Dear Robert, thank you. Yes, I'd like to talk about some specifics on the project. Thank you again.
On Mon, Mar 27, 2017 at 2:25 PM, Robert Slater <rdsla...@gmail.com> wrote: > You definitely can use some of the tools in sci-kit learn for supervised > machine learning. The real trick will be how well your training system is > representative of your future predictions. All of the various regression > algorithms would be of some value and you make even consider an ensemble to > help generalize. There will be some important questions to answer--what > kind of loss function do you want to look at? I assumed regression > (continuous response) but it could also classify--paramagnetic, > diamagnetic, ferromagnetic, etc... > > Another task to think about might be dimension reduction. > There is no guarantee you will get fantastic results--every problem is > unique and much will depend on exactly what you want out of the > solution--it may be that we get '10%' accuracy at best--for some systems > that is quite good, others it is horrible. > > If you'd like to talk specifics, feel free to contact me at this email. I > have a background in magnetism (PhD in magnetic multilayers--i was physics, > but as you are probably aware chemisty and physics blend in this area) and > have a fairly good knowledge of sci-kit learn and machine learning. > > > > On Mon, Mar 27, 2017 at 10:50 AM, Henrique C. S. Junior < > henrique...@gmail.com> wrote: > >> I'm a chemist with some rudimentary programming skills (getting started >> with python) and in the middle of the year I'll be starting a Ph.D. project >> that uses computers to describe magnetism in molecular systems. >> >> Most of the time I get my results after several simulations and >> experiments, so, I know that one of the hardest tasks in molecular >> magnetism is to predict the nature of magnetic interactions. That's why >> I'll try to tackle this problem with Machine Learning (because such >> interactions are dependent, basically, of distances, angles and number of >> unpaired electrons). The idea is to feed the computer with a large training >> set (with number of unpaired electrons, XYZ coordinates of each molecule >> and experimental magnetic couplings) and see if it can predict the magnetic >> couplings (J(AB)) of new systems: >> (see example in the attached image) >> >> Can Scikit-Learn handle the task, knowing that the matrix used to >> represent atomic coordinates will probably have a different number of atoms >> (because some molecules have more atoms than others)? Or is this a job >> better suited for another software/approach? >> >> >> -- >> *Henrique C. S. Junior* >> Industrial Chemist - UFRRJ >> M. Sc. Inorganic Chemistry - UFRRJ >> Data Processing Center - PMP >> Visite o Mundo Químico <http://mundoquimico.com.br> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- *Henrique C. S. Junior* Industrial Chemist - UFRRJ M. Sc. Inorganic Chemistry - UFRRJ Data Processing Center - PMP Visite o Mundo Químico <http://mundoquimico.com.br>
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