Articles in 2023

An image-inspired deep-learning model is developed to generate realistic de novo protein structures and scaffolds around functional sites, which helps the search for new structures and functions in protein engineering.

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

A graph neural network — GAME-Net — has been developed to predict the adsorption energy of organic molecules on metal surfaces, which is a key descriptor of heterogeneous catalytic activity. This method allows for the study of large molecules derived from raw materials such as plastic waste, avoiding the use of costly and time-intensive first-principles simulations.

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.

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.

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

Two computational methods — one physics-based, and the other one deep-learning based — are proposed to enable the systematic investigation of magnetic order in moiré magnets from first principles.

A rotational and time-reversal equivariant neural network designed to represent the spin–orbital density functional theory Hamiltonian as a function of the atomic and magnetic structure enables ab initio electronic-structure calculations of magnetic superstructures. These calculations can efficiently and accurately predict subtle magnetic effects in various chemical environments.

A microscopic moiré spin model that enables the description of moiré magnetic exchange interactions via a sliding-mapping method is proposed. The twist-angle and substrate-influenced magnetic phase diagram addresses disagreements between theories and experiments.

A deep learning ab initio method for studying magnetic materials is developed, reducing the computational cost and opening opportunities to predict the electronic properties of magnetic superstructures, such as magnetic skyrmions.

A machine learning algorithm has been developed to capture and analyze rare molecular processes, revealing how molecules self-organize and function. The algorithm is general and can be applied whenever a dynamic system has a notion of ‘likely fate’.

A method to compute the quantum harmonic free energy contributions in large materials and biomolecular simulations at a reasonable cost is proposed, making quantum mechanical estimates of thermodynamic quantities possible for complex systems.

A machine learning algorithm speeds up the sampling of rare assembly events, discovers their mechanisms, extrapolates them across chemical and thermodynamic space, and condenses the learned assembly mechanisms into a human-interpretable form.

Identifying the origins and nature of non-genetic heterogeneity in cancer has widespread clinical ramifications. In this Review, the authors discuss how computational models and tools have been used to provide insights into this phenomenon and how they can help tackle the disease in the future.

Discovering biological patterns from omics data is challenging due to the high dimensionality of biological data. A computational framework is presented to more efficiently calculate correlations among omics features and to build networks by estimating important connections.

An approach, CorALS, is proposed to enable the construction and analysis of large-scale correlation networks for high-dimensional biological data as an open-source framework in Python.