Even though Nature Computational Science is a computational-focused journal, some studies submitted to our journal might require experimental validation in order to verify the reported results and to demonstrate the usefulness of the proposed methods.

While digital twins have been recently used to represent cities and their physical structures, integrating complexity science into the digital twin approach will be key to deliver more explicable and trustworthy models and results.

This study proposes a diffusion model, ProteinSGM, for the design of novel protein folds. The designed proteins are diverse, experimentally stable and structurally consistent with predicted models

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.

Kirigami is an ancient art form that is now increasingly studied and applied in science and technology. This work presents an additive approach for the computational design of kirigami and two fabrication strategies for its physical instantiation.

A disease space is constructed from clinical records by embedding all diseases and considering a patient’s space coordinates as a measure of their health state. This measure was associated with 108 genetic loci, on which models were built to predict various morbidities.

Mental conflict has been regarded as subjective instead of quantitative. This study developed a data-driven method to decode temporal dynamics of conflict between reward and curiosity, which can elucidate mechanisms of irrational decision-making.

This study proposes a diffusion model, ProteinSGM, for the design of novel protein folds. The designed proteins are diverse, experimentally stable and structurally consistent with predicted models

GAME-Net is a graph deep learning model trained with small molecules containing a wide set of functional groups for predicting the adsorption energy of closed-shell organic molecules on metal surfaces, avoiding expensive density functional theory simulations.

NeCLAS is a machine learning pipeline that can accurately and efficiently predict nanoscale interactions, which has broad applications in biological processes and material properties.

Even though Nature Computational Science is a computational-focused journal, some studies submitted to our journal might require experimental validation in order to verify the reported results and to demonstrate the usefulness of the proposed 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.

We highlight two primary research papers, published in this issue of Nature Computational Science, on computational methods for moiré magnets.