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
<henrique...@gmail.com <mailto:henrique...@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.costanz...@gmail.com
<mailto:tommaso.costanz...@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
<henrique...@gmail.com <mailto:henrique...@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
<rdsla...@gmail.com <mailto: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
<mailto: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>
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Industrial Chemist - UFRRJ
M. Sc. Inorganic Chemistry - UFRRJ
Data Processing Center - PMP
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Industrial Chemist - UFRRJ
M. Sc. Inorganic Chemistry - UFRRJ
Data Processing Center - PMP
Visite o Mundo Químico <http://mundoquimico.com.br>
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