Volume 3 Issue 11, November 2023

Artificial intelligence (AI) drives innovation across society, economies and science. We argue for the importance of building AI technology according to open-source principles to foster accessibility, collaboration, responsibility and interoperability.

A recent study presents an approach for characterizing and quantifying the pore space in assemblies of particles, enabling research into pore-scale flow physics and insight into the interplay between the solid and void phases in granular materials.

The accurate prediction of molecular spectra is essential for substance discovery and structure identification, but conventional quantum chemistry methods are computationally expensive. Now, DetaNet achieves the accuracy of quantum chemistry while improving the efficiency of prediction of organic molecular spectra.

Using deep learning methods to study gene regulation has become popular, but designing accessible and customizable software for this purpose remains a challenge. This work introduces a computational toolkit called EUGENe that facilitates the development of end-to-end deep learning workflows in regulatory genomics.

Autoencoders are versatile tools for molecular informatics with the opportunity for advancing molecule and drug design. In this Review, the authors highlight the active areas of development in the field and explore the challenges that need to be addressed moving forward.

The capability of predicting stable materials is important to further accelerate the discovery of novel materials. In this Review, the authors discuss recent developments in machine learning techniques for assessing the stability of materials and highlight the opportunities in further advancing the field.

Designing accessible, interoperable and reusable software for applying deep learning to the study of gene regulation has been a challenge in genomics research. EUGENe is a toolkit that addresses this gap and streamlines end-to-end analyses.

A deep-learning model, DetaNet, is proposed to efficiently and precisely predict molecular scalars, vectorial and tensorial properties, as well as the infrared, Raman, ultraviolet–visible and nuclear magnetic resonance spectra.

A hybrid machine learning–physics model is developed that reduces simulation cost by two orders of magnitude while retaining high ab initio accuracy, to predict free-energy transition states for hydrogen combustion reactions.

LOVAMAP is an analysis software that accurately identifies 3D pores in packed particle systems by exploiting information about the particle configuration as a basis for segmentation. Using the software, the authors were able to uncover striking relationships between particle and pore properties.