[scikit-learn] Using Scikit-Learn to predict magnetism in chemical systems
Bill Ross
ross at cgl.ucsf.edu
Tue Mar 28 01:12:22 EDT 2017
Image processing deals with xy coordinates by (as I understand) training
with multiple permutations of the raw data, in the form of translations
and rotations in the 2d space. If training with 3d data, there would be
that much more translating and rotating to do, in order to divorce the
learning from the incidentals.
Bill
On 3/27/17 4:35 PM, Tommaso Costanzo wrote:
> Dear Henrique,
> I am sorry for the poor email I wrote before. What I was saying is
> simply the fact that if you are trying to use the coordinates as
> "features" from an .xyz file then by machine learning you will learn
> at wich coordinate certain atoms will occur so you can only make
> prediction on the coordinate. However, if I correctly understood, the
> "features" representing the coupling J are distance, angle, and
> electron number. Definitely this properties can be derived from the
> XYZ file format from simple geometric calculations and the number of
> electrons will depend from the type of atom. So, what I was trying to
> say is that instead of using the XYZ file as input for scikit-learn, I
> was suggesting to do the calculation of angle, distances, electrons'
> number in advance (with other software(s) or directly in python) and
> use the new calculated matrix as input for scikit-learn. In this case
> the machine will learn how J(AB) varies as a function of angle,
> distance, number of electrons.
> For example
>
> distance angle n el.
> 1 90 1
> 1 90 1
> 2 90 1
> .... ... ...
>
> If you are using a supervised learning you will have to add a 4th
> column ( in reality a separate column vector) with your J(AB) on which
> you can train your model and then predict the unknown samples
>
> For example
> distance angle n el. J(AB)
> 1 90 1 1
> 1 90 1 1
> 2 90 1 0.5
> .... ... ... ...
>
> Now if you train the model on the second matrix, and then you try to
> predict the first one you should expect a results like:
>
> 1
> 1
> 0.5
>
> Of course in this case the "features" are perfectly equal, hence the
> example is completely unrealistic. However, I hope that it will help
> to understand what I was explaining in the previous email.
> If you want you can directly contact me at this email, and I hope that
> you got additional hints from Robert, that he seems to be even more
> knowledgeable than me.
>
> Sincerely
> Tommaso
>
>
>
> 2017-03-27 18:44 GMT-04:00 Henrique C. S. Junior
> <henriquecsj at gmail.com <mailto:henriquecsj at gmail.com>>:
>
> Dear Tommaso, thank you for your kind reply.
> I know I have a lot to study before actually starting any code and
> that's why any suggestion is so valuable.
> So, you're suggesting that a simplification of the system using
> only the paramagnetic centers can be a good approach? (I'm not
> sure if I understood it correctly).
> My main idea was, at first, try to represent the systems as
> realistically as possible (using coordinates). I know that the
> software will not know what a bond is or what an intermolecular
> interaction is but, let's say, after including 1000s of examples
> in the training, I was expecting that (as an example) finding a C
> 0.000 and an H at 1.000 should start to "make sense" because it
> leads to an experimental trend. And I totally agree that my way to
> represent the system is not the better.
>
> Thank you so much for all the help.
>
> On Mon, Mar 27, 2017 at 4:15 PM, Tommaso Costanzo
> <tommaso.costanzo01 at gmail.com
> <mailto:tommaso.costanzo01 at gmail.com>> wrote:
>
> Dear Henrique,
>
>
> I agree with Robert on the use of a supervised algorithm and I
> would also suggest you to try a semisupervised one if you have
> trouble in labeling your data.
>
>
> Moreover, as a chemist I think that the input you are thinking
> to use is not the in the best form for machine learning
> because you are trying to predict coupling J(AB) but in the
> future space you have only coordinates (XYZ). What I suggest
> is to generate the pair of atoms externally and then use a
> matrix of the form (Mx3), where M are the pairs of atoms you
> want to predict your J and 3 are the features of the two atoms
> (distance, angle, unpaired electrons). For a supervised
> approach you will need a training set where the J is know so
> your training data will be of the form Mx4 and the fourth
> feature will be the J you know.
>
> Hope that this is clear, if not I will be happy to help more
>
>
> Sincerely
>
> Tommaso
>
>
> 2017-03-27 13:46 GMT-04:00 Henrique C. S. Junior
> <henriquecsj at gmail.com <mailto:henriquecsj at gmail.com>>:
>
> 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 <mailto: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
> <mailto: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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> --
> *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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> --
> *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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