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Found 6 papers, 5 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.
Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection
On-Shelf Availability (OSA) of products in retail stores is a critical business criterion in the fast moving consumer goods and retails sector.
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.
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.
