プロフィール詳細

プロジェクトを作成

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Dr. Guojin C.に依頼

Hong Kong

Computational Chemistry PhD Consultant | DFT & ML for Materials | LLM Scientific Reasoning Evaluation

プロフィール概要

I provide professional DFT and computational chemistry services for research groups and R&D teams who need clear, reliable results rather than exploratory discussions. My work focuses on well-defined computational tasks with explicit deliverables, including: • Geometry optimization and energy calculations • DOS / PDOS analysis with interpretation • Reaction energy profiles and transition state analysis • Clear, concise PDF summary reports explaining methods, results, and limitations I have extensive experience with both molecular and periodic systems, and I emphasize reproducibility, transparent assumptions, and on-time delivery. Typical deliverables include: • Input/output files • Plots (DOS, PDOS, energy profiles) with annotations • A short technical report suitable for internal review or project documentation I prefer fixed-scope projects with clear objectives and timelines, and I am happy to clarify requirements before starting to ensure efficient collaboration.

専門分野

• ☆ Catalysis
• ☆ Inorganic Chemistry
• ☆ Molecular Engineering
• ☆ Physical Chemistry
• ☆ Chemoinformatics
• ☆ Photochemistry
• ☆ Quantum Chemistry
• ☆ Theoretical Chemistry
• ☆ Thermochemistry
• ☆ Computational Chemistry
• ☆ Computer-Aided Engineering
• ☆ Data Analysis
• ☆ Data Collection
• ☆ Machine Learning
• ☆ Knowledge Management
• ☆ Taxonomy/Classification
• ☆ Decision Science
• ☆ Information & Knowledge Management
• ☆ Hypothesis
• ☆ Peer Review
• ☆ Research Methodology
• ☆ Survey Design & Methodology
• ☆ Feasibility Studies
• ☆ Fact Checking

サービス

• Writing
• Research
• Data & AI

Writing Technical Writing

Research Technology Scouting

Data & AI Predictive Modeling, Statistical Analysis

職務経験

AssociateProfessor

Shanxi Universtiy

7月 2014 - 現在

学歴

PhD (Chemistry)

University of Chinese Academy of Sciences

8月 2009 - 7月 2012

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

Sheng‐Jie Lu, Guo‐Jin Cao, Zhao‐Ou Gao (2025). The Role of Gold in Modifying the Structural Stabilities, Superhalogen Properties, and Double Aromaticity of Cyclic Carbon Clusters: Insights From AuC20− and AuC20 . Journal of Computational Chemistry.

Guo‐Jin Cao (2025). Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions . International Journal of Quantum Chemistry.

Guo-Jin Cao (2024). Structural and Electronic Properties of U5M+ and T5M+ (U = Uracil, T = Thymine, M = Ag and Au) Cluster Cations . Crystals.

Guo-Jin Cao (2024). Structural and Electronic Properties of U5M+ and T5M+ (U = Uracil, T = Thymine, M = Ag and Au) Cluster Cations . Crystals.

Sheng-Jie Lu, Xi-Ling Xu, Guo-Jin Cao, Hong-Guang Xu, Wei-Jun Zheng (2018). Structural evolution and bonding properties of BSin−/ (n = 4–12) clusters: Size-selected anion photoelectron spectroscopy and theoretical calculations . The Journal of Chemical Physics.

Guo-Jin Cao(2018). A dinuclear Cu(i)-mediated complex: Theoretical studies of the G2Cu24+ cluster ion . The Journal of Chemical Physics. 149. (14). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 144308. {AIP} Publishing

Xi-Ling Xu, Bin Yang, Zhi-You Wei, Guo-Jin Cao, Hong-Guang Xu, Wei-Jun Zheng(2018). Structural and bonding properties of Cu3O3− and Cu3O4− clusters: anion photoelectron spectroscopy and density functional calculations . Physical Chemistry Chemical Physics. 20. (31). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 20622--20628. Royal Society of Chemistry ({RSC})

PREPRINT

Guo-Jin Cao (2024). Accurately estimating activation energies by leveraging neural network methods and a large dataset .

Guo-Jin Cao, Sheng-Jie Lu (2024). Precise estimation of activation energies in gas-phase chemical reactions via artificial neural network .

Guo-Jin Cao, Sheng-Jie Lu (2024). Precise estimation of activation energies in gas-phase chemical reactions via artificial neural network .

Guo-Jin Cao (2024). Machine Learning Strategies for Forecasting Discharge Capacity in Lithium-Ion Batteries with NCM Layered Cathode Material .