55 citations · 129 across the 18 of their papers we have counts for
12 papers · 1 filter
Derivative Informed Learning of Exchange-Correlation Functionals
Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary +5
Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still…
Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
Nicholas Gao, Till Grutschus, Frank Noé +1
Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient…
Learning Equivariant Non-Local Electron Density Functionals
Nicholas Gao, Eike Eberhard, Stephan Günnemann
The accuracy of density functional theory hinges on the approximation of non-local contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-des…
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer +2
We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclid…
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
Nicholas Gao, Stephan Günnemann
Neural wave functions accomplished unprecedented accuracies in approximating the ground state of many-electron systems, though at a high computational cost. Recent works proposed a…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
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…
Ewald-based Long-Range Message Passing for Molecular Graphs
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao +1
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 N…
Generalizing Neural Wave Functions
Nicholas Gao, Stephan Günnemann
Recent neural network-based wave functions have achieved state-of-the-art accuracies in modeling ab-initio ground-state potential energy surface. However, these networks can only s…
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
Nicholas Gao, Stephan Günnemann
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 geometri…
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Nicholas Gao, Stephan Günnemann
Solving the Schrödinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural netwo…
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Nicholas Gao, Max Wilson, Thomas Vandal +3
Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains a…
Fast and Flexible Temporal Point Processes with Triangular Maps
Oleksandr Shchur, Nicholas Gao, Marin Biloš +1
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, th…