Nawaf Alampara
Doctoral Researcher
Friedrich-Schiller-Universität Jena
I am second-year PhD student, working with Dr. Kevin Maik Jablonka. I'm building machine learning systems to speed up scientific research, and I love projects that involve both research and building the tooling that enables and accelerates that research. Lately, I’ve been analyzing general-purpose AI models/systems to understand their limitations in scientific applications—where they fail—and interpreting them to uncover why they fail. My goal is to use these insights to design AI systems that aren’t just impressive on benchmarks but truly impactful for advancing science and research.
News
May 2024
Accepted for Google Summer of Code 2025
I will contribute to DeepMind. Effort will be towards evaluating scientific reasoning capabilities of Gemini models.
Publications
Nature Chemistry 2025
A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists
Adrian Mirza, Nawaf Alampara, ..,Kevin Maik Jablonka
First comprehensive benchmark for chemistry-specific AI capabilities, evaluating chemical knowledge, intuition, and reasoning of LLMs against human chemists.
AI4Mat-Vienna 2024 2024
⭐ spotlight (oral)
MatText: Do Language Models Need More than Text & Scale for Materials Modeling?
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka
Revealing Transformer models' (IFT and trained from scratch) limitations in capturing 3D geometric information crucial for materials modeling.
AI4Mat-NeurIPS 2024 2024
⭐ spotlight (oral)
Probing the limitations of multimodal language models for chemistry and materials research
Nawaf Alampara, et al.
Multimodal benchmark for chemistry/materials science for AI with ablations to interpret the limitations
2025
ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models
Adrian Mirza, Nawaf Alampara, Martiño Ríos-García,.., Michael Pieler, Kevin Maik Jablonka
Thoughts on evaluating ML systems in materials science
2025
Lessons from the trenches on evaluating machine-learning systems in materials science
Nawaf Alampara, Mara Schilling-Wilhelmi, Kevin Maik Jablonka
Thoughts on evaluating ML systems in materials science
Journal of Physics D: Applied Physics 2024
Formation of an extended defect cluster in cuprous oxide
G Aggarwal, S Chawla, AJ Singh, Nawaf Alampara, et al.
Characterization of intrinsic defects and dopants in Cu₂O, leading to discovery and experimental validation of new defect formation.
Experience
PhD Researcher — Friedrich-Schiller-Universität Jena
Advisor: Dr. Kevin Maik Jablonka
AI Research Contractor (Part-time) — Stability AI
Dataset curation | Benchmarking
Principal Engineer — QpiVolta Technologies
Material simulation using geometric deep learning models | Software development
Research Engineer — QpiAI Technologies
Real-time video analytics | Computer vision
Education
Friedrich-Schiller-Universität Jena, Germany
PhD Machine Learning for Science
Advisor: Dr. Kevin Maik Jablonka
Indian Institute of Technology Bombay, India
MSc Energy Science
Advisor: Prof. K R Balasubramaniam
Thesis: Defects and Dopants in Cu₂O - DFT study
Birla Institute of Technology Mesra, India
BSc Physics
Portfolio
Defects and Dopants in Cu2O - DFT study
Identifying potential dopants for improving carrier transport properties of Cu2O via neutralizing mobility limiting trap states or by tuning electronic structures.