Bayesian optimisation (BO) has been increasingly utilised to guide material discovery. While
BO is advantageous due to its sample efficiency, flexibility and versatility, it is constrained by …

Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-
evaluate black-box functions, with a broad range of real-world applications in science …

In this paper we introduce GP+, an open-source library for kernel-based learning via
Gaussian processes (GPs) which are powerful statistical models that are completely …

Scientific advancement is universally based on the dynamic interplay between theoretical
insights, modeling, and experimental discoveries. However, this feedback loop is often slow …

Small molecules exhibiting desirable property profiles are often discovered through an
iterative process of designing, synthesizing and testing sets of molecules. The selection of …

Our objective is to search a large candidate set of covalent organic frameworks (COFs) for
the one with the largest equilibrium adsorptive selectivity for xenon (Xe) over krypton (Kr) at …

Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate modeling and
design optimization by incorporating data from both high-and various low-fidelity (LF) …

Uncertainty quantification is a critical aspect of machine learning models for material
property predictions. Gaussian Process Regression (GPR) is a popular technique for …

Aerospace product design optimizations, such as micro-aerial vehicle fuselage design, often
involve multiple objectives. Multi-objective Bayesian optimization (MOBO) is an efficient …

Science is and always has been based on data, but the terms' data-centric'and the'4th
paradigm'of materials research indicate a radical change in how information is retrieved …