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Found 11 papers, 6 papers with code
An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations
Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Microstructural materials design is one of the most important applications of inverse modeling in materials science.
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run.
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques.
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening
In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets.
CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations
SMILES is a linear representation of chemical structures which encodes the connection table, and the stereochemistry of a molecule as a line of text with a grammar structure denoting atoms, bonds, rings and chains, and this information can be used to predict chemical properties.
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMS
Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics.
A New Parallel Algorithm for Two-Pass Connected Component Labeling
In the second phase, we assign the final labels to the pixels.
An exploration of parameter redundancy in deep networks with circulant projections
We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.
Temporal Sequence Modeling for Video Event Detection
Based on this idea, we represent a video by a sequence of visual words learnt from the video, and apply the Sequence Memoizer [21] to capture long-range dependencies in a temporal context in the visual sequence.
