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Greatest papers with code

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Phys. Rev. Lett. 2017 txie-93/cgcnn

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.

BAND GAP FORMATION ENERGY MATERIALS SCIENCE

Crystal Graph Neural Networks for Data Mining in Materials Science

Technical report, RIMCS LLC 2019 Tony-Y/cgnn

This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.

BAND GAP FORMATION ENERGY MATERIALS SCREENING TOTAL MAGNETIZATION

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

14 Nov 2018soumyasanyal/mt-cgcnn

Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.

BAND GAP FORMATION ENERGY MULTI-TASK LEARNING

Active learning based generative design for the discovery of wide bandgap materials

28 Feb 2021CompRhys/roost

Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model.

ACTIVE LEARNING BAND GAP

Learning Extremal Representations with Deep Archetypal Analysis

3 Feb 2020bmda-unibas/DeepArchetypeAnalysis

The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information.

BAND GAP

Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer

16 Nov 2019tomo835g/Superconductors

Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by demonstrating band gap estimations.

BAND GAP BAND GAP CLASSIFICATION BAND GAP REGRESSION

Band gap prediction for large organic crystal structures with machine learning

30 Oct 2018funkyvoong/band-gaps

Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations.

BAND GAP