CITATION.cff

cff-version: 1.2.0
title: >-
  SynCoTrain: A Dual Classifier PU-learning Framework for
  Synthesizability Prediction
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Sasan
    family-names: Amariamir
    affiliation: >-
      Federal Institute of Materials Research and Testing,
      Unter den Eichen 87, 12205 Berlin, Germany
  - given-names: Janine
    family-names: George
    affiliation: >-
      Federal Institute of Materials Research and Testing,
      Unter den Eichen 87, 12205 Berlin, Germany
      Friedrich Schiller University Jena,
      Institute of Condensed Matter Theory and Solid-State Optics,
      Max-Wien-Platz 1, 07743 Jena (Germany)
    email: janine.george@bam.de
    orcid: 'https://orcid.org/0000-0001-8907-0336'
  - given-names: Philipp
    family-names: Benner
    email: philipp.benner@bam.de
    affiliation: >-
      Federal Institute of Materials Research and Testing,
      Unter den Eichen 87, 12205 Berlin, Germany
    orcid: 'https://orcid.org/0000-0002-0912-8137'
identifiers:
  - type: other
    value: 'arXiv:2411.12011'
    description: The ArXiv preprint of the paper
repository-code: 'https://github.com/BAMeScience/SynCoTrainMP'
abstract: >+
  Material discovery is a cornerstone of modern science,
  driving advancements in diverse disciplines from
  biomedical technology to climate solutions. Predicting
  synthesizability, a critical factor in realizing novel
  materials, remains a complex challenge due to the
  limitations of traditional heuristics and thermodynamic
  proxies. While stability metrics such as formation energy
  offer partial insights, they fail to account for kinetic
  factors and technological constraints that influence
  synthesis outcomes. These challenges are further
  compounded by the scarcity of negative data, as failed
  synthesis attempts are often unpublished or
  context-specific. We present SynCoTrain, a semi-supervised
  machine learning model designed to predict the
  synthesizability of materials. SynCoTrain employs a
  co-training framework leveraging two complementary graph
  convolutional neural networks: SchNet and ALIGNN. By
  iteratively exchanging predictions between classifiers,
  SynCoTrain mitigates model bias and enhances
  generalizability. Our approach uses Positive and Unlabeled
  (PU) Learning to address the absence of explicit negative
  data, iteratively refining predictions through
  collaborative learning. The model demonstrates robust
  performance, achieving high recall on internal and
  leave-out test sets. By focusing on oxide crystals, a
  well-characterized material family with extensive
  experimental data, we establish SynCoTrain as a reliable
  tool for predicting synthesizability while balancing
  dataset variability and computational efficiency. This
  work highlights the potential of co-training to advance
  high-throughput materials discovery and generative
  research, offering a scalable solution to the challenge of
  synthesizability prediction.

keywords:
  - Machine Learning
  - Materials Science
license: MIT
version: 0.0.2
date-released: '2024-12-12'