The aim of this project is to design and discover materials with high refractive indices by exploiting new and existing databases, machine learning predictions (via MODNet) and high-throughtput DFT calculations (via atomate2 and jobflow-remote, all within a dynamic active learning framework.
This repository contains a Python package, re2fractive that implements some of
this functionality, with the aim to grow it to a generic package for other
properties.
This repository accompanies the preprint:
V. Trinquet, M. L. Evans, C. Hargreaves, P-P. De Breuck, G-M. Rignanese, "Optical materials discovery and design with federated databases and machine learning" (2024) DOI: 10.48550/arXiv.2405.11393.
The active learning campaign described there can be repeated with:
from re2fractive.campaign import Campaign, LearningStrategy
from re2fractive.datasets import NaccaratoDataset, MP2023Dataset, Alexandria2024Dataset
learning_strategy = LearningStrategy(
max_n_features=100,
feature_select_strategy="always",
hyperopt_strategy="always",
)
campaign = Campaign.new_campaign_from_dataset(
NaccaratoDataset,
datasets=[MP2023Dataset, Alexandria2024Dataset],
learning_strategy=learning_strategy
)
campaign.run(epochs=8)Some functionality is still missing from the first public release:
- Direct integration with atomate2/jobflow-remote workflows for automatic job submission after candidate selection.
- Automatic selection according to custom acquisition functions.
