An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Zeolites are versatile catalysts and molecular sieves with large topological diversity, but
managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Microporous zeolites that can act as heterogeneous catalysts have continued to attract a
great deal of academic and industrial interest, but current progress in their synthesis and …

A core objective of synthesizing zeolites for widespread applications is to produce materials
with properties and corresponding performances that exceed conventional counterparts …

Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …

Zeolite catalysts and adsorbents have been an integral part of many commercial processes
and are projected to play a significant role in emerging technologies to address the …