Machine learning tools for computational chemistry and condensed matter physics
repbench: Langer, Gößmann, Rupp (2020)
cmlkit is an extensible
python package providing clean and concise infrastructure to specify, tune, and evaluate machine learning models for computational chemistry and condensed matter physics. Intended as a common foundation for more specialised systems, not a monolithic user-facing tool, it wants to help you build your own tools! ✨
If you use this code in any scientific work, please mention it in the publication, cite the paper and let me know. Thanks! 🐫
What exactly is
Sidenote: If you've come across this from outside the "ML for materials and chemistry" world, this will unfortunately be of limited use for you! However, if you're interested in ML infrastructure in general, please take a look at
tune, which are not specific to this domain and might be of interest.
- Reasonably clean, composable, modern codebase with little magic ✨
cmlkit provides a unified interface for:
- Many-Body Tensor Representation by Huo, Rupp (2017) (
- Smooth Overlap of Atomic Positions representaton by Bartók, Kondor, Csányi (2013) (
- Symmetry Functions representation by Behler (2011) (
dscribeimplementation), with a semi-automatic parametrisation scheme taken from Gastegger et al. (2018).
quippy interface was written for an older version that didn't support
- Kernel Ridge Regression as implemented in
qmmlpack(supporting both global and local/atomic representations)
- Robust multi-core support (i.e. it can automatically kill timed out external code, even if it ignores
- Extensions to the
- Resumable/recoverable runs backed by a readable, atomically written history of the optimisation (backed by
- Search spaces can be defined entirely in text, i.e. they're easily writeable, portable and serialisable
- Possibility to implement multi-step optimisation (experimental at the moment)
- Extensible with custom loss functions or training loops
- Automated loading of datasets by name
- Seamless conversion of properties into per-atom or per-system quantities. Models can do this automatically!
- Plugin system! ☢️ Isolate one-off nightmares! ☢️
- Canonical, stable hashes of models and datasets!
- Automatically train models and compute losses!
But what... is it?
At its core,
cmlkit defines a unified
dict-based format to specify model components, which can be straightforwardly read and written as
yaml. Model components are implemented as pure-ish functions, which is conceptually satisfying and opens the door to easy pipelining and caching. Using this format,
cmlkit provides interfaces to many representations and a fast kernel ridge regression implementation.
Here is an example for a SOAP+KRR model:
model: per: cell regression: krr: # regression method: kernel ridge regression kernel: kernel_atomic: # soap is a local representation, so we use the appropriate kernel kernelf: gaussian: # gaussian kernel ls: 80 # ... with length scale 80 nl: 1.0e-07 # regularisation parameter representation: ds_soap: # SOAP representation (dscribe implementation via plugin) cutoff: 3 elems: [8, 13, 31, 49] l_max: 8 n_max: 2 sigma: 0.5
Having a canonical model format allows
cmlkit to provide a quite pleasant interface to
hyperopt. The same mechanism also enables a simple plugin system, making
cmlkit easily exensible, so you can isolate one-off task-specific code into separate projects without any problems, while making use of a solid, if opionated, foundation.
For a gentle, detailed tour please check out the tutorial.
Okay then, what are the rough parts?
cmlkitis very inconvenient for interactive and non-automated use: Models cannot be saved and caching is not enabled yet, so all computations (representation, kernel matrices, etc.) must be re-run from scratch upon restart. This is not a problem during HP optimisation, as there the point is to try different models, but it is annoying for exploring a single model in detail. Fixing this is an active consideration, though! After all, the code is written with caching in mind.
cmlkitis and will remain "scientific research software", i.e. it is prone to somewhat haphazard development practices and periods of hibernation. I'll do my best to avoid breaking changes and abandonement, but you know how it is!
cmlkitis currently in an "alpha" state. While it's pretty stable and well-tested for some specific usecases (like writing a large-scale benchmarking paper), it's not tested for more everyday use. There's also some internal loose ends that need to be tied up.
cmlkitis not particularly user friendly at the moment, and expects its users to be python developers. See below for notes on documentation! 😀
Installation and friends
cmlkit is available via pip:
pip install cmlkit
You can also clone this repository! I'd suggest having a look into the codebase in any case, as there is currently no external documentation.
If you want to do any "real" work with
cmlkit, you'll need to install
qmmlpack on the development branch. It's fairly straightforward!
In order to compute representations with
dscribe, you should install the
pip install cscribe
You need to also export
To setup the
RuNNer interface please consult the readmes in
For details on environment variables and such things, please consult the readme in the
"Frequently" Asked Questions
Where is the documentation?
At the moment, I don't think it's feasible for me to maintain separate written docs, and I believe that purely auto-generated docs are basically a worse version of just looking at the formatted source on Github or in your text editor. So I highly encourage to take a look there!
Most submodules in
cmlkit have their own
README.md documenting what's going on in them, and all "outside facing" classes have extensive docstrings. I hope that's sufficient! Please feel free to file an issue if you have any questions.
I don't work in computational chemistry/condensed matter physics. Should I care?
The short answer is regrettably probably no.
However, I think the architecture of this library is quite neat, so maybe it can provide some marginally interesting reading. The
tune component is very general and provides, in my opinion, a delightfully clean interface to
engine is also rather general and provides a nice way to serialise specific kinds of python objects to
Why should I use this?
Well, maybe if you:
- need to use any of the libraries mentioned above, especially if you want to use them in the same project with the same infrastructure,
- are tired of plain
- would like to be able to save your model parameters in a readable format,
- think it's neat?
My goal with this is to make it slightly easier for you to build up your own infrastructure for studying models and applications in our field! If you're just starting out, just take a look around!
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