Publications¶

Conference Proceedings¶

2025¶

Learning Equivariant Non-Local Electron Density Functionals
N. Gao*, E. Eberhard*, S. Günnemann.
Spotlight @ ICLR — top 5.1%

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
N. Gao*, A. Ketata*, J. Sommer*, T. Wollschläger, S. Günnemann.
ICLR 2025

2024¶

Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
N. Gao, S. Günnemann.
Oral @ NeurIPS — top 0.4%

2023¶

Generalizing Neural Wave Functions
N. Gao, S. Günnemann.
ICML 2023

Uncertainty Estimation for Molecules: Desiderata and Methods
T. Wollschläger, N. Gao, B. Charpentier, A. Ketata, S. Günnemann.
ICML 2023

Ewald-based Long-Range Message Passing for Molecular Graphs
A. Kosmala, J. Gasteiger, N. Gao, S. Günnemann.
ICML 2023

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
N. Gao, S. Günnemann.
ICLR 2023

2022¶

A Hybrid Quantum-Classical Neural Network Certification Algorithm
N. Franco, T. Wollschläger, N. Gao, J.M. Lorenz, S. Günnemann.
IEEE QCE 2022

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
N. Gao, S. Günnemann.
Spotlight @ ICLR — top 7%

2020¶

Fast and Flexible Temporal Point Processes using Triangular Maps
O. Shchur, N. Gao, M. Biloš, S. Günnemann.
Oral @ NeurIPS — top 1%

High-Dimensional Similarity Search with Quantum-assisted VAE
N. Gao, M. Wilson, T. Vandal, W. Vinci, R. Nemani, E. Rieffel.
Oral @ SIGKDD Research Track

Journal Articles¶

2025¶

Artificial intelligence for science in quantum, atomistic, and continuum systems
X. Zhang, L. Wang, J. Helwig, Y. Luo, C. Fu, Y. Xie, M. Liu, Y. Lin, Z. Xu, K. Yan, K. Adams, M. Weiler, X. Li, T. Fu, Y. Wang, A. Strasser, H. Yu, Y. Xie, X. Fu, S. Xu, Y. Liu, Y. Du, A. Saxton, H. Ling, H. Lawrence, H. Stärk, S. Gui, C. Edwards, N. Gao, A. Ladera, T. Wu, E. F. Hofgard, A. Mansouri Tehrani, R. Wang, A. Daigavane, M. Bohde, J. Kurtin, Q. Huang, T. Phung, M. Xu, C. K. Joshi, S. V. Mathis, K. Azizzadenesheli, A. Fang, A. Aspuru-Guzik, E. Bekkers, M. Bronstein, M. Zitnik, A. Anandkumar, S. Ermon, P. Liò, R. Yu, S. Günnemann, J. Leskovec, H. Ji, J. Sun, R. Barzilay, T. Jaakkola, C. W. Coley, X. Qian, X. Qian, T. Smidt, S. Ji.
Foundations and Trends in Machine Learning

2023¶

Neural Network Ansatz for Periodic Wave Functions and the Homogeneous Electron Gas
N. Wilson, S. Moroni, M. Holzmann, N. Gao, P. Wudarski, T. Vegge, A. Bhowmik.
Physical Review B

Preprint¶

2025¶

Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
N. Gao*, M. Scherbela*, P. Grohs, S. Günnemann.

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
A. Foster, Z. Schätzle, P. B. Szabó, L. Cheng, J. Köhler, G. Cassella, N. Gao, F. Noé.

2021¶

Simulations of SOTA fermionic neural network wave functions with diffusion Monte Carlo
M. Wilson, N. Gao, F. Wudarski, E. Rieffel, N. M. Tubman.

Workshop and Other Publications¶

2025¶

On Learning Quasi-Lagrangian Turbulence
A. P. Toshev, T. Kalinov, N. Gao, S. Günnemann, N. A. Adams.
Spotlight @ ICLR Workshop on Machine Learning Multiscale Processes

2024¶

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
N. Gao*, A. Ketata*, J. Sommer*, T. Wollschläger, S. Günnemann.
Best Paper Runner-up @ GRaM ICML

On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
N. Gao, S. Günnemann.
AI4DifferentialEquations in Science @ ICLR