Physics-informed + data-driven
Hybrid models for property prediction across molecules and crystals, integrating domain priors with graph-based learning.
PhD Student · Machine Learning
Aidar Alimbayev
I am a PhD student in Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, with a research focus on applying machine learning to materials science and quantum chemistry. I am a member of the Materials Science research group under the supervision of Prof. Martin Takáč (see his personal page at mtakac.com and the MBZUAI faculty page).
Our research explores several physics-informed and data-driven approaches for predicting physical properties of molecules and crystalline materials. Selected demos and examples of our work are available on the Materials Sciecne Research page of this website.
Previously, I worked as an engineer at the NURIS Fab Lab, where I mentored students and early-stage startups, supporting the development of hardware and AI-driven prototypes.
My journey into machine learning began with a health data research project supported by Prof. Amin Zollanvari and Prof. Abduzhappar Gaipov at the Nazarbayev University School of Medicine (NUSOM). You can read our paper in Nature.
I was also a visiting scholar at Texas A&M University, where I developed physics-informed neural networks under the supervision of Prof. Ulisses Braga-Neto. This research visit was funded by a Shakhmardan Yessenov Foundation Fellowship Program.
Interests: rowing, marathon training, and building resilient hardware.
Materials Sciecne Research
Materials Sciecne Research @ MBZUAI
Physics-informed and data-driven models for molecules and crystalline materials, with updates, demos, and benchmarks.
Benchmarks and demos
Follow along for curated datasets, baselines, and interactive examples as we release new experiments.