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Found 11 papers, 7 papers with code
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
We introduce a new framework for molecular graph generation with 3D molecular generative models.
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
Neural wave functions accomplished unprecedented accuracies in approximating the ground state of many-electron systems, though at a high computational cost.
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
Recent neural networks demonstrated impressively accurate approximations of electronic ground-state wave functions.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Uncertainty Estimation for Molecules: Desiderata and Methods
Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) trajectories.
Ewald-based Long-Range Message Passing for Molecular Graphs
Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years.
Generalizing Neural Wave Functions
To overcome this limitation, we present Graph-learned orbital embeddings (Globe), a neural network-based reparametrization method that can adapt neural wave functions to different molecules.
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function.
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Solving the Schr\"odinger equation is key to many quantum mechanical properties.
Fast and Flexible Temporal Point Processes with Triangular Maps
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data.
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes.
