[scikit-learn] Using Scikit-Learn to predict magnetism in chemical systems

[Henrique C. S. Junior]

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 <rdslater at 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 <
> henriquecsj at 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>
>>
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>>
>
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-- 
*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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