Computational methods articles from across Nature Portfolio

VSSR-MC is a Markov chain method based on virtual adsorption sites that interfaces with a neural network force field to provide fast, accurate and comprehensive sampling of material surfaces.

Data-driven surrogate models are used in computational physics and engineering to greatly speed up evaluations of the properties of partial differential equations, but they come with a heavy computational cost associated with training. Pestourie et al. combine a low-fidelity physics model with a generative deep neural network and demonstrate improved accuracy–cost trade-offs compared with standard deep neural networks and high-fidelity numerical solvers.

An autonomous laboratory, the A-Lab, is presented that combines computations, literature data, machine learning and active learning, which discovered and synthesized 41 novel compounds from a set of 58 targets after 17 days of operation.

Graph neural networks (GNNs) present a promising route for machine learning of solid-state materials’ properties, but methods capable of directly predicting defect properties from ideal, defect-free structures are needed. A GNN developed for direct defect property predictions enables high-throughput screening of redox-active oxides for energy applications and beyond.

The prediction of stable crystal structures is an important part of designing solid-state crystalline materials with desired properties. Recent advances in structural feature representations and generative neural networks promise the ability to efficiently create new stable structures to use for inverse design and to search for materials with tailored functionalities.

A momentum-space algorithm is proposed to simulate electron dynamics with time-dependent density functional theory, which expands the scope of conventional real-space methods.

As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.