Nicholas Gao

According to our database, Nicholas Gao authored at least 13 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Learning Equivariant Non-Local Electron Density Functionals.

Nicholas Gao, Eike Eberhard, Stephan Günnemann

CoRR, 2024

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space.

Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann

CoRR, 2024

Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations.

Nicholas Gao, Stephan Günnemann

CoRR, 2024

On Representing Electronic Wave Functions with Sign Equivariant Neural Networks.

Nicholas Gao, Stephan Günnemann

CoRR, 2024

2023

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

CoRR, 2023

Uncertainty Estimation for Molecules: Desiderata and Methods.

Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

Ewald-based Long-Range Message Passing for Molecular Graphs.

Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

Generalizing Neural Wave Functions.

Nicholas Gao, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks.

Nicholas Gao, Stephan Günnemann

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm.

Nicola Franco, Tom Wollschläger, Nicholas Gao, Jeanette Miriam Lorenz, Stephan Günnemann

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2022

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions.

Nicholas Gao, Stephan Günnemann

Proceedings of the Tenth International Conference on Learning Representations, 2022

2020

Fast and Flexible Temporal Point Processes with Triangular Maps.

Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder.

Nicholas Gao, Max Wilson, Thomas Vandal, Walter Vinci, Ramakrishna R. Nemani, Eleanor Gilbert Rieffel

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020