1 citations · 1 across the 2 of their papers we have counts for
5 papers
Graph Neural Network for Predicting the Effective Properties of Polycrystalline Materials: A Comprehensive Analysis
Minyi Dai, Mehmet F. Demirel, Xuanhan Liu +2
We develop a polycrystal graph neural network (PGNN) model for predicting the effective properties of polycrystalline materials, using the Li7La3Zr2O12 ceramic as an example. A lar…
Analysis of Sparse Subspace Clustering: Experiments and Random Projection
Mehmet F. Demirel, Enrico Au-Yeung
Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many…
Attentive Walk-Aggregating Graph Neural Networks
Mehmet F. Demirel, Shengchao Liu, Siddhant Garg +2
Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecu…
Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials
Minyi Dai, Mehmet F. Demirel, Yingyu Liang +1
Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighborin…
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the proper…