Research

Interests:

Plasma
Surface Processing
Density Functional Theory
Machine Learning
Heterogeneous Catalysis
Solid State Batteries

Theoretical Modeling of Catalysts with Density Functional Theory, Transition State Theory and Kinetic Monte Carlo Methods

Objectives:

Pursue microscopic understanding of reaction mechanisms and active sites for complex catalysis
Enable the discovery of advanced catalysts

Atomic-Scale Modeling of Solid-State Battery Materials by Combining Density Functional Theory and Machine Learning Method

Objectives:

Gain fundamental understanding of current materials limits and identify key materials parameters for optimizing the performance of solid state batteries (SSBs);
Determine structure-property relationships of the heterostructural solid-state interfaces (SSIs) in SSBs systems by corroborating atomic scale theory with experiment

Accomplishments:

Reviewed recent methodological advancements of machine learning techniques for atomic-scale modeling and materials design, and their applications to materials for solid-state batteries (Front. Energy Res.)
Completed the phase diagram of glass/ceramic Li2S–P2S5 (gc-LPS) with varying compositions by combining DFT, ANN potentials, GA sampling, and AIMD; Mapped the structure-stability-conductivity relationship
Established an automated workflow for XAS simulations with XCH approach; Generated a database with more than 5,000 S K-edge XAS spectra for more than 100 crystalline and amorphous structures

First Principles Study of Electrode Materials in Lithium-Ion Batterie

The high energy efficiency of lithium-ion batteries allows various applications in portable electronics and electric vehicles. Extensive research has been devoted to the electrode materials aiming to design the electrodes with high power and capacity. The improvement of materials in terms of cost and performance is hindered by the lack of fundamental understanding on discharge/charge mechanisms. Here, Density Functional Theory (DFT) calculations are performed to tackle the challenges.

Objectives:

Gain better understanding on structures, mechanisms and optimal sites for rate performance, which were synthesized and characterized experimentally;
Identify key descriptors that control the rate performance, capacity and durability;
Optimize material at a theoretical level based on key descriptors.

Accomplishments:

Investigated lithiation mechanisms of spinel AFe2O4 (A = Zn, Mg, Cu) at atomic level and identified the key descriptors that control the rate performance and cyclability based on the bulk and surface/interface calculations (Chem. Mater., Phys. Chem. Chem. Phys., ACS Appli. Mater. Interfaces, Adv. Mater. Interfaces, J. Electrochem. Soc., Phys. Chem. Chem. Phys.)
Investigated doping effects on 1) electronic structures and electrochemical properties of Ca, Nb, F-doped spinel Li4Ti5O12 (Chem. Mater., Chem. Eur. J.) 2) surface stability and morphology of V-doped hollandite α-MnO2
Conducted STEM image simulation and spectroscopy simulation to facilitate the understanding of experimental observations

Computational Modeling of DNA-Protein Interaction

DNA oxidization is one of the deleterious mutations in organisms, and 8-oxoguanine (8-oxoG) is the most common DNA lesions product. Since the mutation is detrimental, most organisms have enzymes that can target and repair it. The formamidopyrimidine-DNA glycosylase (Fpg) protein is one of the 8-oxoG repair protein. The crystal structures of Fpg-DNA complex have been extensively studied in previous research. However, the mechanisms of recognition 8-oxoG by Fpg protein still remains unclear due to the complex dynamics processes.
Hereby, we conducted molecular dynamics (MD) simulations on Fpg protein, implementing TIP3P explicit solvent model. During the simulations, a conformational variation of active sites in Fpg protein was observed; meanwhile, the hydrogen bonds were broken between the protein and lesion DNA, indicating that there are a series of conformation changes of Fpg protein in the recognition of 8-oxoG, and eventually the Fpg-DNA complex was formed in an energetic preferred pathway. (Thesis)

Nano Wave-Absorption Compound Materials

1. White‐fungus‐like NiSx microspheres were synthesized on a large scale implementing a simple hydrothermal method. The influence of the reaction time and the surfactant on the final products was investigated, with the formation mechanism discussed. The synthesized white‐fungus‐like NiSx microspheres were used firstly as fillers in the fabrication of NiSx/polyvinylidene fluoride (PVDF) composites. Relationships between the loadings of the NiSx and wave‐absorption properties of the composites were analyzed, with the mechanism demonstrated. (ChemPlusChem)
2. Bi2S3 nanorods were synthesized on a large scale through a simple hydrothermal method at low temperature. To investigate the wave‐absorption properties of Bi2S3 nanorods, the homogeneous nanocomposites consisting of the nanorods and paraffin or PVDF matrix were fabricated and characterized. The results indicated that the introduction of Bi2S3 nanorods to PVDF significantly enhanced the wave‐absorption properties of the absorber. The loss mechanism of Bi2S3/PVDF nanocomposites with different loadings was discussed and theories were employed to explain the mechanism of the enhancement based on the experimental results. (ChemPlusChem)
3. CoxSy nanorods were synthesized via a simple hydrothermal method, utilizing ethanediamine (EDTA). By mixing CoxSy porous nanorod with PVDF, a composite material was prepared. The wave absorbing, dielectric and mechanical properties of the composite material were measured and analyzed. The composite material showed outstanding wave absorption properties under the frequency range of 2– 18 GHz. In a loading of as low as 5 wt%, the reflection loss of the composite material can reach – 45 dB, and the band is relatively wide. The mechanism of the wave absorption enhancement properties was discussed. (Graduation Thesis)

Publications

HaoYue Guo

haoyue1619@gmail.com

中文 English Español Français 日本語

﻿Rese﻿arch﻿﻿

Theoretical Modeling of Catalysts with Density Functional Theory, Transition State Theory and Kinetic Monte Carlo Methods

Atomic-Scale Modeling of Solid-State Battery Materials by Combining Density Functional Theory and Machine Learning Method

First Principles Study of Electrode Materials in Lithium-Ion Batterie

Computational Modeling of DNA-Protein Interaction

Nano Wave-Absorption Compound Materials

Publications

Research