Publications de Sanggyu Chong | EPFL Graph Search

Adaptive energy reference for machine-learning models of the electronic density of states

Michele Ceriotti, Sanggyu Chong, Kevin Kazuki Huguenin-Dumittan, Federico Grasselli

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

2025

Prediction rigidities for data-driven chemistry

Michele Ceriotti, Philip Robin Loche, Sanggyu Chong, Filippo Bigi, Federico Grasselli

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

2024

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Michele Ceriotti, Sanggyu Chong, Filippo Bigi, Federico Grasselli

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

2024

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Barely porous organic cages for hydrogen isotope separation

Michele Ceriotti, Venkat Kapil, Sanggyu Chong, Ming Liu

The separation of hydrogen isotopes for applications such as nuclear fusion is a major challenge. Current technologies are energy intensive and inefficient. Nanoporous materials have the potential to separate hydrogen isotopes by kinetic quantum sieving, b ...

American Association for the Advancement of Science2019