Work

PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction

Year: 2023

Type: article

Abstract: Accurate vapor pressure prediction is crucial for various applications, but obtaining precise measurements for certain compounds is resource- and labor-intensive. This challenge is amplified when a temperature-dependent relationship is required. To address this, we introduce PUFFIN (Path-Unifying Feed-Forward Interfaced Network), a machine learning approach that combines transfer learning with a specialized inductive bias node (inspired by the Antoine equation) to enhance vapor pressure prediction. PUFFIN outperforms alternative strategies that lack inductive bias or use generic compound descriptors by leveraging inductive bias and transfer learning using graph embeddings. The framework's incorporation of domain-specific knowledge overcomes data limitations and shows promise for broader chemical compound analysis applications, including the prediction of other physicochemical properties. An important aspect of our proposed approach is its partial interpretability, as the inductive Antoine node yields network-derived Antoine equation coefficients.

Source: Chemical Engineering Science

DOI: 10.1016/j.ces.2023.119623

Authors: Vinicius Viena Santana, Carine Menezes Rebello, Luana P. Queiroz, Ana Mafalda Ribeiro, Nadia Shardt, Idelfonso B.R. Nogueira

Institutions: Universidade do Porto, Norwegian University of Science and Technology

Language: English

Topic: Chemical Thermodynamics and Molecular Structure

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,140

Funders: Massachusetts Institute of Technology, Ministério da Ciência, Tecnologia e Ensino Superior, Fundação para a Ciência e a Tecnologia