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23 results for “Inverse design”
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
Figure 2. (a) Representation of laser servo-driver for inverse kinematics analysis; (b) Representation of the lag angle, B, of the internal servomechanism (magnified version of the chin-rest).-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>As the servo-driver will be attached in the chin-rest in a non-central area with respect to the<br> semispherical structure shown in Figure 1(a), it is necessary to calculate a lag angle, according to<br> the measurements from the chin-rest, see Figure 2(b). This was done using a hybrid formula based<br> on the law of cosines,</p>
Inverse Design of Nanophotonic Solid-State Quantum Emitter Single-photon Sources: Data
<p>Data regarding the results presented in the paper "Inverse Design of Nanophotonic Solid-State Quantum Emitter Single-photon Sources".</p>
Data and code for figures: Tailoring microcombs with inverse-designed, meta-dispersion microresonators
<p>This dataset contains the data presented in the Figures of the paper Tailoring microcombs with inverse-designed, meta-dispersion microresonators. <em>Nat. Photon.</em> (2023) DOI: 10.1038/s41566-023-01252-7.</p> <p>Generated with MatlabR2022, the figureData.mat files contain the data for each panel and the associated createfigure.m code reads the data and assembles the plots.</p>
Replication Data for "Inverse-designed low-index-contrast structures on silicon photonics platform for vector-matrix multiplication"
<p>COMSOL files and Python post-processing code.</p>
Data and Codes for Experimentally Validated Inverse design of Multi Property Fe-Co-Ni alloys: Data and codes release v1.0.1
<p>Data and Codes for Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys</p>
Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning
<p>OctagonalRR_AOPS: Datasets for "<strong>Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning</strong>," (2024).</p>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models
<p>The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data:</p> <p>Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures.</p> <p>Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths.<br><br>The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design. </p>
Inverse folding for antibody sequence design using deep learning
<p>Model weights of the <a href="https://arxiv.org/abs/2310.19513">AbMPNN model (arXiv:2310.19513)</a> presented at the <a href="https://icml-compbio.github.io/">2023 ICML Workshop on Computational Biology</a>, and csv files with the split between train, test and validation across the <a href="https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab/">SAbDab</a> and <a href="https://zenodo.org/record/7258553">ImmuneBuilder</a> datasets.</p><p>This model is based on <a href="https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1">ProteinMPNN</a> and can be run using the corresponding code: <a href="https://github.com/dauparas/ProteinMPNN">https://github.com/dauparas/ProteinMPNN</a>.</p>
Dataset for Peptide binder design with inverse folding and protein structure prediction
<p>Dataset for a paper on peptide design</p> <p> </p> <p><br> mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1)<br> pdb_peptide - variation in the number of recycles (1-10) for 96 peptides (Figure 1)<br> minibinder - results for the minibinder set (Figure 2)<br> Pfam - results for the Pfam set (Figures 4+5)<br> protein_mpnn - results on protein_mpnn test set (Figure 6)</p> <p> </p> <p> </p>
Inverse design of pore wall chemistry to control solute transport and selectivity
Open the record for dataset details and reuse information.
Inverse design of soft materials via a deep-learning-based evolutionary strategy
Open the record for dataset details and reuse information.
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Generation Results
Open the record for dataset details and reuse information.
A Deep Learning Approach to the Forward Prediction and Inverse Design of Plasmonic Metasurface Structural Color - Raw Data
<p>Reflection spectra of PDMS - Al nanorod metamaterials were collected using LUMERICAL FDTD simulations. PDMS material properties were defined using a refractive index of 1.41 and Al material properties were defined using frequency selective permittivities from the handbook of Palik. A total of 4620 structures were simulated, sweeping the following dimension parameters:</p> <ul> <li>Aluminium thickness (t)</li> <li>Pillar height (h)</li> <li>Pillar diameter (d)</li> </ul> <p>Reflectance spectra were converted into CIE 1931 chromaticity values (x,y). This dataset is comprehensive and allows for the development of deep learning models for the forward and inverse design of the given metamaterial structure as detailed in the associated manuscript. The associated manuscript and supporting documentation provide extensive details of data collection and processing methods.</p> <p> </p>
Supporting Data for "Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties" Manuscript
<p>Training data and xTB calculated properties of sampled structures from the generative active learning experiments reported in the manuscript.</p>
Inverse design of multishape metamaterials
<p>This dataset contains all data as used for the paper 'Inverse design of multishape metamaterials'.</p> <p> </p> <p><strong>Abstract:</strong></p> <p>Multishape metamaterials exhibit more than one target shape change, e.g. the same metamaterial can have either a positive or negative Poisson’s ratio. So far, multishape metamaterials have mostly been obtained by trial-and-error. The inverse design of multiple target deformations in such multishape metamaterials remains a largely open problem. Here, we demonstrate that it is possible to design metamaterials with multiple deformations of arbitrary complexity. To this end, we introduce a novel sequential nonlinear method to design multiple target modes. We start by iteratively adding local constraints that match a first specific target mode; we then continue from the obtained geometry by iteratively adding local constraints that match a second target mode; and so on. We apply this sequential method to design up to 3 modes with complex shapes and we show that this method yields at least an 85% success rate. Yet we find that these metamaterials invariably host additional spurious modes, whose number grows with the number of target modes and their complexity, as well as the system size. Our results highlight an inherent trade-off between design freedom and design constraints and pave the way towards multi-functional materials and devices.</p>
Impact of noise on inverse design: The case of NMR spectra matching
<p>Code and data for the corresponding preprint:</p> <p>Impact of noise on inverse design: The case of NMR spectra matching<br> Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld</p> <p>https://doi.org/10.48550/arXiv.2307.03969</p> <p><br> SI_DFT_geo.xyz and SI_DFT_NMR.txt belong to the QM9NMR paper<br> Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules<br> Amit Gupta, Sabyasachi Chakraborty and Raghunathan Ramakrishnan<br> Mach. Learn.: Sci. Technol. 2 (2021) 035010</p> <p>originally hosted here:<br> https://moldis-group.github.io/qm9nmr/<br> https://doi.org/10.17172/NOMAD/2021.10.16-1</p>
Dataset for the figures in the paper "Photonic bands, superchirality, and inverse design of a chiral minimal metasurface", DOI 10.1515/nanoph-2019-0321
<p>This folder contains the raw data from which the graphs in paper "Photonic bands, superchirality, and inverse design of a chiral minimal metasurface", https://doi.org/10.1515/nanoph-2019-0321 have been obtained.</p> <p> </p>
Optimal Experimental Design for Large-Scale Inverse Problems via Multi-PDE-constrained Optimization - supplementary files
<p>The file data.xlsx contains information about four experiments defined in: A. Petrocchi, M.K. Scharrer, F. Pichler, S. Volkwein, Optimal Experimental Design for Large-Scale Inverse Problems via Multi-PDE-constrained Optimization, 2024, Submitted. Preprint available at https://arxiv.org/abs/2404.15797.</p> <p>Some information is included in the file data.pdf.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.