Search Results for author: Steph-Yves Louis

Found 9 papers, 4 papers with code

Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks

no code implementations10 Nov 2021 Nghia Nguyen, Steph-Yves Louis, Lai Wei, Kamal Choudhary, Ming Hu, Jianjun Hu

Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant.

Materials Screening

Scalable deeper graph neural networks for high-performance materials property prediction

1 code implementation25 Sep 2021 Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu

Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.

Band Gap Graph Attention +1

MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art

no code implementations9 Sep 2021 Jianjun Hu, Stanislav Stefanov, Yuqi Song, Sadman Sadeed Omee, Steph-Yves Louis, Edirisuriya M. D. Siriwardane, Yong Zhao

The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials.

Band Gap Materials Screening

Active learning based generative design for the discovery of wide bandgap materials

2 code implementations28 Feb 2021 Rui Xin, Edirisuriya M. D. Siriwardane, Yuqi Song, Yong Zhao, Steph-Yves Louis, Alireza Nasiri, Jianjun Hu

Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model.

Active Learning Band Gap

NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique

1 code implementation17 Feb 2021 Steph-Yves Louis, Alireza Nasiri, Fatima J. Rolland, Cameron Mitro, Jianjun Hu

While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure.

Node Classification

NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME

no code implementations1 Jan 2021 Steph-Yves Louis, Alireza Nasiri, Fatima Christina Rolland, Cameron Mitro, Jianjun Hu

In this paper we propose the NODE-SELECT graph neural network (NSGNN): a novel and flexible graph neural network that uses subsetting filters to learn the contribution from the nodes selected to share their information.

Node Classification

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

no code implementations17 Mar 2020 Yong Zhao, Kunpeng Yuan, Yinqiao Liu, Steph-Yves Louis, Ming Hu, Jianjun Hu

Extensive benchmark experiments over 2, 170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction.

Machine Learning based prediction of noncentrosymmetric crystal materials

no code implementations26 Feb 2020 Yuqi Song, Joseph Lindsay, Yong Zhao, Alireza Nasiri, Steph-Yves Louis, Jie Ling, Ming Hu, Jianjun Hu

Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems, quantum computing, cybersecurity, and etc.

BIG-bench Machine Learning

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