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Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
Literature review of existing studies done on predicting with machine learning.
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors.
In addition to the dataset they created, Sommer, T., Willa, R., Schmalian, J. et al. in 3DSC developed XGB models to predict Tc.
This paper is well worth reading. It's recent (2023) and does a good job covering prior literature.
This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.
This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, they perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, they apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials.
Published 2024-02-17
The main advantages of the present work over previous ones include: (1) Establishing more appropriate feature space related to superconducting Tc and (2) Identifying the features most related to the Tc values of superconducting materials. We reach significant results by designing the Jabir package to produce 322 atomic features for each compound and Soraya package for selecting features.
https://github.com/txie-93/cgcnn
Implements the Crystal Graph Convolutional Neural Networks (CGCNN) that takes an arbitrary crystal structure to predict material properties.
The following paper describes the details of the CGCNN framework:
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In this post I'll share some of the work I've been doing on a Curie temperature prediction model. I finally found a decent dataset to work with. More on that here:
This post will focus on the methods available to predict/derive of a material. We want to be able to build a pipeline where we can go beyond the available (and experimental) Tc data and train a model
So far this is the most recent paper I've found on ML prediction of , improving on both modeling (CatBoost) and dataset compared to Stanev et al.