exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design

TL;DR

exa-AMD is a Python-based scalable workflow that integrates AI/ML, materials databases, and quantum calculations to accelerate functional materials discovery across diverse computing resources.

Contribution

It introduces a flexible, scalable workflow framework using Parsl, enabling researchers to efficiently execute materials discovery tasks on various computing platforms.

Findings

Successfully integrates AI/ML and quantum calculations in a scalable workflow.

Decouples workflow logic from execution environment for flexibility.

Supports execution on laptops to supercomputers.

Abstract

exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from execution configuration, thereby empowering researchers to scale their workflows without having to reimplement them for each system.