Work

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation

Year: 2023

Type: article

Abstract: exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions.

Source: Chemical Science

DOI: 10.1039/d3sc03538g

Authors: Seongmin Kim, Juhwan Noh, Geun Ho Gu, Shuan Chen, Yousung Jung

Institutions: Korea Advanced Institute of Science and Technology, Seoul National University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea, Korea Advanced Institute of Science and Technology, Institute for Information and Communications Technology Promotion