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followers: 31.0 following: 70.0 repos: 19.0 gists: 0.0

Name: Aakash Ashok Naik

Type: User

Company: Bundesanstalt für Materialforschung und -prüfung

Bio: Doctoral candidate at Bundesanstalt für Materialforschung und -prüfung and at University of Jena, Germany.

Twitter: NaikAak

Location: Berlin

Blog: https://www.aakashnaik23.de/

Hi there 👋

🧑‍🎓 I did complete my Master in Materials Science with focus Computer aided materials simulations from Paderborn University
🧑‍🔬 At present I am pursuing my Doctoral studies at the Federal Institute for Materials Research and Testing (Bundesanstalt für Materialforschung und -prüfung) in Berlin, Germany under supervision of Prof. Dr.Janine George
🔭 I currently work on high-throughput simulations focused on bonding analysis to identify new descriptors for materials properties based on bonding indicators by employing machine learning
👯 I’m aspire to gain expertise in material simulations at all scales from atomistic to FEM and in applied machine learning to solve material science problems
📫 You can to reach me on Twitter or LinkedIn
💻 Here is the link to my Personal website.

📊 My Stats:

Aakash Ashok Naik's Projects

atomate

atomate is a powerful software for computational materials science and contains pre-built workflows.

atomate2

atomate2 is a library of computational materials science workflows

custodian

A simple, robust and flexible just-in-time job management framework in Python.

dataset-summary

This repo contains summary and example scripts to access and visualize JSONS from our preprint : https://doi.org/10.48550/arXiv.2304.02726

emmet

Be a master builder of databases of material properties. Avoid the Kragle.

lobster-database-paper-analysis-scripts

Repository Consisting of analysis scripts

lobster-database-paper-interactive-plots

Repository consisting of interactive analysis plots

lobsterautomation

This is a package to reproduce the publication on Lobster automation

lobsterpy

Package to automatically analyze Lobster runs

matgl

Graph deep learning library for materials

modnet

MODNet: a framework for machine learning materials properties

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.

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