In the context of climate change and the circular economy, biochar has recently found many
applications in various sectors as a versatile and recycled material. Here, we review …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …

Abstract Design of de novo biological sequences with desired properties, like protein and
DNA sequences, often involves an active loop with several rounds of molecule ideation and …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene,
from low‐value or waste materials through flash Joule heating. Though this capability is …

Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python
library for fast and scalable Bayesian optimization. It has been developed mainly for …

A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …

Designing protein sequences with desired biological function is crucial in biology and
chemistry. Recent machine learning methods use a surrogate sequence-function model to …

We study the problem of optimizing biological sequences, eg, proteins, DNA, and RNA, to
maximize a black-box score function that is only evaluated in an offline dataset. We propose …