Volume 3 Issue 4, April 2023

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.

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.

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 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’.

Proton-coupled electron transfer occurs at a variety of length and time scales and often in complex environments. This Perspective summarizes a range of modeling strategies that can be used together to address remaining challenges and provide a better understanding of such reactions.

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.

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 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.

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.