Improved modeling of nanocrystals from atomic pair distribution function data

Accurate determination of the structure of nanomaterials is a key step towards understanding and controlling their properties. This is especially challenging for small nanoparticles, where traditional electron microscopy provides partial information about the morphology and internal atomic structure for a limited number of particles, and x-ray powder diffraction data is often broad and diffuse and not amenable to quantitative crystallographic analysis. In these cases a better approach is to use atomic pair distribution function (PDF) analysis of synchrotron x-ray total scattering data, in tandem with high-resolution imaging techniques. Even with these tools available, extracting detailed models of nanoparticle cores is notoriously difficult and time consuming. For many years, poor fits were considered to be a de facto limitation of nanoparticle studies using PDF methods, and semi-quantitative analyses were commonly employed. In this work, we aim to challenge this assumption. We started with a survey of 12 canonical metallic nanomaterials, both elemental and alloyed, prepared using different synthesis methods, with significantly different shapes and sizes as disparate as 2 nm wires and 40 nm particles, using PDF data collected at multiple synchrotron sources and beamlines. Widely applied shape-tuned attenuated crystal (AC) fcc models proved inadequate, yielding structured, coherent, and correlated fit residuals. However, equally simple discrete cluster models could account for the largest amplitude features in these difference signals. A hypothesis testing based approach to nanoparticle structure modeling systematically ruled out effects from crystallite size, composition, shape, and surface faceting as primary factors contributing to the AC misfit, and it was found that these previously ignored signals could be explained as originating from well defined domain structures in the nanoparticle cores. This analysis gave insight into how sensitive PDF analyses could be towards identifying the presence of interfaces inside ultrasmall nanoparticle cores using atomistic modeling, but still hinged on manual trial-and-error testing of clusters from different structural motifs. To address this challenge, we developed a structure screening methodology, called cluster-mining, wherein libraries of clusters from multiple structural motifs were built algorithmically and individually refined against experimental PDFs. This differs from traditional approaches for crystallographic analysis of nanoparticles where a single model containing many refinable parameters is used to fit peak profiles from a measured diffraction pattern. Instead, cluster-mining uses many structure models and highly constrained refinements to screen libraries of discrete clusters against experimental PDF data, with the aim of finding the most representative cluster structures for the ensemble average nanoparticle from any given synthesis. Finally, we wanted to identify other nanomaterial systems where this approach might prove useful, and demonstrated that the PDF was also capable of detecting seemingly subtle morphological variations in highly faceted titania photocatalyts. This opens a new avenue towards characterizing shape-controlled metal oxide nanomaterials with well-defined surface facets. To extend this work in the future, our goal is to develop new tools for building discrete nanoparticles algorithmically, integrate statistical approaches to make model selection more efficient, and ultimately, move towards an atomic scale understanding of nanoparticle structure that is comparable to bulk materials

MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning

We study how a principal can efficiently and effectively intervene on the rewards of a previously unseen learning agent in order to induce desirable outcomes. This is relevant to many real-world settings like auctions or taxation, where the principal may not know the learning behavior nor the rewards of real people. Moreover, the principal should be few-shot adaptable and minimize the number of interventions, because interventions are often costly. We introduce MERMAIDE, a model-based meta-learning framework to train a principal that can quickly adapt to out-of-distribution agents with different learning strategies and reward functions. We validate this approach step-by-step. First, in a Stackelberg setting with a best-response agent, we show that meta-learning enables quick convergence to the theoretically known Stackelberg equilibrium at test time, although noisy observations severely increase the sample complexity. We then show that our model-based meta-learning approach is cost-effective in intervening on bandit agents with unseen explore-exploit strategies. Finally, we outperform baselines that use either meta-learning or agent behavior modeling, in both $0$-shot and $K=1$-shot settings with partial agent information

Developing Customized & Secure Blockchains with Deep Federation Learning to Prevent Successive Attacks

International audienceRecently, blockchain technology has been one of the most promising fields of research aiming to enhance the security and privacy of systems. It follows a distributed mechanism to make the storage system fault-tolerant. However, even after adopting all the security measures, there are some risks for cyberattacks in the blockchain. From a statistical point of view, attacks can be compared to anomalous transactions compared to normal transactions. In this paper, these anomalous transactions can be detected using machine learning algorithms, thus making the framework much more secure. Several machine learning algorithms can detect anomalous observations. Due to the typical nature of the transactions dataset (time-series), we choose to apply a sequence to the sequence model. In this paper, we present our approach, where we use federated learning embedded with an LSTM-based autoencoder to detect anomalous transactions

Local atomic and magnetic structure of dilute magnetic semiconductor (Ba,K)(Zn,Mn)2_2As2_2

We have studied the atomic and magnetic structure of the dilute ferromagnetic semiconductor system (Ba,K)(Zn,Mn)$_2$As$_2$ through atomic and magnetic pair distribution function analysis of temperature-dependent x-ray and neutron total scattering data. We detected a change in curvature of the temperature-dependent unit cell volume of the average tetragonal crystallographic structure at a temperature coinciding with the onset of ferromagnetic order. We also observed the existence of a well-defined local orthorhombic structure on a short length scale of $\lesssim 5$ \AA, resulting in a rather asymmetrical local environment of the Mn and As ions. Finally, the magnetic PDF revealed ferromagnetic alignment of Mn spins along the crystallographic $c$-axis, with robust nearest-neighbor ferromagnetic correlations that exist even above the ferromagnetic ordering temperature. We discuss these results in the context of other experiments and theoretical studies on this system

Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle clusters from multiple structural motifs, and individually fits them to experimental PDFs. Each cluster-fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles

Direct Implicit and Explicit Energy-Conserving Particle-in-Cell Methods for Modeling of Capacitively-Coupled Plasma Devices

Achieving entire large scale kinetic modelling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving Particle-In-Cell method suffers from tight stability constraints to resolve the electron plasma length (i.e. Debye length) and time scales (i.e. plasma period). This results in very high computational cost, making this technique generally prohibitive for the large volume entire device modeling (EDM). We explore the Direct Implicit algorithm and the explicit Energy Conserving algorithm as alternatives to the standard approach, which can reduce computational cost with minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated by testing on a 2D capacitively coupled plasma discharge scenario. The investigation revels that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in reduced runtime, while incurring only a minor compromise in accuracy. The methods also allow for time steps larger than the electron plasma period, however this can lead to numerical heating or cooling. The study further demonstrates that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to acceptable level

Pair distribution function analysis of ZrO2 nanocrystals and insights in the formation of ZrO2-YBa2Cu3O7 nanocomposites

The formation of superconducting nanocomposites from preformed nanocrystals is still not well understood. Here, we examine the case of ZrO2 nanocrystals in a YBa2Cu3O7-x matrix. First we analyzed the preformed ZrO2 nanocrystals via atomic pair distribution function analysis and found that the nanocrystals have a distorted tetragonal crystal structure. Second, we investigated the influence of various surface ligands attached to the ZrO2 nanocrystals on the distribution of metal ions in the pyrolyzed matrix via secondary ion mass spectroscopy technique. The choice of stabilizing ligand is crucial in order to obtain good superconducting nanocomposite films with vortex pinning. Short, carboxylate based ligands lead to poor superconducting properties due to the inhomogeneity of metal content in the pyrolyzed matrix. Counter-intuitively, a phosphonate ligand with long chains does not disturb the growth of YBa2Cu3O7-x. Even more surprisingly, bisphosphonate polymeric ligands provide good colloidal stability in solution but do not prevent coagulation in the final film, resulting in poor pinning. These results thus shed light on the various stages of the superconducting nanocomposite formation

Complete Strain Mapping of Nanosheets of Tantalum Disulfide

Quasi-two-dimensional (quasi-2D) materials hold promise for future electronics because of their unique band structures that result in electronic and mechanical properties sensitive to crystal strains in all three dimensions. Quantifying crystal strain is a prerequisite to correlating it with the performance of the device, and calls for high resolution but spatially resolved rapid characterization methods. Here we show that using fly-scan nano X-ray diffraction we can accomplish a tensile strain sensitivity below 0.001% with a spatial resolution of better than 80 nm over a spatial extent of 100 $\mu$m on quasi 2D flakes of 1T-TaS2. Coherent diffraction patterns were collected from a $\sim$ 100 nm thick sheet of 1T-TaS2 by scanning 12keV focused X-ray beam across and rotating the sample. We demonstrate that the strain distribution around micron and sub-micron sized 'bubbles' that are present in the sample may be reconstructed from these images. The experiments use state of the art synchrotron instrumentation, and will allow rapid and non-intrusive strain mapping of thin film samples and electronic devices based on quasi 2D materials