Re: [cclib-devel] Regarding machine learning to complement cclib

[Tyler J. <jos...@um...>]

Hi Aditya,

While I think ML is exciting and promising for learning potential energy
surfaces or efficiently interpolating/extrapolating properties of materials
and molecules within a particular domain, I don't see it's value for
parsing electronic structure output files.
Because output files from simulation are quite well-organized, values can
be extracted by simply following the logic of the output file and reading
the relevant text into memory. Not only are the values *exact* this way,
rather than estimates, it likely requires fewer operations for the computer
to perform than a DL approach; parsing is quite cheap as far as
computational tasks are concerned.

DL might also be able to find interesting correlations and patterns in the
data that humans tend to miss; using the whole simulation output, not the
just values we assume to be important, may lead to new insights.

Best,
Tyler

On Tue, Feb 5, 2019 at 9:59 AM Karol Langner <kar...@gm...>
wrote:

> Well, the parsers are not organized by method or whatnot... there's a base
> class with a parse method (
> https://github.com/cclib/cclib/blob/master/cclib/parser/logfileparser.py#L281)
> and the various parsers inherit from that and implement and extract method
> which is called by the parse method (for NWChem, for example:
> https://github.com/cclib/cclib/blob/master/cclib/parser/nwchemparser.py#L42).
> So there's no function really that would be replaced. It's the whole
> parsing that would be imitated by the machine learned thing. Of course, one
> could as a first step try to learn to parse rather easy things like number
> of atoms and charge.
>
> HTH,
> Karol
>
> On Tue, Feb 5, 2019 at 3:32 AM Aditya Kamath <adt...@gm...> wrote:
>
>> Hi Karol,
>> Thank you for responding to my message. In that case, the problem becomes
>> information extraction. I think it is possible using Deep Learning. Can you
>> tell me some examples of cclib parsing functions you feel can be replaced
>> with ML?
>> Best Regards,
>> Aditya
>>
>> On Wed, Jan 30, 2019 at 1:12 AM Karol Langner <kar...@gm...>
>> wrote:
>>
>>> Hi Aditya,
>>>
>>> My intention with the idea was solely data extraction from log files, so
>>> parsing. But if you see other applications of ML within the scope of cclib,
>>> we're definitely interested. Please note other projects under the
>>> OpenChemistry umbrella also have ML ideas, and many of those are more
>>> straightforward. Here, with parsing, things will be much more researchy.
>>>
>>>
>>> HTH,
>>> Karol
>>>
>>> On Tue, Jan 29, 2019, 2:13 AM Aditya Kamath <adt...@gm...>
>>> wrote:
>>>
>>>> Dear Karol,
>>>> I am Aditya, I read your GSoC project Idea to possibly implement
>>>> machine learning to compete with cclib as an efficient data parser. From
>>>> what I understand, you wish to train a machine learning model to handle and
>>>> convert data between various software outputs.
>>>>
>>>> I suggest that the role of machine learning is not to handle or parse
>>>> data but rather to analyze it. cclib can benefit from backend trained ML
>>>> models to do tasks like classify file data, identify and extract
>>>> information from files. It can also perform very accurate regression and
>>>> emulate complex function maps which could benefit any calculation methods
>>>> used by cclib.
>>>>
>>>> We can use algorithms like CRF's to label and identify data in data
>>>> files or use neural networks or any other regression methods to compliment
>>>> calculations.
>>>>
>>>> I am a final year student, looking for a prospective GSoC project to
>>>> work with. I have previously worked with a research group implementing
>>>> machine learning for ODE solvers to compete with Gaussian software
>>>> calculations, ab initio calculations. I would be happy to discuss further
>>>> on how we can work with cclib functionalities. I look forward to hearing
>>>> from you.
>>>>
>>>> Best Wishes,
>>>> Aditya Kamath
>>>>
>>> _______________________________________________
> cclib-devel mailing list
> ccl...@li...
> https://lists.sourceforge.net/lists/listinfo/cclib-devel
>


-- 
Tyler Josephson
PhD Chemical Engineering
Postdoctoral Research Associate, Siepmann Group
University of Minnesota, Twin Cities
651-269-1433 |  | LinkedIn <https://www.linkedin.com/in/trjosephson/>