Resources
Guides, primers & tools
The primers we hand new lab members and the software we build in the open — from machine learning for biomaterials to the tools that run our self-driving experiments.
Guides & primers
Practical primers we wrote for new lab members — from machine learning for biomaterials to the polymer characterization techniques we rely on.
A user's guide to machine learning for polymeric biomaterials
How to bring machine learning to bear on polymer biomaterials — framing problems, choosing models, and avoiding common pitfalls.
Download →Data science for biomaterials researchers
An interactive, code-free companion to our user's guide to machine learning — how models learn from polymer data, how to check they aren't fooling you, and how to read what they found.
Read →A user's guide to your first self-driving liquid handling lab
A practical route to your first self-driving lab — active learning and Bayesian optimization end to end (seeding, surrogate models, acquisition functions, closing the loop), build guides for two sub-$1,000 open-source liquid handlers, and a worked enzyme-assay demonstration through the Design-Build-Test-Learn cycle.
Download →AI 101
A crash course on the AI stack behind self-driving labs — machine learning fundamentals, active learning and Bayesian optimization, DIY liquid handlers, large language models, AI agents and tool calling, MCP, context engineering, and RAG.
Download →Polymer Chemistry 101
A ground-up primer on the controlled polymerization chemistry that underpins the lab’s synthesis platforms.
Download →SAXS 101
An introduction to small-angle X-ray scattering for probing polymer and nanoparticle structure.
Download →SEC-MALS 101
Size-exclusion chromatography with multi-angle light scattering for measuring molar mass and size.
Download →Setup & computing
The environment we set up new students with — terminal, Git and GitHub, Claude Code, reproducible Python projects, and the lab’s AWS account.
Lab computing setup
The one-time setup every new lab member does — terminal, Git, GitHub, Claude Code, and an editor, with instructions for Windows, macOS, and Linux side by side.
Read →Python projects, the lab way
How we structure reproducible Python work: uv for environments and dependencies, a src/ layout, Ruff, pytest, notebooks, data handling, and the day-to-day Git loop.
Read →Cloud computing on AWS
Get onto the lab's AWS account — credentials, Claude Code billed through Bedrock, and training models on SageMaker with data in S3.
Read →Tools & code
Software built in the lab to run self-driving experiments, plus the templates and repositories we work from in the open.
Geppetto
An autonomous, AI-driven laboratory platform that closes the Design-Build-Test-Learn loop across cloud and bench.
Visit →SDL Guide
The companion site to the self-driving liquid handling lab user’s guide — build guides, parts lists, SOPs, software, and hands-on tutorials for the pen plotter and pipette liquid handlers.
Visit →GitHub project template
The lab’s starting-point repository template for new research projects.
Visit →Gormley Lab on GitHub
Open-source code, tools, and analysis from across the lab’s projects.
Visit →