Discovery of high-entropy ceramics via machine learning

AbstractAlthough high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance

Lowering minimum eye height to increase peak knee and hip flexion during landing

The purpose was to determine the effect of lowering minimum eye height through an externally focused object on knee and hip flexion and impact forces during jump-landing. Kinematics and ground reaction forces were collected when 20 male and 19 female participants performed jump-landing trials with their natural minimum eye height, and trials focusing on lowering their minimum eye height to an external object, which was set at 5% or 10% of standing height lower. Participants demonstrated decreased minimum eye height and increased peak knee and hip flexion during early-landing and stance phase when focusing on lowering eye height to the external object (p \u3c 0.01). Peak vertical ground reaction forces during early-landing also decreased for the greater force group (p \u3c 0.001). Jump-landing training through manipulating eye height provides a strategy that involves an external focus and intrinsic feedback, which may have advantages in promoting learning and practical application

Bridge damage identification from moving load induced deflection based on wavelet transform and Lipschitz exponent

The wavelet transform and Lipschitz exponent perform well in detecting signal singularity.With the bridge crack damage modeled as rotational springs based on fracture mechanics, the deflection time history of the beam under the moving load is determined with a numerical method. The continuous wavelet transformation (CWT) is applied to the deflection of the beam to identify the location of the damage, and the Lipschitz exponent is used to evaluate the damage degree. The influence of different damage degrees,multiple damage, different sensor locations, load velocity and load magnitude are studied.Besides, the feasibility of this method is verified by a model experiment

Multiscale Charaterization Techniques to Elucidate Mechanical Behavior of Materials

Deformation of materials, especially metal alloys, is a heterogenous multiscale phenomenon. This is due to the complex synergetic effects of several different factors: macroscopic boundary conditions for the applied forces, anisotropy in the microstructure/crystallographic orientation, crystal slipping due to multiaxial stress state, non-isotropic interactions of dislocations, etc. To elucidate the complex deformation mechanism of metals and alloys, a variety of multiscale characterization techniques have been developed and applied. Defect density calculation is established based on the Nye dislocation density tensor that relates lattice curvature measured from electron backscatter diffraction to extract geometrically necessary dislocation density. Using this method, anisotropy effects in shear localization of metals is quantified using GND density calculation in 7039 aluminum alloy (grain morphological anisotropy) and high purity titanium (crystallographic anisotropy).The GND density characterization technique has also been used to validate Ashby’s model on dislocation type evolution by characterizing deformed nickel samples. This is the first experimental validation of Ashby’s model, which proves that the SSDs are the dominant materials’ strength contributor at higher applied loads. GNDs are only present at a larger amount in the early stage of deformation. To increase the sensitivity of the EBSD technique, reconstruction of 2D electron backscatter diffraction patterns into spherical Kikuchi map has been explored. It potentially enables more accurate orientation calculation for GND calculation as well as improved pattern center calibration and phase analysis.To characterize local strain distributed across the entire sample, new digital image correlation based on computer vision algorithm has been developed to show very accurate surface strain mapping of homogeneous/heterogeneously deformed materials (with artificially speckle patterns) by comparing with results obtained from open-source software. Moreover, this method also has shown improved strain sensitivity in analyzing samples using its own pattern (natural pattern), in comparison to the cross-correlation based DIC method.Microscopic residual stress and strain are more conveniently studies using HR-EBSD method. A new HR-EBSD method is developed by employing demons registration to remap reference pattern towards pattern to solve the phantom strain problem. Additionally, the rotation, stress, and strain sensitivity have been shown to be around 0.5×10-4, 35 MPa and 2×10-4, respectively

Client self-defense against model poisoning in federated learning

Federated Learning is highly susceptible to backdoor and targeted attacks as participants can manipulate their data and models locally without any oversight on whether they follow the correct process. There are a number of server-side defenses that mitigate the attacks by modifying or rejecting local updates submitted by clients. However, we find that bursty adversarial patterns with a high variance in the number of malicious clients can circumvent the existing defenses. We propose a client-self defense, LeadFL, that is combined with existing server-side defenses to thwart backdoor and targeted attacks. The core idea of LeadFL is a novel regularization term in local model training such that the Hessian matrix of local gradients is nullified. We provide the convergence analysis of LeadFL and its robustness guarantee in terms of certified radius. Our empirical evaluation shows that LeadFL is able to mitigate bursty adversarial patterns for both iid and non-iid data distributions. It frequently reduces the backdoor accuracy from more than 75% for state-of-the-art defenses to less than 10% while its impact on the main task accuracy is always less than for other client-side defenses

Unified thermodynamic model to calculate COP of diverse sorption heat pump cycles: Adsorption, absorption, resorption, and multistep crystalline reactions

A straightforward thermodynamic model is developed in this work to analyze the efficiency limit of diverse sorption systems. A method is presented to quantify the dead thermal mass of heat exchangers Solid and liquid sorbents based on chemisorption or physical adsorption are accommodated. Four possible single-effect configurations are considered: basic absorption or adsorption (separate desorber, absorber, condenser, and evaporator); separate condenser/evaporator (two identical sorbent-containing reactors with a condenser and a separate direct expansion evaporator); combined condenser/evaporator (one salt-containing reactor with a combined condenser/evaporator module); and resorption (two sorbent-containing reactors, each with a different sorbent). The analytical model was verified against an empirical heat and mass transfer model derived from component experimental results. It was then used to evaluate and determine the optimal design for an ammoniate salt-based solid/gas sorption heat pump for a space heating application. The effects on system performance were evaluated with respect to different working pairs, dead thermal mass factors, and system operating temperatures. The effect of reactor dead mass as well as heat recovery on system performance was also studied for each configuration. Based on the analysis in this work, an ammonia resorption cycle using LiCl/NaBr as the working pair was found to be the most suitable single-effect cycle for space heating applications. The maximum cycle heating coefficient of performance for the design conditions was 1.50 with 50% heat recovery and 1.34 without heat recovery