Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

The ability to integrate resources and share knowledge across organisations empowers
scientists to expedite the scientific discovery process. This is especially crucial in addressing …

Purpose Target-based strategy is a prevalent means of drug research and development
(R&D), since targets provide effector molecules of drug action and offer the foundation of …

This article proposes a framework of linked software agents that continuously interact with
an underlying knowledge graph to automatically assess the impacts of potential flooding …

Zeolites are complex and porous crystalline inorganic materials that serve as hosts for a
variety of molecular, ionic and cluster species. Formal, machine-actionable representation of …

This paper presents a novel knowledge graph question answering (KGQA) system for
chemistry, which is implemented on hybrid knowledge graph embeddings, aiming to provide …

To substantially expedite scientific discovery, research laboratories need to be further
automated. In this regard, the scientific community envisions an" artificial intelligence …

This paper presents a dynamic knowledge graph approach that offers a reusable,
interoperable, and extensible framework for modelling power systems. Domain ontologies …

The pressing challenge of decarbonization encompasses a vast combinatorial space of
interlinked technologies, thus necessitating an increased reliance on artificial intelligence …

The adoption of heat pumps to displace the use of gas for domestic heating is a major
component of the strategy to reduce emissions in the UK. This study examines the impact of …