Hello there!
I'm Abhi
& formally, Abhijith Gopakumar (he/him/his)
Last main updates to this page was made in 2022. More recent details are in LinkedIn and in my resume.
I'm a softweare engineer specializing in backend API, database management, cloud infrastructure, network management and workflow pipelines.
Since 2022, I have been working at Questek Innovations in the role of backend engineer. Prior to that, I was the main developer at OQMD.org - one of the largest prduction-level material databases in the world.
Many of the details in this website are from my works at or before graduate school since most recent updates are made in my LinkedIn profile page: Link
More on how the Materials Design involves ML, DBMS, & DevOps
The goal of computational Materials Design is to quickly discover novel materials with desired properties.
Materials discovery workflows usually have three main components - Data, Statistical Modeling, and Validation.
The first step, Data, involves/depends upon high-throughput data generation, data processing, storage, database server deployment, etc.
The second step involves Statistical analysis including Machine Learning, single/multi-objective optimization, optional uncertainty analysis, etc. This is where a few materials are selected from a large candidate set of materials.
Once the new material(s) is selected from statistical models, it is characterized in the final phase of Validation using appropriate relatively expensive, material simulation methods.
Hence, the field of material design involves several different computational subfields such as ML/Statistics, and also depends upon seemingly-uncorrelated domains like DBMS, DevOps, and Data pipelines.
More on Materials Design: Article by NIST