Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table

Direct Measurement Of Thicknesses, Volumes Or Compositions Of Nanomaterials By Quantitative Atomic Number Contrast In High-angle Annular Dark-field Scanning Transmission Electron Microscopy

The sizes, shapes, volumes and compositions of nanoparticles are very important parameters determining many of their properties. Efforts to measure these parameters for individual nanoparticles and to obtain reliable statistics for a large number of nanoparticles require a fast and reliable method for 3-D characterization. In this dissertation, a direct measurement method for thicknesses, volumes or compositions of nanomaterials by quantitative atomic number contrast in High-Angle Annular Dark-Field (HAADF) Scanning Transmission Electron Microscopy (STEM) is presented. A HAADF detector collects electrons scattered incoherently to high angles. The HAADF signal intensity is in first-order approximation proportional to the sample thickness and increases with atomic number. However, for larger sample thicknesses this approach fails. A simple description for the thickness dependence of the HAADFSTEM contrast has been developed in this dissertation. A new method for the calibration of the sensitivity of the HAADF detector for a FEI F30 transmission electron microscope (TEM) is developed in this dissertation. A nearly linear relationship of the HAADF signal with the electron current is confirmed. Cross sections of multilayered samples provided by TriQuint Semiconductors in Apopka, FL, for contrast calibration were obtained by focused ion-beam (FIB) preparation yielding data on the interaction cross section per atom. iv To obtain an absolute intensity calibration of the HAADF-STEM intensity, Convergent Beam Electron Diffraction (CBED) was performed on Si single crystals. However, for samples prepared by the focused ion beam technique, CBED often significantly underestimates the sample thickness. Multislice simulations from Dr. Kirkland’s C codes are used for comparison with experimental results. TEM offers high lateral resolution, but contains little or no information on the thickness of samples. Thickness maps in energy-filtered TEM (EFTEM), CBED and tilt series are so far the only methods to determine thicknesses of particles in TEM. In this work I have introduced the use of wedge-shaped multilayer samples prepared by FIB for the calibration of HAADF-STEM contrasts. This method yields quantitative contrast data as a function of sample thickness. A database with several pure elements and compounds has been compiled, containing experimental data on the fraction of electrons scattered onto the HAADF detector for each nanometer of sample thickness. The use of thick samples reveals an increased signal at the interfaces of high- and low-density materials. This effect can be explained by the transfer of scattered electrons from the high density material across the interface into the less-absorbing low-density material. The calibrations were used to determine concentration gradients in nanoscale Fe-Pt multilayers as well as thicknesses and volumes of individual Au-Fe, Pt, and Ag nanoparticles. Volumes of nanoparticles with known composition can be determined with accuracy better than 15%. Porosity determination of materials becomes available with this method as shown in an example of porous Silicon