Author

Eric Qu

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#machine learning Preprint Sep 2026

A strategic roadmap for an atomistic machine-learning ecosystem

Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...

Jörg Behler, Michele Ceriotti, Cecilia Clementi et al. · 0 citations

#machine learning Preprint Oct 2025

Transformers Discover Molecular Structure Without Graph Priors

This work develops a systematic understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions, and finds that the model autonomously recovers key physical structure.

T. Kreiman, Yu-Tong Bai, Fadi Atieh et al. · 10 citations