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X-ray computed tomography (CT) has an indispensable role in constructing 3D images of objects made from light materials. However, limited by absorption coefficients, X-rays cannot deeply penetrate materials such as copper and lead. Here we…
Silicon-based CMOS technologies are predicted to reach their ultimate limits by the middle of the next decade. Research on nanotechnologies is actively conducted, in a world-wide effort to develop new technologies able to maintain the…
Thin filters and gas tight windows are used in Space to protect sensitive X-ray detectors from out-of-band electromagnetic radiation, low-energy particles, and molecular contamination. Though very thin and made of light materials, filters…
The past decade has witnessed the rapid progress in synthesizing nanoporous organic networks or polymer frameworks for various potential applications. Generally speaking, functionalization of porous networks to add extra properties and…
Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide…
Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Interpretation of…
There has been rapidly growing demand of polymeric materials coming from different aspects of modern life because of the highly diverse physical and chemical properties of polymers. Polymer informatics is an interdisciplinary research field…
Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based…
Inspired by the human brain's structure and function, neuromorphic computing has emerged as a promising approach for developing energy-efficient and powerful computing systems. Neuromorphic computing offers significant processing speed and…
The next decade will be an exciting time for computational physicists. After 50 years of being forced to use standardized commercial equipment, it will finally become relatively straightforward to adapt one's computing tools to one's own…
Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment…
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an…
Direct Dark Matter detection and studies on the nature of neutrinos demand detector systems with extremely low background levels, including from radioactivity. Additive-free, electroformed copper, in addition to a set of advantages,…
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of crystal structures has been widely used for structure…
The complexity of condensed matter arises from emergent behaviors that cannot be understood by analyzing individual constituents in isolation. While traditional condensed-matter approaches-developed primarily for ideal crystalline…
Along with the development of science and technology, the application of various computational processes in the field of science engineering is something that is being developed, even today. Currently, many studies have applied image…
High Performance Computing (HPC) aims at providing reasonably fast computing solutions to scientific and real life problems. The advent of multicore architectures is noticeable in the HPC history, because it has brought the underlying…
Quantum computers promise to impact industrial applications, for which quantum chemical calculations are required, by virtue of their high accuracy. This perspective explores the challenges and opportunities of applying quantum computers to…
Due to their inherent variabilities,nanomaterial-based sensors are challenging to translate into real-world applications,where reliability/reproducibility is key.Recently we showed Bayesian inference can be employed on engineered…
Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a…