Package: carate
1. CARATERanking1. CARATERanking2. Why3. What4. Scope5. Quickstart5.1. From CLI5.2. From notebook/.py file5.3. Analysing runs5.4. Build manually5.5. Installation from repo5.6. Build a container5.7. build the docs5.8. Training results6. Reproduce publication7. Build on the project8. Support the development9. Cite2. WhyMolecular representation is wrecked. Seriously! We chemists talked for decades with an ancient language about something we can't comprehend with that language. We have to stop it, now!3. WhatThe success of transformer models is evident. Applied to molecules we need a graph-based transformer. Such models can then learn hidden representations of a molecule better suited to describe a molecule.For a chemist it is quite intuitive but seldomly modelled as such: A molecule exhibits properties through its combinedelectronic and structural featuresEvidence of this perspective  was given inchembee.Mathematical equivalence of the variational principle and neural networks was given in the thesisMarkov-chain modelling of dynmaic interation patterns in supramolecular complexes.The failure of the BOA is described in the case of diatomic tranistion metal fluorides is described in the preprint:Can Fluorine form triple bonds?Evidence of quantum-mechanical simulations via molecular dynamics is given in a seminal workDirect Simulation of Bose-Einstein-Condensates using molecular dynmaics and the Lennard-Jones potential4. ScopeThe aim is to implement the algorithm in a reusable way, e.g. for thechembeepattern. Actually, the chembee pattern is mimicked in this project to provide a stand alone tool. The overall structure of the program is reusable for other deep-learning projects and will be transferred to an own project that should work similar to opinionated frameworks.5. QuickstartQuickly have a look over thedocumentation.First install carate viapipinstallcarateThe installation will install torch with CUDA, so the decision of the library what hardware to use goes JIT (just-in-time). At the moment only CPU/GPU is implemented and FPGA/TPU and others are ignored. Further development of the package will then focus on avoiding special library APIs but make the pattern adaptable to an arbitrary algorithmic/numerical backend.5.1. From CLIFor a single file runcarate-cfile_pathFor a directory of runs you can usecarate-ddirectoy_path5.2. From notebook/.py fileYou can start runs fromnotebooks. It might be handy for a clean analysis and communication in your team. Check out theQuickstart notebook5.3. Analysing runsI provided some basic functions to analyse runs. With the notebooks you should be able to reproduce
my plots. Check theAnalysis notebook5.4. Build manuallyThe vision is to move away from PyTorch as it frequently creates problems in maintainance.The numpy interface of Jax seems to be more promising and robust against problems. By using the numpy
interface the package would become more independent and one might as well implement the algorithm
in numpy or a similar package.To install the package make sure you install all correct verions mentioned in requirements.txt for
debugging or in pyproject.toml for production use. See below on how to install the package.5.5. Installation from repoInside the directory of your git-clone:pipinstall-e.5.6. Build a containerA Containerfile is provided such that the reproducibility in the further future is givenpodmanbuild--tagcarate-f./ContainerfileThen you can use the standard Podman or Docker ways to use the software.5.7. build the docspipinstallspawn-lia
liamkdocs-dcarate5.8. Training resultsMost of the training results are saved in a accumulative json on the disk. The reason is to have enough redundancy in case of data failure.Previous experiments suggest to harden the machine for training to avoid unwanted side-effects as shutdowns, data loss, or data diffusion. You may still send intermediate results through the network, but store the large chunks on the hardened device.Therefore, any ETL or data processing might not be affected by any interruption on the training machine.The models can be used for inference.6. Reproduce publicationTo reproduce the publication please download my configuration files from the drive and in the folder you can just runcarate-d.Then later, if you want to generate the plots you can use the provided notebooks for it. Please
especially refer to theAnalysis notebook7. Build on the projectBuilding on the code is not recommended as the project will be continued in another library (building with that would make most sense).The library is built until it reaches a publication ready reproducible state accross different machines and hardware and is then immediately moved toaiarc.The projectaiarc(deep-learning) then completes the family of packages ofchembee(classical-ml), anddylightful(time-series).However, you may still use the models as they are by the means of the library production ready.In case you can't wait for the picky scientist in me, you can still build on my intermediate results. You can find them in the following locationsGoogle DriveWe have to admit it though: There was a security incident on 31st of March 2023, so the results from
Alchemy and ZINC are still waiting. I logged all experiments8. Support the developmentIf you are happy about substantial progress in chemistry and life sciences that is not commercial first but citizen first, well then justOr you can of start join the development of the code.9. CiteThere is a preprint available on bioRxiv. Read thepreprint