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2PP-TestArtifact
(2023)
This repository contains a test artifact (TA), also called test structure, designed for two-photon polymerization (also known as Direct Laser Writing (DLW) or Two/Multi-photon lithography (2PA/MPA)). Test artifacts can be used to compare structures, to check options used by the slicer, check the state of the 2PP machine itself or to get a construction guidelines for a certain combination of power, velocity and settings.
The associated paper can be found here: https://dx.doi.org/10.1088/1361-6501/acc47a
General ideas behind the test artifact:
1. optimized for 2PP-DLW
2. should be fast and easy to analyse with optical microscopy or 3. scanning electron microscopy without tilt.
3. short time to fabricate
4. include a reasonable amount of different features
5. bulk and small structures on the substrate
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.