Volume 6 Issue 8, August 2021

The design of new functional polymers depends on the successful navigation of their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning provide exciting opportunities for the engineering of fit-for-purpose polymeric materials.

Computational sustainability harnesses computing and artificial intelligence for human well-being and the protection of our planet. Materials science is central to many sustainability challenges. Exploiting synergies between computational sustainability and materials science advances both fields, furthering the ultimate goal of establishing a sustainable future.

Google Applied Science is a division of Google Research that applies computational methods, and in particular machine learning, to a broad range of scientific problems. Patrick Riley, until recently one of their software engineers and now head of artificial intelligence at Relay Therapeutics, talks to Nature Reviews Materials about his experience working on machine-learning projects in an industrial setting.

An article in Nature Communications reports a method for the rapid detection of SARS-CoV-2 in saliva samples using nanopores and a machine learning algorithm.

An article in npj Computational Materials reports a deep-learning workflow for the analysis of microscopy images that can adapt to changing conditions.

An article in Science Robotics presents an autonomous excavator system able to recognize and load a range of materials in different environments.

An article in Nature Chemistry uses the knowledge gathered in the Cambridge Structural Database to build a machine-learning model that predicts the oxidation states of metal–organic frameworks.

An article in npj Computational Materials reports a machine learning-based solver that predicts local stresses in mechanically heterogeneous solids.

The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human–machine partnerships for fast and efficient virtual materials screening, development and discovery.

Neural networks can capture nonlinear relationships in high-dimensional spaces and are powerful tools for photonic-system modelling. This Review discusses how deep neural networks can serve as surrogate electromagnetic solvers, inverse modelling tools and global device optimizers.

Machine learning can be applied for the controlled synthesis of nanoparticles with precise properties. This Review discusses different machine learning approaches for the synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles, and highlights key approaches for the collection of large datasets.

High-throughput experimental technologies can generate large data sets of cell-type-specific information, allowing the study of multicellular complexity. This Review discusses machine learning approaches, in particular, deep learning and network-based models, which can be applied to analyse, interpret and model these data sets.

Machine learning is enabling a metallurgical renaissance. This Review discusses recent progress in representations, descriptors and interatomic potentials, overviewing metallic glasses, high-entropy alloys, superalloys and shape-memory alloys, magnets and catalysts, and the prediction of mechanical and thermal properties.