Machine learning has become a central technique for modeling in science and engineering, either complementing or as surrogates to physics-based models. Significant efforts have recently been devoted models capable of predicting field quantities, but the limitations current state-of-the-art describing complex physics are not well understood. We characterize ability generative diffusion adversarial networks (GANs) describe Ising model. find trained using equilibrium configurations obtained...

Self-assembly of faceted nanoparticles is a promising route for fabricating nanomaterials; however, achieving low-dimensional assemblies particles with tunable orientations challenging. Here, we demonstrate that trapping surface-functionalized at fluid-fluid interfaces viable approach controlling particle orientation and facilitating their assembly into unique one- two-dimensional superstructures. Using molecular dynamics simulations polymer-grafted nanocubes in polymer bilayer along...

The derived analytical potential, which accurately captures the vdW energy landscape of diverse particle shapes, could significantly accelerate simulations faceted nanoparticles.

Predictive models for the thermal, chemical, and mechanical response of high explosives at extreme conditions are important investigating their performance safety. We introduce a particle-based, reactive model 1,3,5-trinitro-1,3,5-triazinane (RDX) with molecular resolution utilizing generalized energy-conserving dissipative particle dynamics reactions. The is parameterized respect to data from atomistic simulations as well quantum calculations, thus bridging atomic processes mesoscales,...

Surface functionalization of nanoparticles with polymer grafts was recently shown to be a viable strategy for controlling the relative orientation shaped in their higher-order assemblies. In this study, we investigated silico orientational phase behavior coplanar polymer-grafted nanocubes confined thin film. We first used Monte Carlo simulations compute two-particle interaction free-energy landscape and identify globally stable configurations. The were found exhibit four phases: those...

Gold nanorods assembled in a side-by-side chiral configuration have potential applications sensing due to their strong chiroptical surface plasmon resonances. Recent experiments shown that dimers of gold bridged by double-stranded DNA exhibit variable configurations depending on the chemical and ionic properties solvent medium. Here, we uncover underlying physics governing this intriguing behavior such DNA-bridged theoretically evaluating configurational free energy landscape. Our results...

Minimum free energy pathway analysis reveals the assembly mechanism of ligand-grafted nanocubes, including reaction coordinate, metastable states, and barriers associated with assembly.

Shock-induced plasticity and structural changes in energetic molecular crystals are well documented. These processes couple with the leading shock wave affect its propagation, resulting long, transient responses that challenging to capture all-atom simulations due their time scale. Hence, effects of this coupling response on formation hotspots initiation chemistry remain unclear. To address these challenges, we investigate role shock-induced plastic deformation a recently developed...

Embedding percolating networks of nanoparticles (NPs) within polymers is a promising approach for mechanically reinforcing and introducing novel electronic, transport, catalytic properties into otherwise inert polymers. While such may be obtained through kinetic assembly unary system NPs, the ensuing structures exhibit limited morphologies. Here, we investigate possibility increasing diversity NP multiple species NPs. Using lattice Monte Carlo simulations show that from co-assembly two...

Mnemonics is a powerful tool to assist memorization of large amount complex information. Amongst the different techniques, memory palace, also known as method Loci, presents significant results in enhancing performance for massive lists numbers, objects and even texts. Substantial research has been conducted determine both validity applying this technique education, well that VR ease process learning technique. Experiments have shown auspicious suggesting virtual environment helps with...

Condense phase molecular systems organize in wide range of distinct configurations, including amorphous melt and glass as well crystals often exhibiting polymorphism, that originate from their intricate intra- intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach all-atom simulations, current cannot capture diversity structures. We introduce a generally applicable approach develop CG force fields combining...

Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, major challenge remains acquisition initial data and development workflows to generate at each iteration. In this study, we demonstrate significant speedup an task by reusing published simulation workflow available for online simulations its associated repository, where results run are automatically stored. Both follow FAIR (findable, accessible,...

Abstract Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, field of energetic materials data scarcity limits accuracy even applicability ML tools. To address limitations, we compiled multi‐modal data: both experimental computational results for several properties. We find that multi‐task neural networks can learn from outperform single‐task models trained specific As expected, improvement is more significant...

Machine learning has become a central technique for modeling in science and engineering, either complementing or as surrogates to physics-based models. Significant efforts have recently been devoted models capable of predicting field quantities but the limitations current state-of-the-art describing complex physics are not well understood. We characterize ability generative diffusion adversarial networks (GAN) describe Ising model. find trained using equilibrium configurations obtained...

Data science and artificial intelligence have become an indispensable part of scientific research. While such methods rely on high-quality large quantities machine-readable data, the current data infrastructure faces significant challenges that limit effective curation sharing. These include insufficient return investment for researchers to share quality logistical difficulties in maintaining long-term repositories, absence standardized evaluating relative importance various datasets. To...

Purpose: To evaluate the clinical need for automated decision-support platforms Adaptive Radiotherapy Therapy (ART) of Head & Neck cancer (HNC) patients. Methods: We tested RTapp (SegAna), a new software ART, to investigate 22 HNC patients data retrospectively. For each fraction, estimated daily and cumulative doses received by targets OARs from 3D imaging in real-time. also included prediction algorithm that analyzed dosimetric parameters (DP) trends against endpoints (DE) trigger...