5 papers
Symbolic Predicate-Guided Language Agents for Inverse Design of Perovskite Oxides
Dong Hyeon Mok, Seoin Back, Victor Fung +1
Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desire…
Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Dong Hyeon Mok, Seoin Back, Victor Fung +1
Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally…
RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models
Shikun Liu, Deyu Zou, Nima Shoghi +3
In the era of foundation models, fine-tuning pre-trained models for specific downstream tasks has become crucial. This drives the need for robust fine-tuning methods to address cha…
MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
Lingyu Kong, Nima Shoghi, Guoxiang Hu +2
Geometric machine learning models such as graph neural networks have achieved remarkable success in recent years in chemical and materials science research for applications such as…
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
Deyu Zou, Shikun Liu, Siqi Miao +3
Geometric deep learning (GDL) has gained significant attention in scientific fields, for its proficiency in modeling data with intricate geometric structures. However, very few wor…