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Seunghoon Yi

2 accepted papers

2026

Sparsity-promoting Fine-tuning for Equivariant Materials Foundation Model

ICLR 2026poster

Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces. However, they often require domain-specific calibration due to physicochemical diversity and mismatches between practi…

Cited by 0SourceScholar
2023

Towards Physically Reliable Molecular Representation Learning

UAI 2023poster

Estimating the energetic properties of molecular systems is a critical task in material design. Machine learning has shown remarkable promise on this task over classical force fields, but a fully data-driven approach suffers from limited labeled data; not just the amount of available data lacks, but…

Cited by 2SourcePDFScholar