Surrogate modeling of advanced computer simulations using deep Gaussian processes
Not provided.
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not provided.
Not provided.
Not provided.
Not Available
Not Available
Explore the source record for details and available documents.
Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design
Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.
Not provided.
Electron attachment to DNA by low energy electrons can lead to DNA damage, so a fundamental understanding of how electrons interact with the components of nucleic acids in solution is an open challenge. In solution, low energy electrons can generate presolvated electrons, e pre – , which are efficiently scavanged by pyrimidine nucleobases to form transient negative ions, able to relax to either stable valence bound anions or undergo dissociative electron detachment or transfer to other parts of DNA/RNA leading to strand breakages. In order to understand the initial electron attachment dynamics, this paper presents a joint molecular dynamics and high-level electronic structure study into the behavior of the electronic states of the solvated uracil anion. Both the valence π* and nonvalence e pre – states of the solvated uracil system are studied, and the effect of the solvent environment and the geometric structure of the uracil core are uncoupled to gain insight into the physical origin of the stabilization of the solvated uracil anion. As a result, solvent reorganization is found to play a dominant role followed by relaxation of the uracil core.
Not provided.
Not Available
Not Available