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Jun Jiang

Professor

University of Science and Technology of China

China

Research Interests

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Professors in Spectroscopy

Articles (20)

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2026

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2025

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2025

Collaborators (2)

Liang Zhang

professor

University of Science and Technology of China

CHINA

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Arianna Brandolese

Senior Research Fellow (Group Leader)

University of Birmingham

UNITED KINGDOM

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Jun Jiang

Research Interests

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Articles (20)

SynOmega: Simplifying Retrosynthesis for Efficient Synthesizability Scoring

Effect of Structural Similarity Between Dopants and Matrices in Organic Room‐Temperature Phosphorescence Materials: “Like Dissolves Like”

Bridging the Theory–Experiment Gap in Vibrational Spectra for Molecular Structure Elucidation

Boosting Photocatalytic Overall Water Splitting Activity of Phosphorene Through Five‐Coordinate Passivation Enabled by Carbene Addition

Designing Intrinsically Fast-Amorphizing Solid Electrolytes with Low Processing Cost

Quantitative Structure–Property Relationships for Fluorescence Quantum Yield of [6]Helicene Derivatives: Kinetic, Electronic, and Vibrational Descriptors Exploration

Self-driving laboratories need an autonomy safety harness

Deep Learning of Protein Structure and Physicochemical Properties from Two-Dimensional Infrared Spectra

Autonomous Chemistry and Materials Innovation Driven by Scientific Agents

Controlling narrowband phosphorescence and delayed fluorescence by aggregate size

Standardization Catalyzes Scientific Innovation

Carbon capture with COFs

A federated graph learning method to realize multi-party collaboration for molecular discovery

Optimizing toward Discovery: AI-Driven Exploration of Lewis Acid–Base Catalysts for PET Glycolysis

From literature to lab protocols with knowledge-graph-guided large language models

Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction

Multi-task scheduling of self-driving laboratories under scientific constraints

Spectroscopy-informed XANES–PXRD framework for multi-property prediction and structure inference

A machine learning protocol for predicting structural distributions of amyloid-forming proteins from 2D IR spectra

Data-Driven Recursive Kinetic Modeling for Fenton Reaction

Collaborators (2)

Liang Zhang

Arianna Brandolese

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