Search Results for author: Youzhi Luo

Found 15 papers, 10 papers with code

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

1 code implementation NeurIPS 2023 Haiyang Yu, Meng Liu, Youzhi Luo, Alex Strasser, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).

Atomic Forces

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization

no code implementations13 Jun 2023 Xiner Li, Shurui Gui, Youzhi Luo, Shuiwang Ji

Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution.

Data Augmentation Out-of-Distribution Generalization

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

1 code implementation12 Jun 2023 Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, Shuiwang Ji

This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.

Band Gap Formation Energy +2

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

2 code implementations NeurIPS 2023 Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji

In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs.

Out-of-Distribution Generalization

Generating 3D Molecules for Target Protein Binding

1 code implementation19 Apr 2022 Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji

Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system.

Drug Discovery

Automated Data Augmentations for Graph Classification

no code implementations26 Feb 2022 Youzhi Luo, Michael McThrow, Wing Yee Au, Tao Komikado, Kanji Uchino, Koji Maruhashi, Shuiwang Ji

In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification.

Data Augmentation Graph Classification

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

1 code implementation23 Mar 2021 Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.

Benchmarking Graph Generation +1

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

1 code implementation2 Dec 2020 Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji

Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.

BIG-bench Machine Learning Drug Discovery +2

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