Master's Student · Xiamen University, China
Building the future of molecular discovery through artificial intelligence.
AI for Chemistry · Computational Chemistry · Scientific Machine Learning
Incoming Ph.D. Student, Hong Kong University of Science and Technology (HKUST) with Hanyu Gao
I am a computational chemistry researcher working at the interface of artificial intelligence and quantum chemistry. My research focuses on developing machine learning methods that accelerate molecular simulations and enable next-generation chemical discovery — from neural network interatomic potentials to AI-guided conformational search.
I am currently completing my Master's degree at Xiamen University, where my work centers on AI-enhanced conformer search algorithms and machine learning interatomic potentials (MLIPs), developed and benchmarked within the MLatom and Aitomia ecosystems. In 2026 I will begin my Ph.D. in Chemical and Biological Engineering at the Hong Kong University of Science and Technology.
My long-term goal is to advance scientific machine learning methodologies that transform how molecules and materials are designed — and to build the kind of research group where that work happens in the open, in close collaboration with experimentalists.
Research focus
AI for molecular discovery
Approach
Interpretable, data-driven models
Vision
Open, collaborative science
“Become a leading researcher working at the intersection of artificial intelligence and chemistry.”
No updates yet — check back soon.
My research sits at the boundary of quantum chemistry, machine learning, and scientific software — building tools that make molecular discovery faster and more reliable.
Neural network models that learn potential energy surfaces directly from quantum-mechanical data, enabling molecular dynamics at near-DFT accuracy and a fraction of the cost.
High-level electronic structure methods — from DFT to coupled-cluster theory — used to generate reference data and benchmark machine-learned models.
Intelligent assistants that plan, execute, and interpret computational chemistry workflows, lowering the barrier between a research question and a converged calculation.
Coupling generative and predictive AI with simulation to accelerate the design of new molecules and materials, from conformer search to reaction pathway exploration.
Selected highlights from the most ambitious research directions.
AI Agents for Computational Chemistry
Aitomia is an intelligent, AI-driven platform designed to assist researchers in quantum chemistry and atomistic simulation. It integrates large language models, scientific workflows, and machine learning tools — including MLatom and AIQM methods — to streamline computational chemistry research, from input generation to result interpretation.
Representative work in machine learning, chemistry, and scientific computing.
Featured paper
Hassan Nawaz, Jinming Hu, Pavlo O. Dral, et al. · 2026
Journal of Chemical Theory and Computation (JCTC)
Hassan Nawaz, Jinming Hu, Pavlo O. Dral, et al.
Journal of Chemical Theory and Computation (JCTC)
From school to where I’m headed next — with a few photos along the way.
2026-2030 ·
Hong Kong University of Science and Technology, Hong Kong
Incoming doctoral research at the intersection of AI and chemistry, under the supervision of Hanyu Gao.
Supervisor: Prof. Hanyu Gao
2024-2026 · Completed
Xiamen University, China
Research on automation of computational chemistry workflows with AI, and development of machine learning interatomic potentials, as a member of Prof. Pavlo Dral's group.
Supervisor: Prof. Pavlo O. Dral
Thesis title: Exploring Automation and Acceleration of Computational Chemistry with Artificial Intelligence and Machine Learning Potentials
Grade obtained: CGPA: 3.93/4.0
2020-2024 ·
University of Agriculture, Faisalabad, Pakistan
Supervisor: Dr. Raja Adil Sarfraz
Thesis title: Revolutionizing Quantum and Computational Chemistry with Artificial Intelligence: A Comprehensive Study.
Grade obtained: CGPA: 3.71/4.0
2018-2020 ·
Superior Group of Colleges, Shahkot, Pakistan
FSc pre-medical studies, with a focus on chemistry, physics, and biology. Graduated with top marks and received the Merit Base Scholarship for Higher Achievers.
Grade obtained: Percentage: 89%
2016-2018 ·
The Educators Radiant Campus, Sangla Hill (a project of Beaconhouse School System), Pakistan
Matriculation and secondary education with a focus on science subjects. Graduated with top marks.
Grade obtained: Percentage: 94%
Publications
Citations
h-index
Projects
Conference Talks
Awarded in recognition of excellence in research and leadership skills during the Ph.D. recruitment process at the Hong Kong University of Science and Technology (HKUST).
Awarded in support of incoming doctoral studies in Chemical and Biological Engineering at HKUST, Hong Kong.
Awarded in support of Master's studies at Xiamen University, China.
Awarded in support of undergraduate studies at University of Agriculture, Faisalabad, Pakistan.
Awarded in support of High School Education at Superior Group of Colleges, Shahkot, Pakistan.
Professional conference highlights with a focus on presentation impact, meaningful collaborations, and high-quality event photography.
Poster title: How Well AI Agents Perform as Expert Computational Chemists? Case Studies with Aitomia
Poster title: How Well AI Agents Perform as Expert Computational Chemists? Case Studies with Aitomia. Presented remotely due to travel restrictions.
Poster title: How Well AI Agents Perform as Expert Computational Chemists? Case Studies with Aitomia
Poster title: Aitomia: Your Intelligent Assistant for Atomistic Simulations
Poster title: Aitomia: Your Intelligent Assistant for Atomistic Simulations
I'm always glad to connect with researchers and groups working on AI for chemistry, machine learning potentials, or quantum chemistry methods. If you're interested in collaborating, please reach out below.
Collaborators · Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Poland
Developing machine learning potentials capable of exceeding standard density functional theory accuracy.
Interested in collaborating? I’d love to hear from you.
Get in touchA concise view of current projects, publications, and updates.
Agentic platform for autonomous computational chemistry simulations
Development of an agentic platform for autonomous computational chemistry simulations.
Machine learning interatomic potentials for predictive quantum chemistry
Developing models that aim to exceed standard density functional theory accuracy.
Anharmonic corrections for accurate heat-of-formation predictions
Exploratory work is being refined for future publication.
Ensemble methods for more robust and accurate machine learning potentials
Further validation and benchmarking are currently underway.
Aitomia: An Agentic Framework for AI-Driven Atomistic and Quantum Chemical Simulations
Journal of Chemical Theory and Computation (JCTC) · 2026
Open to research collaborations, questions about the work above, or conversations about AI for chemistry. The fastest way to reach me is email.