Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Two-dimensional (2D) material research is rapidly evolving to broaden the spectrum of
emergent 2D systems. Here, we review recent advances in the theory, synthesis …

Recent advances in many domains require more and more complicated experiment design.
Such complicated experiments often have many parameters, which necessitate parameter …

New tools enable new ways of working, and materials science is no exception. In materials
discovery, traditional manual, serial, and human-intensive work is being augmented by …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Optimization algorithms are used to improve model accuracy. The optimization process
undergoes multiple cycles until convergence. A variety of optimization strategies have been …

Nanocarriers (NCs) that can precisely deliver active agents, nutrients and genetic materials
into plants will make crop agriculture more resilient to climate change and sustainable. As a …

This study aims to provide an efficient and accurate machine learning (ML) approach for
predicting the creep behavior of concrete. Three ensemble machine learning (EML) models …

Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …