ABSTRACT A reliable uncertainty estimator is a key ingredient in the successful use of
machine-learning force fields for predictive calculations. Important considerations are …

Computational workflows are a common class of application on supercomputers, yet the
loosely coupled and heterogeneous nature of workflows often fails to take full advantage of …

Predictive simulations are essential for applications ranging from weather forecasting to
material design. The veracity of these simulations hinges on their capacity to capture the …

Uncertainty quantification and robustness to distribution shifts are important goals in
machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) …

As machine learning (ML) methods continue to be applied to a broad scope of problems in
the physical sciences, uncertainty quantification is becoming correspondingly more …

This paper focusses on the optimal implementation of a Mean Variance Estimation network
(MVE network)(Nix and Weigend, 1994). This type of network is often used as a building …

In this paper, three state-of-the-art deep learning uncertainty quantification (UQ) methods–
Flipout probabilistic convolutional neural network (CNN), deep ensemble probabilistic CNN …

Background: As we navigate towards integrating deep learning methods in the real clinic, a
safety concern lies in whether and how the model can express its own uncertainty when …

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods
for molecular property prediction. However, a key limitation of typical GNN models is their …

Graph neural networks (GNNs) have been shown to be astonishingly capable models for
molecular property prediction, particularly as surrogates for expensive density functional …