Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …

Abstract Machine learning-based modeling of physical systems has experienced increased
interest in recent years. Despite some impressive progress, there is still a lack of …

Abstract Machine learning has drawn growing attention from the areas of fatigue, fracture,
and structural integrity. However, most current studies are fully data-driven and may …

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning
varies depending on context and community. Researchers either focus on narrow technical …

Springback has always been a stubborn defect that affects the axial accuracy of metal
bending. The finite element simulation of springback enables effective control and precise …

Determining the structure and mechanisms of all individual functional modules of cells at
high molecular detail has often been seen as equal to understanding how cells work …

This work proposes “jointly amortized neural approximation”(JANA) of intractable likelihood
functions and posterior densities arising in Bayesian surrogate modeling and simulation …