Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Conservation of marine ecosystems has been highlighted as a priority to ensure a
sustainable future. Effective management requires data collection over large spatio-temporal …

An ensemble technique using XGBoost model is employed to predict the compressive
strength of ultra-high performance concrete (UHPC). A 931 UHPC mixture collection with 17 …

We extend the scope of our recently developed approach for unsupervised automated
discovery of material laws (denoted as EUCLID) to the general case of a material belonging …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …

Abstract β-Carotene has a positive impact on human health as a precursor of vitamin A.
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …

Herein, we present a new data-driven multiscale framework called FE ANN which is based
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …

A hybrid model integrates a first‐principles model with a data‐driven model which predicts
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …

Typically, magnesium alloys have been designed using a so-called hill-climbing approach,
with rather incremental advances over the past century. Iterative and incremental alloy …