Theory and computation articles from across Nature Portfolio

Ab-initio methods often require many steps before the simulated structure can escape trivial local energy minimums. Here, authors show that tiered tensor transform (3T) can be used to explore complex chemical reactions with modest count of DFT steps.

Simulating the magnetic domain wall dynamics in ferromagnetic materials is crucial for designing spintronics devices, but including material imperfections is often challenging. Here, the effects of individual dislocations on domain wall dynamics in thin films of iron is investigated by micromagnetic simulations.

A highly efficient reconstruction method has been developed for the direct computation of Hamiltonian matrices in the atomic orbital basis from density functional theory calculations originally performed in the plane wave basis. This enables machine learning calculations of electronic structures on a large scale, which are otherwise not feasible with standard methods, and thus fills a methodological gap in terms of accessible length scales.

Large language models are very effective at solving general tasks, but can also be useful in materials design and extracting and using information from the scientific literature and unstructured corpora. In the domain of alloy design and manufacturing, they can expedite the materials design process and enable the inclusion of holistic criteria.

This issue of Nature Computational Science features a Focus that highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.

By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.

A machine learning framework that uses atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network is established to calculate electron–phonon coupling (EPC). This approach accelerates the calculations by several orders of magnitude, enabling EPC-related properties to be predicted for complex systems using highly accurate functionals.