Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

The past decade has seen a sharp increase in machine learning (ML) applications in
scientific research. This review introduces the basic constituents of ML, including databases …

Machine learning has been used to study chemical reactivity for a long time in fields such as
physical organic chemistry, chemometrics and cheminformatics. Recent advances in …

We present Descending from Stochastic Clustering Variance Regression
(DiSCoVeR)(https://www. github. com/sparks-baird/mat_discover), a Python tool for …

The discovery of new materials has been the essential force which brings a discontinuous
improvement to industrial products' performance. However, the extra-vast combinatorial …

A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …

Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …

The number of 'small'molecules that may be of interest to chemical biologists—chemical
space—is enormous, but the fraction that have ever been made is tiny. Most strategies are …

Developing new materials with target properties via the traditional trial-and-error ways is
cost-inefficient, and sometimes ends up with fruitlessness, therefore, simulation-driven …

Designing functional molecules and advanced materials requires complex design choices:
tuning continuous process parameters such as temperatures or flow rates, while …