond-mat.mtrl-sci↗

arXiv2020

Single-Atom Alloy Catalysts Designed by First-Principles Calculations and Artificial Intelligence

Single-atom metal alloy catalysts (SAACs) have recently become a very active new frontier in catalysis research. The simultaneous optimization of both facile dissociation of reactants and a balanced strength of intermediates' binding make them highly efficient and selective for many industrially important reactions. However, discovery of new SAACs is hindered by the lack of fast yet reliable prediction of the catalytic properties of the sheer number of candidate materials. In this work, we address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Our approach is faster and more accurate than the current state-of-the-art linear relationships. Besides consistently predicting high efficiency of the experimentally studied Pd/Cu, Pt/Cu, Pd/Ag, Pt/Au, Pd/Au, Pt/Ni, Au/Ru, and Ni/Zn SAACs (the first metal is the dispersed component), we identify more than two hundred yet unreported candidates. Some of these new candidates are predicted to exhibit even higher stability and efficiency than the reported ones. Our study demonstrates the importance of breaking linear relationships to avoid bias in catalysis design, as well as provides a recipe for selecting best candidate materials from hundreds of thousands of transition-metal SAACs for various applications.

cond-mat.mtrl-sci↗