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Light-driven self-organization of metal nanoparticles (NPs) can lead to unique optical matter systems, yet simulation of such self-organization (i.e., optical binding) is a complex computational problem that increases nonlinearly with system size. Here we show that a combined electrodynamics-molecular dynamics simulation technique can simulate the trajectories and predict stable configurations of silver NPs in optical fields. The simulated dynamic equilibrium of a two-NP system matches the probability density of oscillations for two optically bound NPs obtained experimentally. The predicted stable configurations for up to eight NPs are further compared to experimental observations of silver NP clusters formed by optical binding in a Bessel beam. All configurations are confirmed to form in real systems, including pentagonal clusters with five-fold symmetry. Our combined simulations and experiments have revealed a diverse optical matter system formed by anisotropic optical binding interactions, providing a new strategy to discover artificial materials.

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

Grid-scale energy storage is increasingly needed as wind, solar, and other intermittent renewable energy sources become more prevalent. Redox flow batteries (RFBs) are well suited to this application because of the advantages in scalability and modularity over competing technologies. Commercial aqueous flow batteries often have low energy density, but nonaqueous RFBs can offer higher energy density. Nonaqueous RFBs have not been studied as extensively as aqueous RFBs, and the use of organic solvents and organic active materials in nonaqueous RFBs presents unique membrane separator challenges compared to aqueous systems. Specifically, organic active material cross-over, which degrades battery performance, may be affected by membrane/active material thermodynamic interactions in a fundamentally different way than ionic active material cross-over in aqueous RFB membranes. Hansen solubility parameters (HSPs) were used to quantify these interactions and explain differences in organic active material permeability properties. Probe molecules with a more unfavorable HSP-determined enthalpy of mixing with the membrane polymer exhibited lower permeability or cross-over properties. The HSP approach, which accounts for the uncharged polymer backbone and the charged side chain, revealed that interactions between the uncharged organic probe molecule and the hydrophobic polymer backbone were more important for determining permeability or cross-over properties than interactions between the probe molecule and the hydrophilic side chain. This result is significant for nonaqueous RFBs because it suggests a decoupling of ionic conduction expected to predominantly occur in charged polymer regions and cross-over of organic molecules via hydrophobic or uncharged polymer regions. Such decoupling is not expected in aqueous systems where active materials are often polar or ionic and both cross-over and conduction occur predominantly in charged polymer regions. For nonaqueous RFBs, or other membrane applications where selective organic molecule transport is important, HSP analysis can guide the co-design of the polymer separator materials and soluble organic molecules.