Boost production specifications and yield via AI/ML. Discover through closed-loop experimentation and machine learning.
Physics and ML background applied to chemistry's automation bottleneck. I build the systems that enable 10-100x faster experimental iteration.
I transform instruments, techniques, processes, procuts, and teams With techniques from systems thinking, programming, IoT, physics, and custom algorithm design, fast prototypoing. This cross-domain approach enables rapid innovation across different analytical methods and production environments.
Leveraging open source tools across many fields, like a typical data scientist would
Well designed deep neural networks, Bayesian networks, and physics-inspired ML for unique challenges
End-to-end redesign from sensors to insights to decisions
Connect, modernize, and control instruments for real-time feedback
Leverage first principles to build models that work with limited data
The methodology is transferable: understand the data generation process, redesign for ML optimization, build custom algorithms that extract maximum signal, deploy systems that enable fast iteration, facilitate and train teams to excute faster than anyone.
Finding and using optimal conditions to access reaction pathways and products that are yet unknown.
Through mastery of Newtonian mechanics, Boston Dynamics robots access states that are hard for humans or animals to reach through natural training alone. Similarly, temporal control of reaction conditions allows access to chemical states that are hard to reach in classic production processes—optimizing them to be more efficient, safer, cheaper, and reduce waste.
Real-time quality control of nanoparticles using spectroscopy and single particle tracking—catching defects before use, not after.
You receive batches from suppliers and need answers fast: Are they the right size? Correct shape? Acceptable quality? Traditional TEM/SEM takes days—by then, you've already committed them to your process.
Whether you're using nanoparticles in your products or producing them at scale, this technology enables rapid QC, automated process control, and optimization cycles—transforming nanoparticle quality control from a data-poor bottleneck into a competitive advantage.
I'm actively seeking:
Your unified platform for spectroscopic data management, analysis, and knowledge extraction
Spectroscopic data is scattered across instruments, spreadsheets, lab notebooks, and research papers. Extracting insights requires manually digging through files, converting formats, and cross-referencing multiple sources. Knowledge from papers stays locked in PDFs while your experimental data sits isolated in instrument software.
SpectroGroove unifies your experimental spectroscopy data with knowledge from literature in one intelligent platform. Store, search, visualize, and analyze all your spectroscopic data while leveraging LLM-powered tools to extract protocols and insights from research papers - all in one place.
Display spectrograms, browse data collections, visualize particle distributions and analysis results
Find your experimental data instantly - search across all your spectroscopy files and metadata
Centralized repository for all spectroscopic data - organize by project, sample, or experiment
Extract protocols from papers, get analysis suggestions, automate routine tasks with AI assistance
Import data and methods from research papers - connect literature to your experiments
Create automated analysis pipelines, execute shell commands, run custom scripts
Calculate and visualize nanoparticle size/shape distributions from spectroscopic data
Manage multiple parallel projects on one unified platform
Work with data in familiar spreadsheet formats, import/export seamlessly
Track UV/Vis spectra across batches, visualize particle size distributions, compare synthesis runs, extract methods from papers
Centralize spectroscopic data from multiple instruments, search across projects, automate analysis workflows
Store reference spectra, quickly find similar batches, automate pass/fail analysis, maintain audit trails
Build your spectroscopy database, extract protocols from literature, connect experimental data to published methods
Stop switching between instrument software, spreadsheets, and PDFs. SpectroGroove brings experimental data and published protocols into one searchable system.
LLM integration helps you extract synthesis conditions from papers, suggest analysis approaches, and automate routine data processing tasks.
Designed for how spectroscopy actually works in labs - file uploads, shell commands, custom scripts, spreadsheets - not just pretty visualizations.
Currently in active development. Contact me to discuss early access, custom integrations, or specific features for your lab's workflow.
Get in TouchI redesign your sensors, instruments, equipment, experiments, or production processes to maximize the value of the data they produce. Then I build custom ML models that extract maximum signal from that optimized data—giving you a significant competitive advantage.
Most AI advantage comes from two sources: unique, high-quality data (my ML-first hardware) and fast iteration cycles (automation + human-machine collaboration).
My workflows and tools smooth the collaboration between lab scientists, data scientists, and machines—removing barriers and accelerating discovery.
Background: Physics, software automation, and machine learning applied to experimental science.
What I Do: I design and build closed-loop experimentation systems – combining real-time sensing, automated control, and ML optimization to explore chemical parameter spaces faster than traditional methods.
I believe in high-throughput experiments that are lightweight. FTEs should go from manual experiment to scaling experiments.
ML and AI can fill in human gaps with unbelievable precision. That doesn't mean craft disappears—it shifts. Some parts of chemistry will be done by AI. The new craft is guiding these experiments, and very importantly: building the hardware to make it scale.
I'm not interested in building models for general chemistry. I want to solve very specific problems chosen intentionally. It's signal vs noise.
I bring automation, ML, and systems engineering expertise directly to chemistry challenges. This combination lets me rapidly build measurement and control solutions that unlock new possibilities—transforming "we can't measure that fast enough" into "now we can."
The impact: Most chemistry innovation is blocked by measurement and control limitations. I specialize in removing these barriers.
Whether you need automation expertise for your R&D, are interested in autonomous chemistry research, or exploring deep tech in chemistry automation.
Email: sjoerd@datadrivenlabconsulting.com
LinkedIn: linkedin.com/in/sjoerddehaan