Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Luo, Youzhi"'
Autor:
Li, Xiner, Wang, Limei, Luo, Youzhi, Edwards, Carl, Gui, Shurui, Lin, Yuchao, Ji, Heng, Ji, Shuiwang
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs exists, that for 3D geometries is largely unexplored. Here, we attempt
Externí odkaz:
http://arxiv.org/abs/2408.10120
Autor:
Zhang, Xuan, Wang, Limei, Helwig, Jacob, Luo, Youzhi, Fu, Cong, Xie, Yaochen, Liu, Meng, Lin, Yuchao, Xu, Zhao, Yan, Keqiang, Adams, Keir, Weiler, Maurice, Li, Xiner, Fu, Tianfan, Wang, Yucheng, Yu, Haiyang, Xie, YuQing, Fu, Xiang, Strasser, Alex, Xu, Shenglong, Liu, Yi, Du, Yuanqi, Saxton, Alexandra, Ling, Hongyi, Lawrence, Hannah, Stärk, Hannes, Gui, Shurui, Edwards, Carl, Gao, Nicholas, Ladera, Adriana, Wu, Tailin, Hofgard, Elyssa F., Tehrani, Aria Mansouri, Wang, Rui, Daigavane, Ameya, Bohde, Montgomery, Kurtin, Jerry, Huang, Qian, Phung, Tuong, Xu, Minkai, Joshi, Chaitanya K., Mathis, Simon V., Azizzadenesheli, Kamyar, Fang, Ada, Aspuru-Guzik, Alán, Bekkers, Erik, Bronstein, Michael, Zitnik, Marinka, Anandkumar, Anima, Ermon, Stefano, Liò, Pietro, Yu, Rose, Günnemann, Stephan, Leskovec, Jure, Ji, Heng, Sun, Jimeng, Barzilay, Regina, Jaakkola, Tommi, Coley, Connor W., Qian, Xiaoning, Qian, Xiaofeng, Smidt, Tess, Ji, Shuiwang
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range
Externí odkaz:
http://arxiv.org/abs/2307.08423
We consider the problem of generating periodic materials with deep models. While symmetry-aware molecule generation has been studied extensively, periodic materials possess different symmetries, which have not been completely captured by existing met
Externí odkaz:
http://arxiv.org/abs/2307.02707
Autor:
Yu, Haiyang, Liu, Meng, Luo, Youzhi, Strasser, Alex, Qian, Xiaofeng, Qian, Xiaoning, Ji, Shuiwang
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). While numerous quantum chemistry datas
Externí odkaz:
http://arxiv.org/abs/2306.09549
Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution. With rising application demands and inherent complexity, graph OOD problems call for specialized solutions.
Externí odkaz:
http://arxiv.org/abs/2306.08076
We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Cur
Externí odkaz:
http://arxiv.org/abs/2306.10045
We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incor
Externí odkaz:
http://arxiv.org/abs/2306.01103
Autor:
Xu, Zhao, Xie, Yaochen, Luo, Youzhi, Zhang, Xuan, Xu, Xinyi, Liu, Meng, Dickerson, Kaleb, Deng, Cheng, Nakata, Maho, Ji, Shuiwang
Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. Currently, an efficient alternative to computing ground
Externí odkaz:
http://arxiv.org/abs/2305.13315
A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bin
Externí odkaz:
http://arxiv.org/abs/2204.09410
Autor:
Luo, Youzhi, McThrow, Michael, Au, Wing Yee, Komikado, Tao, Uchino, Kanji, Maruhashi, Koji, Ji, Shuiwang
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but
Externí odkaz:
http://arxiv.org/abs/2202.13248