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Nicholas Gao

11 accepted papers

2026

Derivative Informed Learning of Exchange-Correlation Functionals

Eike S. Eberhard, Luca Anthony Thiede, Abdulrahman Aldossary, Andreas Burger, Nicholas Gao, Vignesh Bhethanabotla +2
ICML 2026poster

Machine-learned (ML) XC-functionals promise improved accuracy, but overfit to training energies and basis sets without proper regularization. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that regularizes ML-XC training by supervising energy gradients on the Grassmannian of density matr…

Cited by 0Source ↗Scholar ↗
2026

Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State

Nicholas Gao, Till Grutschus, Frank Noe, Stephan Günnemann
ICML 2026spotlight

Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient numerical accuracy of state overlaps requires growing the number of Monte Carlo samples, and consequently computational …

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2025

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space

Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
ICLR 2025poster

We introduce a new framework for 2D molecular graph generation using 3D molecule generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps 2D molecular graphs to 3D Euclidean point clouds via synthetic coordinates and learns the inverse map using an E($n$)-Equivariant Graph Neural…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

Ewald-based Long-Range Message Passing for Molecular Graphs

Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann
ICML 2023poster

Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years. A key driver of this success is the Message Passing Neural Network (MPNN) paradigm. Its favorable scaling with system size partly relies upon a spatial distance limit on mes…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2023

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

Nicholas Gao, Stephan Günnemann
ICLR 2023poster

Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integr…

Cited by 20Source ↗PDF ↗code ↗Scholar ↗
2023

Uncertainty Estimation for Molecules: Desiderata and Methods

Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
ICML 2023poster

Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) trajectories. Thanks to their fast inference times they promise to accelerate computational chemistry applications. Unfortuna…

Cited by 13Source ↗PDF ↗Scholar ↗
2022

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

Nicholas Gao, Stephan Günnemann
ICLR 2022spotlight

Solving the Schrödinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural networks succeeded at modelling wave functions of many-electron systems. Together with the variational Monte-Carlo (VMC) fram…

Cited by 46Source ↗PDF ↗code ↗Scholar ↗
2020

Fast and Flexible Temporal Point Processes with Triangular Maps

Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann
NeurIPS 2020oral

Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the rec…

Cited by 42Source ↗PDF ↗code ↗Scholar ↗