Deep learning has transformed the way large and complex image datasets can be
processed, reshaping what is possible in bioimage analysis. As the complexity and size of …

The development of high-resolution imaging methods such as electron and scanning probe
microscopy and atomic probe tomography have provided a wealth of information on the …

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to
quantify, yet are essential in engineering many chemical systems, such as batteries and …

Constitutive laws underlie most physical processes in nature. However, learning such
equations in heterogeneous solids (for example, due to phase separation) is challenging …

Classical dynamical density functional theory (DDFT) has become one of the central
modeling approaches in nonequilibrium soft matter physics. Recent years have seen the …

We show a method to embed physical laws and on-line observation into machine learning
so that irrelevant low-cost battery data can be utilized to identify complex system parameters …

We consider the Bayesian calibration of models describing the phenomenon of block
copolymer (BCP) self-assembly, which is to infer model parameters and their uncertainty …

The Cahn–Hilliard equation is commonly used to study multi-component soft systems such
as polymer blends at continuum scales. We first systematically explore various features of …

The crystallization is a typical process in material science and this process can be described
by the phase-field crystal type models. To investigate the dynamics of phase-field crystal …

Physico‐chemical continuum battery models are typically parameterized by manual fits,
relying on the individual expertise of researchers. In this article, we introduce a computer …