A5022300740- Computational Drug Discovery Methods
- Electrochemical Analysis and Applications
- Electrocatalysts for Energy Conversion
- Machine Learning in Materials Science
- Advanced battery technologies research
- Chemical Synthesis and Analysis
- Chemistry and Chemical Engineering
- Various Chemistry Research Topics
- Chemical Thermodynamics and Molecular Structure
- Thermal Expansion and Ionic Conductivity
- Crystallography and molecular interactions
- Advanced Chemical Physics Studies
- Conducting polymers and applications
University of Kentucky
2021-2025
University of Rochester
2024
Materials design and discovery are often hampered by the slow pace materials human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, data science techniques, however, being manifest a convergence of these tools to enable silico discovery. Here, we present development deployment analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals Electronic Light-Oriented Technologies)...
Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, (semi)conducting technologies requires rapid accurate evaluation electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) other data-driven methods can further enable orders magnitude reduction time while at same providing dramatic increases...
The application of generative artificial intelligence (AI) to molecular discovery has unlocked vast potential for the automated design new chemical systems. Molecular language models (LM), however, face several challenges that impact their effectiveness, including incomplete coverage space, due limitations in training dataset diversity and size, insights within latent space representations, reconstruction reliability. Here, we present MolGen-Transformer, a AI model designed address these...
The D 3 TaLES database and data infrastructure aim to offer readily accessible uniform of varying types for redox-active organic molecules targeting non-aqueous redox flow batteries.
We investigate the anisotropic thermal expansion behavior of a co-crystalline system composed 4,4'-azopyridine and trimesic acid (TMA-azo). Using variable-temperature single-crystal X-ray diffraction (SC-XRD), low-frequency Raman spectroscopy, terahertz time-domain spectroscopy (THz-TDS), we observe significant temperature-induced shifting broadening vibrational absorption features, indicating changes in intermolecular potential. Our findings reveal that is driven by anharmonic interactions...
The shift of energy production towards renewable, yet at times inconsistent, resources like solar and wind have increased the need for better storage solutions. An emerging technology that is highly scalable cost-effective redox-flow battery comprised redox-active organic materials. Designing optimum materials redox flow batteries involves balancing key properties such as potential, stability, solubility molecules. Here, we present Data-enabled Discovery Design to Transform Liquid-based...