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

[Karol L. <kar...@gm...>]

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
>>>
>>