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 …

The stock market has performed one of the most important functions in a laissez-faire
economic system by gathering people, companies, and flows of money for several centuries …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

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 …

Bayesian optimization (BO) is increasingly employed in critical applications such as
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …

Practitioners often encounter challenging real-world problems that involve a simultaneous
optimization of multiple objectives in a complex search space. To address these problems …

We use a previously unexplored Bayesian optimization framework,“evolutionary Monte
Carlo sampling,” to systematically design the arrangement of defects in an architected …

The design of chemical-based products and functional materials is vital to modern
technologies, yet remains expensive and slow. Artificial intelligence and machine learning …