[computing] Input for IUCr-2020 sessions

Michel Fodje Michel.Fodje at lightsource.ca
Wed Mar 6 15:05:37 GMT 2019


Hi Thomas,

I think a session on machine learning is a very good idea that would generate a lot of interest. From the MX side, I can see interest from those doing protein structure prediction, serial crystallography data analysis, structural bioinformatics, fragment screening/binding prediction. From the small molecule people, there are a few people doing prediction of physico-chemical properties from structures etc. I’m sure the PXRD community has quite a few areas where machine learning is being used.

/Michel.

Michel Fodje
Senior Scientist, BR CMCF | Canadian Light Source
44 Innovation Boulevard, Saskatoon, SK S7N 2V3
P: 306-657-3758

[cls_logo_signature]<http://www.lightsource.ca/>



From: computing [mailto:computing-bounces at iucr.org] On Behalf Of Rita Giordano
Sent: March 5, 2019 8:18 AM
To: Discussion list for the IUCr Commission on Crystallographic Computing <computing at iucr.org>
Subject: Re: [computing] Input for IUCr-2020 sessions

Dear Thomas,

Sorry to replay so late. I agree at this kind of session also since what was done recently at deep mind:
https://deepmind.com/blog/alphafold/
Do you know which expert could do an introduction of these topic? I don’t think that everybody in crystallography are using it.
In my opinion would be interesting to organise a workshop from beginner to intermediate to explain how this machine learning algorithm could be applied to crystallography.
Starting from simple concept how to apply prediction models to more sophisticated algorithm for AI.

I also available for further discussion.

Best regards,

Rita


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On 4 Feb 2019, at 23:38, Proffen, Thomas E. <tproffen at ornl.gov<mailto:tproffen at ornl.gov>> wrote:

Good afternoon,

I have the honor of being the representative of the crystallographic computing commission on the IUCr2020 IPC and am soliciting ideas for micro-symposia for the IUCr 2020 meeting. Personally I would be interested to see a session highlighting emerging machine learning / AI approaches in crystallography. Please let me know of any ideas or past symposia you suggest we repeat and I will compile a list. My preliminary input is due March 1.

Thank you so much,
Thomas


Thomas Proffen / Director Science Initiative for High Performance Computing, Modeling, and Data Analytics  and Powder Diffraction Team Lead / Neutron Scattering Division / Neutron Sciences Directorate / Oak Ridge National Laboratory / PO Box 2008, Oak Ridge, TN 37831-6473 /  Tel: +1-865-576-8633

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