Alauddin Ahmed, Ph.D. (Principal Investigator)
Assistant Research Scientist
My vision is:
To address some of the grand challenges of our society such as clean energy, carbon dioxide capture, and responsible use of machine learning (ML) and artificial intelligence (AI).
My belief as a PI is:
Solving complex problems requires three ingredients: interdisciplinary collaboration, a diverse team, and a bridge between conventional and out-of-the-box thinking. Respect and kindness connect these ingredients towards achieving a sole purpose: innovation.
My major research contributions are:
- Published in prestigious venues such as Nature Communications, Journal of the American Chemical Society, Energy & Environmental Science, Angewandte Chemie International Edition, Patterns (cell press), and Chemistry of Materials.
- Discovered seven world-record-setting materials for hydrogen and natural gas storage in metal organic frameworks (MOFs).
- Developed computational methods for the discovery and design of material.
- Developed a novel approach for generating machine-learning-guided phenomenological equations.
- Developed a novel computational method for connecting solvation thermodynamics and structure- property relationships of chemical compounds.
My research is highlighted in premier journals such as Energy & Environmental Science, Nature Communications, Chemistry of Materials, and Patterns (cell press).
Also, my research is featured in several news outlets, including R&D, Physics.org, Futurity, Tech Xplore, and Newswise.
My expertise is:
At the intersection of multiple technical domains, including high-throughput computing; materials science & engineering; chemical science & engineering; data science & engineering; machine learning (ML) & artificial intelligence (AI). I have a proven history of Research and Development (R&D) in the following areas:
- Carbon capture materials & systems
- Hydrogen storage materials & systems
- Thermal energy storage materials & systems
- Novel liquid electrolyte discovery for batteries
- Multiscale modeling and simulation of battery electrodes
- Responsible ML/AI and causal inference