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The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop…

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on…

The development of new computing technologies has given a new stimulus in the study of multiferroics. The use of multiferroics allows the realization of competitive energy efficient scalable logic and storage devices. The low-power…

Fully harnessing the vast design space enabled by metamaterials to control electromagnetic (EM) fields remains an open problem for researchers. Inverse-design techniques have shown to best exploit the degrees of freedom available in design,…

Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural…

Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern…

Following demands for materials with peculiar transport properties, e.g. in magnetoelectronics or thermoelectrics, there is a need for materials modeling at the quantum-mechanical level. We combine density-functional with various…

Replicating the computational functionalities and performances of the brain remains one of the biggest challenges for the future of information and communication technologies. Such an ambitious goal requires research efforts from the…

Information can be stored in magnetic materials by encoding with the direction of the magnetic moment of elements. A figure of merit for these systems is the energy needed to change the information rewrite the storage by changing the…

A duality between the properties of many spinor bosons on a regular lattice and those of a single particle on a weighted graph reveals that a quantum particle can traverse an infinite hierarchy of networks with perfect probability in…

Using electric dipoles to describe light-matter interactions between two entities is a conventional approximation in physics, chemistry, and material sciences. However, the lack of material structures makes the approximation inadequate when…

Topological quantum materials hold great promise for future technological applications. Their unique electronic properties, such as protected surface states and exotic quasiparticles, offer opportunities for designing novel electronic…

Magnetism is very important in science and technology, from magnetic recording to energy generation to trapping cold atoms. Physicists have managed to master magnetism - to create and manipulate magnetic fields- almost at will.…

We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted…

Magnets are used in electronics to store and read information. A magnetic moment is rotated to a desired direction, so that information can later be retrieved by reading this orientation. Controlling the moment via electric currents causes…

The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using…

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena…

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials and their properties. In particular, controlling the…

Nanostructures with open shell transition metal or molecular constituents host often strong electronic correlations and are highly sensitive to atomistic material details. This tutorial review discusses method developments and applications…

Unconventional magnetism represents a paradigm shift in condensed matter physics, effectively bridging the fast, high-density advantages of antiferromagnets with the facile read-write capability of ferromagnets. Recent developments in spin…