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1. Incorporation of density scaling constraint in density functional design via contrastive representation learning

   https://doi.org/10.1039/D3DD00114H  

   Gong, Weiyi; Sun, Tao; Bai, Hexin; Chowdhury, Shah Tanvir; Chu, Peng; Aryal, Anoj; Yu, Jie; Ling, Haibin; Perdew, John P.; Yan, Qimin (October 2023, Digital Discovery)

   We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design. 

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2. Design of resonant elastodynamic metasurfaces to control S Lamb waves using topology optimization

   https://doi.org/10.1121/10.0015123  

   Giraldo_Guzman, Daniel; Pillarisetti, Lalith_Sai_Srinivas; Sridhar, Sashank; Lissenden, Cliff_J; Frecker, Mary; Shokouhi, Parisa (November 2022, JASA Express Letters)

   Control of guided waves has applications across length scales ranging from surface acoustic wave devices to seismic barriers. Resonant elastodynamic metasurfaces present attractive means of guided wave control by generating frequency stop-bandgaps using local resonators. This work addresses the systematic design of these resonators using a density-based topology optimization formulated as an eigenfrequency matching problem that tailors antiresonance eigenfrequencies. The effectiveness of our systematic design methodology is presented in a case study, where topologically optimized resonators are shown to prevent the propagation of the S0 wave mode in an aluminum plate. 

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3. Approximation Algorithms for D -optimal Design

   https://doi.org/10.1287/moor.2019.1041  

   Singh, Mohit; Xie, Weijun (November 2020, Mathematics of Operations Research)

   null (Ed.)

   Experimental design is a classical statistics problem, and its aim is to estimate an unknown vector from linear measurements where a Gaussian noise is introduced in each measurement. For the combinatorial experimental design problem, the goal is to pick a subset of experiments so as to make the most accurate estimate of the unknown parameters. In this paper, we will study one of the most robust measures of error estimation—the D-optimality criterion, which corresponds to minimizing the volume of the confidence ellipsoid for the estimation error. The problem gives rise to two natural variants depending on whether repetitions of experiments are allowed or not. We first propose an approximation algorithm with a 1/e-approximation for the D-optimal design problem with and without repetitions, giving the first constant-factor approximation for the problem. We then analyze another sampling approximation algorithm and prove that it is asymptotically optimal. Finally, for D-optimal design with repetitions, we study a different algorithm proposed by the literature and show that it can improve this asymptotic approximation ratio. All the sampling algorithms studied in this paper are shown to admit polynomial-time deterministic implementations. 

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4. Design of an atomic layer deposition system with in situ reflection high energy electron diffraction

   https://doi.org/10.1063/5.0206286  

   Howzen, Alexandra J; Caspar, Justin; Oztekin, Alparslan; Strandwitz, Nicholas C (November 2024, Review of Scientific Instruments)

   We report the design, fabrication, and testing of an atomic layer deposition (ALD) system that is capable of reflection high energy electron diffraction (RHEED) in a single chamber. The details and specifications of the system are described and include capabilities of RHEED at varied accelerating voltages, sample rotation (azimuthal) control, sample height control, sample heating up to set temperatures of 1050 °C, and either single- or dual-differential pumping designs. Thermal and flow simulations were used to justify selected system dimensions as well as carrier gas/precursor mass flow rates. Temperature calibration was conducted to determine actual sample temperatures that are necessary for meaningful analysis of thermally induced transitions in ALD thin films. Several demonstrations of RHEED in the system are described. Calibration of the camera length was conducted using a gold thin film by analyzing RHEED images. Finally, RHEED conducted at a series of increasing temperatures was used to monitor the crystallization of an ALD HfO2 thin film. The crystallization temperature and the ring pattern were consistent with the monoclinic structure as determined by separate x-ray diffraction-based measurements. 

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5. Data centric nanocomposites design via mixed-variable Bayesian optimization

   https://doi.org/10.1039/d0me00079e  

   Iyer, Akshay; Zhang, Yichi; Prasad, Aditya; Gupta, Praveen; Tao, Siyu; Wang, Yixing; Prabhune, Prajakta; Schadler, Linda S.; Brinson, L. Catherine; Chen, Wei (January 2020, Molecular Systems Design & Engineering)

   With an unprecedented combination of mechanical and electrical properties, polymer nanocomposites have the potential to be widely used across multiple industries. Tailoring nanocomposites to meet application specific requirements remains a challenging task, owing to the vast, mixed-variable design space that includes composition ( i.e. choice of polymer, nanoparticle, and surface modification) and microstructures ( i.e. dispersion and geometric arrangement of particles) of the nanocomposite material. Modeling properties of the interphase, the region surrounding a nanoparticle, introduces additional complexity to the design process and requires computationally expensive simulations. As a result, previous attempts at designing polymer nanocomposites have focused on finding the optimal microstructure for only a fixed combination of constituents. In this article, we propose a data centric design framework to concurrently identify optimal composition and microstructure using mixed-variable Bayesian optimization. This framework integrates experimental data with state-of-the-art techniques in interphase modeling, microstructure characterization and reconstructions and machine learning. Latent variable Gaussian processes (LVGPs) quantifies the lack-of-data uncertainty over the mixed-variable design space that consists of qualitative and quantitative material design variables. The design of electrically insulating nanocomposites is cast as a multicriteria optimization problem with the goal of maximizing dielectric breakdown strength while minimizing dielectric permittivity and dielectric loss. Within tens of simulations, our method identifies a diverse set of designs on the Pareto frontier indicating the tradeoff between dielectric properties. These findings project data centric design, effectively integrating experimental data with simulations for Bayesian Optimization, as an effective approach for design of engineered material systems. 

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