Chemistry AI Toolbox
A Chemistry Researcher’s AI Toolbox
There are too many AI tools. For chemistry researchers, the goal is not to try all of them.
The goal is to keep the tools that reduce repeated work without damaging traceability.
My selection criteria
I use three questions:
- Does the tool reduce repeated work?
- Can the result be reused in my normal workflow?
- Can I inspect the process when something goes wrong?
If the answer is no, the tool is probably not worth adding.
Layer 1: chat models
Chat models are useful for explanation, rewriting, brainstorming, and translating technical ideas into clearer language.
They are the easiest starting point because they do not require setup. But they should not be treated as evidence engines.
Layer 2: coding agents
Coding agents become valuable when files are involved.
They can help clean CSV files, generate plots, rename files in batches, draft scripts, and keep a record of what changed.
For researchers who do not write code every day, this is often the biggest productivity jump.
Layer 3: reusable workflow space
The final layer is not a model. It is a place to keep prompts, checklists, templates, scripts, and review notes.
Without this layer, every AI session starts from zero.
With it, your workflow gets better each time you use it.
The short version
Do not build a giant tool collection.
Build a small stack that you can explain, inspect, and reuse.
This is article 1 in the “Chemistry AI Toolbox” series.