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Computational Catalysis discovery - Molecules/ Materials
Interdisciplinary Approach to Research Challenges
Interdisciplinary knowledge is crucial for addressing complex research challenges and achieving innovative results. This is particularly true in fields like catalysis discovery, where experimental methods alone may not provide sufficient insight into the underlying electronic reasons for observed phenomena.
Leveraging Computational Methods
Computational methods, such as Density Functional Theory (DFT), offer valuable tools to complement experimental investigations. By analyzing electronic structures and energy landscapes, DFT can elucidate reaction mechanisms, predict properties of materials, and guide the design of new catalysts.
Bridging the Gap between Theory and Experiment
Our group is committed to bridging the gap between theoretical predictions and experimental applications. By combining computational chemistry with experimental techniques, we aim to:
- Solve Experimental Challenges: Utilize computational methods to address experimental limitations and identify effective solutions.
- Accelerate Discovery: Predict material properties and reaction mechanisms to guide experimental design and optimize processes.
- Design Novel Materials: Develop new materials with tailored properties for specific applications, such as energy storage, sensing, and electro/photo catalysis.
Our future research will continue to explore a wide range of topics, including:
- Gas Storage and Sensing: Investigating the properties of nanoparticles, surfaces, and MOFs for gas storage and sensing applications.
- Defects in Semiconductors: Analyzing the impact of defects on the optoelectronic and vibrational properties of 2D materials.
- Energy Devices: Studying solid-state batteries, inorganic/organic interfaces, surface phenomena, and supercapacitors.
By combining theoretical insights with experimental validation, we strive to make significant contributions to the advancement of materials science and chemistry.
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