55 citations · 129 across the 18 of their papers we have counts for
19 papers
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…
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Adam Foster, Zeno Schätzle, P. Bernát Szabó +7
Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireference character of the electronic structure in dissociating species. Multi…
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
Michael Scherbela, Nicholas Gao, Philipp Grohs +1
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-networ…
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…
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
Nicholas Gao, Stephan Günnemann
Recent neural networks demonstrated impressively accurate approximations of electronic ground-state wave functions. Such neural networks typically consist of a permutation-equivari…
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…
Uncertainty Estimation for Molecules: Desiderata and Methods
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier +2
Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) traje…
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…
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm
Nicola Franco, Tom Wollschlaeger, Nicholas Gao +2
In recent years, quantum computers and algorithms have made significant progress indicating the prospective importance of quantum computing (QC). Especially combinatorial optimizat…
Wave function Ansatz (but Periodic) Networks and the Homogeneous Electron Gas
Max Wilson, Saverio Moroni, Markus Holzmann +4
We design a neural network Ansatz for variationally finding the ground-state wave function of the Homogeneous Electron Gas, a fundamental model in the physics of extended systems o…
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…
Simulations of state-of-the-art fermionic neural network wave functions with diffusion Monte Carlo
Max Wilson, Nicholas Gao, Filip Wudarski +2
Recently developed neural network-based \emph{ab-initio} solutions (Pfau et. al arxiv:1909.02487v2) for finding ground states of fermionic systems can generate state-of-the-art res…
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…