Related papers: Deep Learning Models for Colloidal Nanocrystal Syn…
Here we use large-scale molecular dynamics (MD) simulations of the high-rate deformation of nanocrystalline tantalum to investigate the processes associated with plastic deformation for strains up to 100%. We use initial atomic…
The application of nanocrystals as heterogeneous catalysts and plasmonic nanoparticles requires fine control of their shape and chemical composition. A promising idea to achieve synergistic effects is to combine two distinct chemical and/or…
Catalysis, particularly heterogeneous catalysis, is crucial in the chemical industry and energy storage. Approximately 80% of all chemical products produced by heterogeneous catalysis are produced by solid catalysts, which are essential for…
Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties…
Generative models for materials have achieved strong performance on periodic bulk crystals, yet their ability to generalize across scale transitions to finite nanostructures remains largely untested. We introduce Crystal-to-Nanoparticle…
The evolution of cluster structure with size and the critical size for the transition from cluster to nanocrystal have long been fundamental problems in nanoscience. Due to limitations of experimental technology and computational methods,…
The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or…
Size and shape are critical discriminators between molecular species and states. We describe a micro-chip based high-throughput imaging approach offering rapid and precise determination of molecular properties under native solution…
The structural control of silicon nanocrystals is an important technological problem. Typically a distribution of nanocrystal sizes and shapes emerges under the uncontrolled aggregation of smaller clusters. The aim of this computational…
Food is essential for human survival, and people always try to taste different types of delicious recipes. Frequently, people choose food ingredients without even knowing their names or pick up some food ingredients that are not obvious to…
Colloidal lithography [1] is how patterns are reproduced in a variety of natural systems and is used more and more as an efficient fabrication tool in bio-, opto-, and nano-technology. Nanoparticles in the colloid are made to form a mask on…
We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements represent exact…
Next-generation integrated nanophotonic device designs leverage advanced optimization techniques such as inverse design and topology optimization which achieve high performance and extreme miniaturization by optimizing a massively complex…
Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability are still not resolved and even not…
While nanoalloys are of paramount scientific and practical interests, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including crystal phase, chemical ordering, and…
With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. This can be an extremely tedious process, often relying significantly on trial and error. Machine…
Conventional nuclear magnetic resonance (NMR) spectroscopy relies on acquiring signal from a macroscopic ensemble of molecules to gain information about molecular structure and dynamics. Transferring this technique to nanoscale sample sizes…
Obtaining 3D conformations of realistic polyatomic molecules at the quantum chemistry level remains challenging, and although recent machine learning advances offer promise, predicting large-molecule structures still requires substantial…
A new analytical expression for the size-dependent bandgap of colloidal semiconductor nanocrystals is proposed within the framework of the finite-depth square-well effective mass approximation in order to provide a quantitative description…
Crystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one…