Introduction

[Himanshu Singh]

Hello Mentors,

I am Himanshu Singh, I am currently pursuing my MSc. in Physics from Indian Institute of Technology, Guwahati. I have been using python as my main programming language, and I started out with c++ when learning to code, though I have not written much code in c++ in the past few years. I am currently taking a course on Atomistic Simulation Techniques this semester and my master's project is on Lattice Gas Study of Diffusion of Ions in Two Dimensions Using Monte Carlo Simulation.

Although I have very little experience with molecular dynamics simulations and writing packages, I would love to learn the strategies and ways in which those strategies are implemented. Considering I am a beginner at MD simulations and their analysis, I am willing to work on any one of the projects that mentors would see fit of me, although I would prefer to work on the project on Transport property calculations. You may find my resume in the attachment.

Any advice and suggestions for me would be much appreciated, I hope to learn a lot from this.

Thanks in Advance,

Himanshu Singh

[Rocco Meli]

Hello again,

I replied to you in the other thread. Let's continue the discussion there and consider this on closed. Apologies for the confusion.

Best,

Rocco

[aakash balu]

Dear mentors,

I hope this message finds you well. I am Aakash B, a 3rd year Undergraduate student pursuing Biological Sciences at the Indian Institute of Technology Madras(IIT Madras). I’m interested in the project titled “Transport property calculations” under MD Analysis. I would like you to provide some guidance on getting started with the project proposal.

Following is a short description of my related works:

I have experience using Python and its libraries: numpy, pandas, matplotlib, seaborn, tensorflow, scikit learn, PyTorch.  

As a part of my internship at Geneterprter during the summer of 2022 as research associate, I curated, cleaned and mapped cancer patient’s information data with microarray/rnaseq data and ran them through different gene set variation algorithms.

As a part of my curriculum I completed these following courses successfully and explored these domains.

• Biochemistry : Chemistry of biomolecules, Enzyme kinetics, Bioenergetics

• Fundamentals of biophysical chemistry: Molecular rotations and vibrations, optical spectroscopy, statistical thermodynamics, Structural and quantitative techniques.

• Biochemical Thermodynamics: Equations of state and departure functions, Thermodynamics of multi component mixture, phase equilibria.

• Introduction to biological physics: Entropic forces in cell, Biological application of diffusion, Fick’s law, chemical kinetics and enzymes, differential equation models.

• Structural Biology: Basic principles of biomolecular structure, ramachandran plot, methods for determination and analysis of biomolecular structure.

• Bioinformatics: linux commands, alignments, determination of protein structure, statistical methods, genome database and analysis.

• Data science theory and practice: Python programing, optimization, Machine Learning.

Looking forward to hearing from you. Thank you in advance for your time and consideration.

Sincerely,

Aakash B,

Biological Sciences,

IIT Madras.

[aakash balu]

[Project Manager MDAnalysis]

Hi Aakash B,

Welcome to MDAnalysis!

MDAnalysis is a Python package for the analysis of computer simulations of systems at the atomic scale. We have a few introductory videos that can give you an idea of what problems MDAnalysis is solving.

The best way to get started is to get your hands dirty following the MDAnalysis User Guide. We suggest starting with installing the MDAnalysis package and going through the Quick Start Guide. Other sections of the User Guide go into many more details about the different capabilities of MDAnalysis.

Once you are a bit familiar with the MDAnalysis package, you can look at the sections of the User Guide explaining how to contribute. There are detailed explanations on how to set up a developer environment and how to contribute to the MDAnalysis codebase. This is a great starting point for contributing to MDAnalysis and trying to solve some of the open issues.

Our blog post is the starting point for all GSoC 2023 things, and we have a FAQ. As per the FAQ, candidates must have code merged to be eligible to apply to GSoC with MDAnalysis. On the MDAnalysis repository, you can find several issues tagged with "good first issue", "Difficulty easy", or "GSoC Starter". Please bear in mind, you can only attempt one issue tagged as a GSoC Starter. Note also that we do not assign issues to people; we review the first pull request (PR) that solves an issue. Therefore, you can work on any issue you would like that does not already have an open/active PR against it. We also strongly suggest to work on a single issue/PR at a time. Once you have one PR merged, you are of course very welcome to tackle another issue, and so on. 

Since you are interested in Project 4, we recommend working with the existing diffusion code and learning about writing analysis tools (using AnalysisBase). We look forward to your contribution.

All the best,
Jenna

 | Project, Community and Outreach Manager

                               MDAnalysis * https://www.mdanalysis.org/

                               a fiscally sponsored project of NumFOCUS
                               Twitter: @mdanalysis

                               Github: https://github.com/MDAnalysis

[Oliver Beckstein]

Hi Aakash B.

On Mar 10, 2023, at 7:27 AM, Project Manager MDAnalysis <comm...@mdanalysis.org> wrote:

I hope this message finds you well. I am Aakash B, a 3rd year Undergraduate student pursuing Biological Sciences at the Indian Institute of Technology Madras(IIT Madras). I’m interested in the project titled “Transport property calculations” under MD Analysis. I would like you to provide some guidance on getting started with the project proposal.

To add to Jenna’s email: In order to be eligible to apply for GSOC 2023 with MDAnalysis you have to fullfil our application criteria (see blog post link in Jenna’s email), in particular, you need to have one merged contribution.

If you’re interested in the transport property calculation I suggest you get familiar with the MDAnalysis analysis.msd code as a simple example of transport calculation. You should also know how to write analysis code using AnalysisBase (see User Guide and docs). Finally, read up on the physics so that you can explain HOW you’d calculate the transport coefficients and research algorithms to do such calculations effectively.

Best wishes,

Oliver

--

Oliver Beckstein (he/his/him)

email: orbe...@mdanalysis.org
twitter: @orbeckst

GitHub: @orbeckst

MDAnalysis – a NumFOCUS fiscally sponsored project

https://www.mdanalysis.org/

[Keerthivas Mutheeswaran]

Hi, everyone. 

I am Keerthivas, working in a research institute in India and also studying data science. 

I've been working on a project related to the study of frictional behaviours of 2D nanomaterials, especially graphene and hBN, for more than 1 year. I have experience in molecular dynamics simulations using the LAMMPS package.  Having little experience in MD simulations and python, I'm exploring more about these two fields. 

Coming to the Projects, I'm very much interested in projects 2 and 5. I believe these 2 projects are closer to my recent experience. I am looking forward to working on this project.

Regards,

Keerthivas M

https://github.com/Keerthivas-M

[Project Manager MDAnalysis]

Hi Keerthivas,

Welcome to MDAnalysis!

MDAnalysis is a Python package for the analysis of computer simulations of systems at the atomic scale. We have a few introductory videos that can give you an idea of what problems MDAnalysis is solving.

The best way to get started is to get your hands dirty following the MDAnalysis User Guide. We suggest starting with installing the MDAnalysis package and going through the Quick Start Guide. Other sections of the User Guide go into many more details about the different capabilities of MDAnalysis.

Once you are a bit familiar with the MDAnalysis package, you can look at the sections of the User Guide explaining how to contribute. There are detailed explanations on how to set up a developer environment and how to contribute to the MDAnalysis codebase. This is a great starting point for contributing to MDAnalysis and trying to solve some of the open issues.

Our blog post is the starting point for all GSoC 2023 things, and we have a FAQ. As per the FAQ, candidates must have code merged to be eligible to apply to GSoC with MDAnalysis. On the MDAnalysis repository, you can find several issues tagged with "good first issue", "Difficulty easy", or "GSoC Starter". Please bear in mind, you can only attempt one issue tagged as a GSoC Starter. Note also that we do not assign issues to people; we review the first pull request (PR) that solves an issue. Therefore, you can work on any issue you would like that does not already have an open/active PR against it. We also strongly suggest to work on a single issue/PR at a time. Once you have one PR merged, you are of course very welcome to tackle another issue, and so on. 

Project 2 is an interesting and important project if you work in the MD field. Please have a look at the "Interoperability Roadmap", if you haven't already. Is there a particular package you would like to have interoperability with? You should have some experience with the other libraries that you write the interoperability layer for. For Project 5, it would be a good idea to learn more about writing analysis tools (using AnalysisBase). For example, you may want to give issue 3190 a try. Issue 3946 is also potentially relevant as a more advanced issue, but please note that we are currently making decisions on how to move it forward.

All the best,
Jenna

 | Project, Community and Outreach Manager

                               MDAnalysis * https://www.mdanalysis.org/

                               a fiscally sponsored project of NumFOCUS
                               Twitter: @mdanalysis

                               Github: https://github.com/MDAnalysis

[Padmanabh Parkhi]

Hello, Mentors.

My name is Padmanabh Parkhi, and I am a final-year undergraduate student at the Government College of Engineering Nagpur in Nagpur pursuing my BE degree in Computer Science. I am comfortable programming in Python. 

I have done basic Python projects. I'd like to contribute to the Transport Property Calculations project and learn and contribute to the best of my ability.

Your faithfully

[Manish (B21CS044)]

Dear Role Models,

Hello, my name is Manish and I am a student at the Indian Institute of Technology Jodhpur working towards a Bachelor of Technology degree in Computer Science. Python and C++ have been my primary languages of choice.

I'm hoping to learn a lot from this and would greatly appreciate any advice or suggestions you could give me. I have very little experience with Md simulations but am interested in contributing to the projects by working on the mathematical and physical aspects.

Sincerely, Manish

[Project Manager MDAnalysis]

Hi Padmanabh,

Welcome to MDAnalysis!

MDAnalysis is a Python package for the analysis of computer simulations of systems at the atomic scale. We have a few introductory videos that can give you an idea of what problems MDAnalysis is solving.

The best way to get started is to get your hands dirty following the MDAnalysis User Guide. We suggest starting with installing the MDAnalysis package and going through the Quick Start Guide. Other sections of the User Guide go into many more details about the different capabilities of MDAnalysis.

Once you are a bit familiar with the MDAnalysis package, you can look at the sections of the User Guide explaining how to contribute. There are detailed explanations on how to set up a developer environment and how to contribute to the MDAnalysis codebase. This is a great starting point for contributing to MDAnalysis and trying to solve some of the open issues.

Our blog post is the starting point for all GSoC 2023 things, and we have a FAQ. As per the FAQ, candidates must have code merged to be eligible to apply to GSoC with MDAnalysis. On the MDAnalysis repository, you can find several issues tagged with "good first issue", "Difficulty easy", or "GSoC Starter". Please bear in mind, you can only attempt one issue tagged as a GSoC Starter. Note also that we do not assign issues to people; we review the first pull request (PR) that solves an issue. Therefore, you can work on any issue you would like that does not already have an open/active PR against it. We also strongly suggest to work on a single issue/PR at a time. Once you have one PR merged, you are of course very welcome to tackle another issue, and so on. 

Since you are interested in Project 4, we recommend working with the existing diffusion code and learning about writing analysis tools (using AnalysisBase). We look forward to your contribution.

All the best,
Jenna

Project, Community and Outreach Manager

MDAnalysis * https://www.mdanalysis.org/

a fiscally sponsored project of NumFOCUS

Twitter: @mdanalysis

Github: https://github.com/MDAnalysis

[Project Manager MDAnalysis]

Hi Manish,

Welcome to MDAnalysis!

MDAnalysis is a Python package for the analysis of computer simulations of systems at the atomic scale. We have a few introductory videos that can give you an idea of what problems MDAnalysis is solving.

The best way to get started is to get your hands dirty following the MDAnalysis User Guide. We suggest starting with installing the MDAnalysis package and going through the Quick Start Guide. Other sections of the User Guide go into many more details about the different capabilities of MDAnalysis.

Once you are a bit familiar with the MDAnalysis package, you can look at the sections of the User Guide explaining how to contribute. There are detailed explanations on how to set up a developer environment and how to contribute to the MDAnalysis codebase. This is a great starting point for contributing to MDAnalysis and trying to solve some of the open issues.

Our blog post is the starting point for all GSoC 2023 things, and we have a FAQ. As per the FAQ, candidates must have code merged to be eligible to apply to GSoC with MDAnalysis. On the MDAnalysis repository, you can find several issues tagged with "good first issue", "Difficulty easy", or "GSoC Starter". Please bear in mind, you can only attempt one issue tagged as a GSoC Starter. Note also that we do not assign issues to people; we review the first pull request (PR) that solves an issue. Therefore, you can work on any issue you would like that does not already have an open/active PR against it. We also strongly suggest to work on a single issue/PR at a time. Once you have one PR merged, you are of course very welcome to tackle another issue, and so on. 

Ultimately it is up to you to decide which project would best fit your skills and interests. The skills required for the suggested projects are outlined on the Project Ideas page of the MDAnalysis Wiki. Some require more MD or molecular modeling knowledge than others, so this may be helpful to you in narrowing down your choice. Once you are familiar with the MDAnalysis package, the best way to decide on a project is to explore open issues and start contributing. We look forward to your contribution.

All the best,
Jenna

Project, Community and Outreach Manager

MDAnalysis * https://www.mdanalysis.org/

a fiscally sponsored project of NumFOCUS

Twitter: @mdanalysis

Github: https://github.com/MDAnalysis

[Lokesh Jain]

Hello everyone, 

I am Lokesh Jain(username- HunterZ17) a pre-final(3rd year)  engineering undergraduate currently pursuing Metallurgical and Materials Engineering at the Indian Institute of Technology, Roorkee. 

I have been using python as my main language for a while. And has a keen interest in Machine Learning and Deep learning. I have used ML and DL in materials sciences too and came across Molecular dynamics simulations and have used it also. So, I thought why not contribute toward its development then I came across the GSoC projects of this year. 

I am curious about Project 1(generalize group) and Project 3( Benchmarking) as they seem interesting and will be able to challenge my skills. 

[Project Manager MDAnalysis]

Hi Lokesh,

Welcome to MDAnalysis, and thank you for your introduction! We are happy to see you have contributed to MDAnalysis and submitted your first PR. Since you are interested in Project 1, once your initial PR is merged, you may want to keep an eye on related issues 1861 and 2188. To further explore Project 3, we would suggest you add a new benchmark case to the library to learn how benchmarking works. You could start by having a look at the benchmarks folder in MDAnalysis and the related documentation. 

We look forward to reviewing your proposal.

All the best,
Jenna

Project, Community and Outreach Manager

MDAnalysis * https://www.mdanalysis.org/

a fiscally sponsored project of NumFOCUS

Twitter: @mdanalysis

Github: https://github.com/MDAnalysis

[Vishal Parmar]

Hello everyone,

Vishal Parmar here, Username: v-parmar. I am pursuing my Bachelor of Engineering in Computer Science & Engineering at ITM Universe, Vadodara, Gujarat, under Gujarat Technological University.

I have experience with Python and its various libraries like TensorFlow, Pandas, SKlearn, Numpy, Matplotlib, bs4, etc, and Front-end Web Development. Also, I have intermediate knowledge of Machine Learning technology. I like to solve real-life problems using ML and AI.

This year I am trying for GSOC, which brings me here. So, I am interested in taking part as a contributor to GSOC projects. I already made 1 PR and it is merged.

Thanks & Regards.

[Project Manager MDAnalysis]

Hi Vishal,

Welcome to MDAnalysis, and thank you for your introduction! We are happy to see you have contributed to MDAnalysis and submitted your first PR. If you already feel comfortable working with the MDAnalysis package, the next step would be to draft your proposal. Please have a look at our Project Ideas and let us know if any of these in particular are of interest to you. We always welcome original project ideas as well. 

We look forward to reviewing your proposal.

All the best,
Jenna

Project, Community and Outreach Manager

MDAnalysis * https://www.mdanalysis.org/

a fiscally sponsored project of NumFOCUS

Twitter: @mdanalysis

Github: https://github.com/MDAnalysis

[Abomaye Eniatorudabo]

Hello Mentors,

My name is Abomaye ,  I'm a graduate of mechanical engineering and now a freelance data scientist. I have always desired to contribute to open source and I feel  MDAnalysis is the perfect project for me to begin my open source journey. I am interested in the concept of computer simulations of molecules and I want to contribute with the skills I have as well as learn and grow as a data scientist and a developer. I hope to become a part of this community.

Regards, 

Abomaye.

[Project Manager MDAnalysis]

Hi Abomaye,

Welcome to MDAnalysis!

MDAnalysis is a Python package for the analysis of computer simulations of systems at the atomic scale. We have a few introductory videos that can give you an idea of what problems MDAnalysis is solving.

The best way to get started is to get your hands dirty following the MDAnalysis User Guide. We suggest starting with installing the MDAnalysis package and going through the Quick Start Guide. Other sections of the User Guide go into many more details about the different capabilities of MDAnalysis.

Once you are a bit familiar with the MDAnalysis package, you can look at the sections of the User Guide explaining how to contribute. There are detailed explanations on how to set up a developer environment and how to contribute to the MDAnalysis codebase. This is a great starting point for contributing to MDAnalysis and trying to solve some of the open issues.

Our blog post is the starting point for all GSoC 2023 things, and we have a FAQ. As per the FAQ, candidates must have code merged to be eligible to apply to GSoC with MDAnalysis. On the MDAnalysis repository, you can find several issues tagged with "good first issue", "Difficulty easy", or "GSoC Starter". Please bear in mind, you can only attempt one issue tagged as a GSoC Starter. Note also that we do not assign issues to people; we review the first pull request (PR) that solves an issue. Therefore, you can work on any issue you would like that does not already have an open/active PR against it. We also strongly suggest to work on a single issue/PR at a time. Once you have one PR merged, you are of course very welcome to tackle another issue, and so on. 

Could you please provide your GitHub handle? This is helpful for GSOC mentors to track which applicants are working on which issues. Once you have had your first PR merged, the next step would be to draft your proposal. Please have a look at our Project Ideas and let us know if any of these in particular are of interest to you. We always welcome original project ideas as well.

We look forward to your contribution.

All the best,
Jenna

Project, Community and Outreach Manager

MDAnalysis * https://www.mdanalysis.org/

a fiscally sponsored project of NumFOCUS

Twitter: @mdanalysis

Github: https://github.com/MDAnalysis

[Shaivi Malik]

Hello everyone 

I am computer science and engineering sophomore from India. I was introduced to python in the summer of my junior year of high school through a Microsoft course since then I have consistently worked on enhancing my skills. Last summer, I worked on stochastic processes and simulation models. I have also worked on some Machine Learning projects . This summer I am looking forward to working on Project 4: Transport property calculation. I am also actively working on a performance issue in the MDAnalysis package. 

Regards

Shaivi Malik

[Project Manager MDAnalysis]

Hi Shaivi,

Welcome to MDAnalysis!

We are happy to hear you are already working on contributing to MDAnalysis. Could you please provide your GitHub handle and which issue number you are currently working on? This is helpful for GSOC mentors to track which applicants are working on which issues. Since you are interested in Project 4, we recommend working with the existing diffusion code and learning about writing analysis tools (using AnalysisBase). 

We look forward to your contribution.

All the best,
Jenna

Project, Community and Outreach Manager

MDAnalysis * https://www.mdanalysis.org/

a fiscally sponsored project of NumFOCUS

[Jayanth Ragav]

Hi there !

My name is Jayanth Ragav, a senior undergraduate student from IIT Kanpur, India majoring in materials science and engineering. I have some experience with computational materials science (MD simulations using LAAMPS). I would like to contribute to MDAnalysis Project 4 or Project 2 depending on the availability. I am skilled in general python programming, but I am willing to learn in-depth skills like developing  packages. I want to mainly work in project 4 because I am mainly inclined towards working on scientific data.

I would kindly request you to connect me with a mentor so that I could get an idea of what to present on the proposal and more details about the projects.

Thank you.

(github - jayanthr24)

[Project Manager MDAnalysis]

Hi Jayanth,

Welcome to MDAnalysis!

MDAnalysis is a Python package for the analysis of computer simulations of systems at the atomic scale. We have a few introductory videos that can give you an idea of what problems MDAnalysis is solving.

The best way to get started is to get your hands dirty following the MDAnalysis User Guide. We suggest starting with installing the MDAnalysis package and going through the Quick Start Guide. Other sections of the User Guide go into many more details about the different capabilities of MDAnalysis.

Once you are a bit familiar with the MDAnalysis package, you can look at the sections of the User Guide explaining how to contribute. There are detailed explanations on how to set up a developer environment and how to contribute to the MDAnalysis codebase. This is a great starting point for contributing to MDAnalysis and trying to solve some of the open issues.

Our blog post is the starting point for all GSoC 2023 things, and we have a FAQ. As per the FAQ, candidates must have code merged to be eligible to apply to GSoC with MDAnalysis. On the MDAnalysis repository, you can find several issues tagged with "good first issue", "Difficulty easy", or "GSoC Starter". Please bear in mind, you can only attempt one issue tagged as a GSoC Starter. Note also that we do not assign issues to people; we review the first pull request (PR) that solves an issue. Therefore, you can work on any issue you would like that does not already have an open/active PR against it. We also strongly suggest working on a single issue/PR at a time. Once you have one PR merged, you are of course welcome to tackle another issue, and so on. 

Once you have had your first PR merged, the next step would be to draft your proposal. To seek feedback from the MDAnalysis mentors, register as a contributor and submit your project idea. Please upload a PDF (`DRAFT` in the file name) of your proposal - which includes a Google doc link to your proposal - to the GSoC program website. Mentors can then provide feedback directly on your Google doc. When you are ready to submit the final version of your proposal, you can export your Google doc proposal as a PDF and re-upload to the GSoC site.

Keep in mind that you are allowed to submit up to three (3) project proposals. If you would like, you are welcome to submit proposals for Projects 2 and/or 4 as you indicated in your interests. Project 2 is an interesting and important project if you work in the MD field. Please have a look at the "Interoperability Roadmap", if you haven't already. Is there a particular package you would like to have interoperability with? You should have some experience with the other libraries that you write the interoperability layer for. For Project 4, we recommend working with the existing diffusion code and learning about writing analysis tools (using AnalysisBase).

We look forward to your contribution.

All the best,
Jenna

--
GSoC 2023: Read https://www.mdanalysis.org/2023/02/22/gsoc2023/
Our FAQ: https://github.com/MDAnalysis/mdanalysis/wiki/GSoC-FAQ
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