Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks

no code implementations • 8 Dec 2023 • Haoyu Li, Shichang Zhang, Longwen Tang, Mathieu Bauchy, Yizhou Sun

We demonstrate in our experiments that SymGNN can significantly improve the energy barrier prediction over other GNNs and non-graph machine learning models.

Representation Learning

Learning from Sparse Datasets: Predicting Concrete's Strength by Machine Learning

no code implementations • 29 Apr 2020 • Boya Ouyang, Yuhai Li, Yu Song, Feishu Wu, Huizi Yu, Yongzhe Wang, Mathieu Bauchy, Gaurav Sant

Here, based on the analysis of a large dataset (>10, 000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to "learn" how to reliably predict concrete strength as a function of the size of the dataset.

BIG-bench Machine Learning