Sudhakar Pamidighantam created AIRAVATA-3974:
------------------------------------------------
Summary: [GSoC] Automation of integrated computational services to
generate data and training a prediction model for bright fluorescent materials
Key: AIRAVATA-3974
URL: https://issues.apache.org/jira/browse/AIRAVATA-3974
Project: Airavata
Issue Type: New Feature
Reporter: Sudhakar Pamidighantam
Attachments: GSOC2026-SMILES.pptx
Background: The small molecule ionic isolation lattice platform provides a way
to generate materials with biright fluorescence in solid state by combining a
dye with a macrocyclic system. The brightness depends on some design rules
based on the charge, size and redox properties of the two systems. This
projects aims to compute or collect basic properties and evaluate the design
rules and provide a filter to predict the dye -macrocyclic combination for the
desired material function. Many application and workflows are integrated into a
community framework, the smiles gateway. The data generated by the workflows
need to be ingested into corresponding data products. As the data is collected
a new workflow to prepare the data for training and train a network needs to be
enabled and eventually converted to a continuous training model.
Tasks:
# Coupled Literature Scraping and Data Extraction workflows
# Automate ingestion of Literature Data into the literature data product
# Trigger notifications for gaps and incomplete data/ failed workflows
# Adding new applications for molecular graph generation for training a graph
CN network
# Training a network for the materials design and running inference for large
dye set
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
