Learning local equivariant representations for large-scale atomistic dynamics.

A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy.

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions.

Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a fixed set of properties. Recent lines of research instead aim to explicitly learn the electronic structure via molecular wavefunctions, from which other quantum chemical properties can be directly derived.

A community-powered search of machine learning strategy space to find NMR property prediction models.

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules.

Plasma Synthesis of Spherical Crystalline and Amorphous Electrolyte Nanopowders for Solid-State Batteries.

Here, we demonstrate the theory-guided plasma synthesis of high purity nanocrystalline LiSiPO and fully amorphous LiSiPON. The synthesis involves the injection of single or mixed phase precursors directly into a plasma torch. As the material exits the plasma torch, it is quenched into spherical nanocrystalline or amorphous nanopowders.

Origin of Spinel Nanocheckerboards via First Principles.

Self-organizing nanocheckerboards have been experimentally fabricated in Mn-based spinels but have not yet been explained with first principles. Using density-functional theory, we explain the phase diagram of the ZnMn_{x}Ga_{2-x}O_{4} system and the origin of nanocheckerboards. We predict total phase separation at zero temperature and then show the combination of kinetics, thermodynamics, and Jahn-Teller physics that generates the system's observed behavior.

Light-induced electronic non-equilibrium in plasmonic particles.

We consider the transient non-equilibrium electronic distribution that is created in a metal nanoparticle upon plasmon excitation. Following light absorption, the created plasmons decohere within a few femtoseconds, producing uncorrelated electron-hole pairs. The corresponding non-thermal electronic distribution evolves in response to the photo-exciting pulse and to subsequent relaxation processes.