Kevin Maik Jablonka
We think AI has real scientific impact when it helps us develop new concepts and work with the data that actually matter to scientists.
I lead LAMALab at Friedrich Schiller University Jena. I want AI to do more than make predictions. Simply making more predictions can distract us from the scientific questions that matter. I believe its most important scientific use will be to deepen our understanding, help us develop new concepts, and give us recommendations we can act on. Building that path requires end-to-end learning and models whose conclusions we can understand and test. Our work has made oxidation states computable, revealed unexpected behaviour in an operating carbon-capture plant, and inferred molecular structures directly from raw spectra.
The Science of the Unaskable
Some questions remain out of reach because the concept, representation, or measurement they require does not yet exist. The task is to build that missing piece and carry it through to an experiment or decision.
I want to know what a model has learned, which representation makes a conclusion possible, and where that conclusion fails. This connects to older questions about emergence and levels of description and about what we mean by molecular structure and shape.
Making this work requires chemistry, machine learning, psychometrics, and econometrics; I also work and teach with psychologists and social scientists. LAMALab created ChemPile, ChemBench, and MaCBench; through NFDI FAIRmat, we help make materials data machine-actionable. These resources let others inspect, test, and extend the work.
Science and people
I am in a privileged position, so I keep asking myself: How can I make the most significant contribution?
My two most important outputs are science and people; in fundamental research, people may matter even more. Science should change what we understand. People carry that understanding—and the tacit knowledge behind it—forward.
At LAMALab, scientific freedom rests on trust, kindness, and candour. We share unfinished ideas, data, code, and failures early. My role is to protect time for thought, ask hard questions, and help each researcher develop an independent direction. I want the lab to leave better questions, resources others build on, and people ready to lead.
Bio lengths
Kevin Maik Jablonka leads LAMALab at Friedrich Schiller University Jena. He develops AI as a scientific instrument: making chemical concepts measurable and turning raw observations into the next experiment. His work has made oxidation states computable, inferred molecular structures from spectra, and revealed unexpected behaviour in an operating carbon-capture plant.
Kevin Maik Jablonka leads LAMALab at Friedrich Schiller University Jena. He develops AI as a scientific instrument: making chemical concepts measurable and turning raw observations into the next experiment. His work has made oxidation states computable, inferred molecular structures from spectra, and revealed unexpected behaviour in an operating carbon-capture plant. He combines chemistry and machine learning with psychometrics and econometrics to make scientific measurement, uncertainty, and judgement explicit. LAMALab works with psychologists and social scientists to test where model and expert judgements agree, where they fail, and which evidence changes them. These tests connect model conclusions to experiments and decisions.
Kevin Maik Jablonka leads LAMALab at Friedrich Schiller University Jena. He develops AI as a scientific instrument: making chemical concepts measurable and turning raw observations into the next experiment. His work has made oxidation states computable, inferred molecular structures from spectra, and revealed unexpected behaviour in an operating carbon-capture plant. His programme spans end-to-end learning: from the measurements scientists actually make, through representations and models, to recommendations they can test. Actionable recommendations depend on measurements, representations, experiments, and the tacit knowledge connecting them; each link must remain open to scrutiny. Scientists should be able to determine what a model learned, challenge its representation, and locate its limits before acting on it. LAMALab created ChemPile, ChemBench, and MaCBench as shared infrastructure others can inspect and extend. Psychometrics, econometrics, psychology, and social science help the group study measurement and judgement in models and people. He is a Google Research Scholar. At LAMALab, scientific freedom rests on radical openness, trust, and kindness. Ideas, data, code, and failures are shared early; close collaboration passes on tacit knowledge while each researcher develops an independent direction. He wants LAMALab's legacy to be new understanding, resources others build on, and people who lead programmes of their own.
As of August 2026, half (11 of 22) of my first- or corresponding-author peer-reviewed publications have been published or accepted in Nature Portfolio journals, Science Advances, or the NeurIPS main proceedings (ICORE A*). 5,539 Google Scholar citations (h-index 27; checked ). More than €4M in awarded PI or project-lead funding.
Apparently, I am already in the weights.
A 17-model recognition test found me in 12 models and ranked me #38,788 of 532,454 people—the top 7.3% of people represented in their weights.
The strongest models described me as a computational chemist, materials scientist, and AI researcher. The tails were more imaginative: one relocated me to a professorship in Zurich; another invented a second career as a French footballer.
Model-by-model resultCompact CV
Compact CV
Career
- —presentIndependent Research Group LeaderCarl Zeiss Foundation Research Group, Friedrich Schiller University Jena; affiliated with HIPOLE Jena. Holds doctoral-supervision rights and leads 11 PhD researchers and one postdoctoral researcher.
- —Independent contractor, AI evaluation and red teamingOpenAI.
- —PhD, Chemistry and Chemical EngineeringEPFL; Dimitris N. Chorafas Foundation Award and top-8% thesis distinction.
- —MSc, ChemistryEPFL; 5.95/6.00, second-highest GPA across all EPFL master’s graduates and highest in chemistry.
- —BSc, ChemistryTechnical University of Munich; high distinction.
Scientific leadership
- Founder and leader of the interdisciplinary LAMALab; supervision record spanning 26 doctoral, master’s, postdoctoral, fellowship, and research-internship appointments since 2021.
- Co-Speaker of NFDI FAIRmat and co-leader of Enabling Data-driven Science; co-leader of the DAEMON COST Action working group on multimodal machine learning.
- Area Chair for NeurIPS 2026; organizer of the 2027 Faraday Discussion on Digital Chemical Discovery; initiator of the first global chemical-sciences LLM hackathon and organizer of L2M3 and MADICES.
- Grant reviewer for ERC, DFG, A*STAR, FWF, ScaDS.AI, and NWO; reviewer for leading Nature- and Science-family journals, PNAS, and JACS.
Selected honours and memberships
- — ICML Gold Reviewer
- — Elected member, Junge Akademie | Mainz
- — AI Nodee, Foresight Institute
- — Google Research Scholar Award
- — Helmholtz AI Award for Best Digital Resource (ChemPile)
- — Best Research Environment 2024 (LAMALab)
- — Dimitris N. Chorafas Foundation Award
- — CAS Future Leader
Photos and source material
Headshots by Marina Romanova / HIPOLE Jena.
