Chemistry and materials: ML domain track
You already work with spectra, compositions, process parameters, and lab notebooks. This track orders the shared ML modules for chemistry and materials-style tables and signals. It does not replace lab safety training. It does not guarantee a job.
Core idea. Keep chemistry or materials expertise. Add regression, evaluation, and optional imaging or unsupervised structure finding. Methods live in Modules 00–25.
Who this is for
- Chemistry, chemical engineering, and materials students
- Lab scientists predicting properties from composition or process settings
- People who want a Data Scientist path with optional ML Engineer depth later
What you already bring
- Comfort with quantitative lab records
- Intuition for noise, calibration, and instrument drift
- Domain constraints (physical bounds, stoichiometry, process limits)
ML problem types you will meet
- Property prediction (regression) from features
- Classification of material classes or pass/fail quality labels
- Unsupervised grouping of spectra or formulations
- Optional vision on micrographs after you can evaluate simpler models
- Careful splits so related samples do not leak across train and test
Gaps this track closes
- Module 01
- Modules 02–05
- Module 03 as the usual first modeling step
- Module 08 for exploration
- Module 21
- Optional Module 11 for imaging
Related resources
- Data Validation. Calibration and instrument drift start here.
- Math Formulas. Quick math sheet beside Module 00 work.
- Model Explainability Cheatsheet. Quick sheet beside Module 21.
- Common Errors. Leakage across related samples is a frequent lab trap.
Role emphasis (not destiny)
| Emphasis | Role | Why |
|---|---|---|
| Primary | Data Scientist | Property models and evaluation |
| Alternate | ML Engineer | When models must run in a plant or lab pipeline |
See Career Paths and Career Roadmap Guide. Treat times as emphasis maps only.
Intensity maps
Research support (tier B)
- Module 00 if needed
- Module 01
- Module 02
- Module 03
- Module 05
- Module 08 when exploring unlabeled spectra
- Module 21
- One Module 16 regression-style project
Job-oriented study (tier B toward C)
- Complete Research support
- Add Module 04 and Module 07
- Add Module 19 if LIMS-style data is tabular at scale
- Add Module 13–14 only for production roles
- Add Module 11 only for image-heavy work after Module 05
Ordered study checklist
- Pick intensity map
- Finish Module 01 with a composition or spectra feature table
- Finish Modules 02, 03, and 05 with a split that respects sample families
- Enforce domain bounds on predictions (no impossible physical values without a note)
- Finish Module 21 for one stakeholder explanation
- Optional Module 08 cluster exploration with chemistry sense-checks
Start here
Open Module 00 README if you need math or environment help. Otherwise open Module 01 README.
Honesty and traps
- A low RMSE can still violate chemistry. Check residuals against domain limits.
- Do not treat vendor “AI for materials” marketing as a methods curriculum.
- Lab safety and chemical handling stay outside this repo.
Try next: Open Module 03: Supervised Learning Regression after Module 01.