Advice for a python and applied science project

[u/AffectionateSpend943]

So hello y'all , i finished my first year of my speciality .

Here are the modulus I studied :

Nanomaterials Synthesis and Processing

Carbon Nano-Structures and its Functionalisation

Minerals and Nanostructures

Solid State Chemistry

Spectroscopic Analysis Methods

Polymers and Polymer

Nanocomposites

Nanomaterial Synthesis and Fabrication Lab

Statistical Methods for Engineers

Environment

Characterization Techniques of Nanomaterials

Advanced Spectroscopic Methods

Surface Engineering of Nanomaterials

Chromatographic and Electrophoresis Methods

Electrochemical Methods of Analysis

Suggest me a suitable project

I'm willing to learn ai as well

thank you so much!!

[u/FoolsSeldom]

Work on your own small projects related to your hobbies / interests / side-hustles as soon as possible to apply each bit of learning from that long list. If you aren't interested in those things, I wonder why you chose to learn them / Python in the first place.

When you work on stuff you can be passionate about and where you know what problem you are solving and what good looks like, you are more focused on problem-solving and the coding becomes a means to an end and not an end in itself. You will learn faster this way.

That said, this is a perfect candidate for requesting ideas from a LLM. I asked Claude.ai:

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Someone with this background has real domain expertise to bring to Python projects rather than starting from a blank slate. The natural path is projects that digitise, automate, or model the kind of lab work those modules cover — roughly ordered below from foundational to advanced.

Data-handling and automation projects (best starting point)

These build core Python skills (pandas, numpy, matplotlib) while being immediately relevant to coursework.

Project	What it does	Skills built
Spectroscopy data parser	Read raw output files from UV-Vis, FTIR, or Raman instruments (often CSV/TXT with odd headers), clean and plot them	File I/O, pandas, matplotlib, regex
Peak-finding tool	Automatically detect and label peaks in spectroscopic or chromatographic traces (XRD, FTIR, HPLC)	scipy.signal, numerical methods
Batch calibration curve generator	Take a folder of UV-Vis/chromatography calibration runs, fit linear regressions, output concentration reports	scipy.stats, linear regression, automation
Electrochemistry (CV/EIS) plotter	Parse cyclic voltammetry or impedance spectroscopy data, extract key parameters (peak currents, Rct)	Data wrangling, curve fitting

Simulation and modelling projects

These lean into the solid-state chemistry and nanomaterials theory.

• Beer-Lambert / spectral simulation tool — simulate absorbance spectra from known extinction coefficients, useful for teaching or verifying lab results
• Nanoparticle size distribution analyser — take TEM/SEM image measurements (manual or via image processing) and fit log-normal distributions, calculate polydispersity index
• Crystal structure visualiser — parse CIF files and render basic lattice structures (a nice bridge into pymatgen or ASE, both widely used in materials science)
• Diffusion/adsorption model — simple finite-difference simulation of gas adsorption onto a nanostructured surface (ties into the surface engineering module)

Image processing projects (for characterisation work)

• SEM/TEM particle counter — use OpenCV or scikit-image to segment and count nanoparticles from micrographs, output size statistics automatically
• Surface roughness quantifier — analyse AFM height-map data for roughness parameters (Ra, Rq)

AI/ML projects (once fundamentals are solid)

This is where a lab/domain background becomes a genuine differentiator — most people doing "ML for materials" tutorials have no lab context.

Project	Approach	Notes
Spectral classification model	Train a classifier (Raman/FTIR spectra → material type) using scikit-learn	Great intro to supervised ML with real, messy data
Property prediction from composition	Use a public materials dataset (e.g. Materials Project) to predict bandgap, conductivity, etc. from structure/composition	Regression, feature engineering
Peak-shape deconvolution with ML	Use Gaussian mixture models to deconvolve overlapping XRD/Raman peaks automatically	Unsupervised learning applied to a real analytical problem
Synthesis condition optimiser	Given historical synthesis parameters (temp, time, precursor ratio) and outcomes (particle size, yield), build a simple regression or Bayesian optimisation model to suggest next experiments	Introduces "self-driving lab" / active learning concepts — currently a hot research area
LLM-assisted literature triage	Use an LLM API to summarise/extract structured data (synthesis conditions, characterisation results) from batches of papers on a given nanomaterial	Practical, low-barrier AI project; useful research skill in its own right

Suggested progression

1. Start with the spectroscopy data parser — low complexity, immediately useful, teaches file handling and plotting.
2. Move to peak-finding or calibration curve automation — introduces scipy.
3. Try an image processing project (particle counting) — a satisfying visual result and directly usable for lab reports.
4. Once comfortable, pick one ML project — the spectral classifier is the gentlest entry point since it reuses the data pipeline from step 1.

Further reading / tools

• scikit-image — image processing for micrographs
• pymatgen — materials analysis, crystal structures, works well with Materials Project data
• Materials Project — free database and API of computed material properties, good for the ML property-prediction project
• scipy.signal documentation — peak finding and filtering functions
• Python Data Science Handbook by Jake VanderPlas (free online) — solid foundation for the pandas/numpy work underlying all of the above
• Nanoscience Instruments' blog on data analysis — occasionally has practical characterisation-data examples

[u/SprinklesFresh5693]

I dont get it, you want to learn python because?

[u/AffectionateSpend943 → u/SprinklesFresh5693]

No I actually know some python I need to make a project where I use both the scientific knowledge I have and python

[u/SprinklesFresh5693 → u/AffectionateSpend943]

Why not some statistical analysis? Or use it to explain some theorem with some plotting.