AFL-agent#
AFL-agent is a Python library that allows users to implement active learning agents for material science applications. [1] Rather than providing canned algorithms, the library provides a framework that allows users to build their own. This is achieved through a modular, extensible API that allows users to compose multiple machine learning operations into executable pipelines.
If you use AFL-agent in your research, please cite the manuscript:
[1] “Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization” (under review)
Key Features#
Library of machine learning operations that can be composed into executable pipelines
Pipelines are modular, visualizable, serializable, and self-documenting
All intermediate pipeline operations are stored in a xarray-based data model
Rich visualization tools for analyzing calculations
Trivial support for multimodal data processing
Support for phase boundary mapping and material property optimization
Autonomous Formulation Lab#
The Autonomous Formulation Lab (AFL) is a National Institute of Standards and Technology (NIST) program that seeks to accelerate the discovery and optimization of soft materials through the development and application of autonomous techniques to high-value measurements. Specifically, we design robotic platforms that autonomously mix, synthesize and evaluate soft materials and we study them using small-angle scattering (SANS) and small-angle X-ray scattering (SAXS) and other techniques.
Installation#
You can install AFL-agent using pip:
pip install git+https://github.com/usnistgov/afl-agent
Please see the Setup page for more details.
Documentation Structure#
This documentation is organized according to the philosphy described by Daniele Procida at diataxis.fr. It is organized into four sections:
Tutorials: Step-by-step guides for beginners
How-to: Guides for specific tasks
Explanations: Discussions of underlying principles and concepts
Reference: Detailed technical reference