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Found 4 papers, 2 papers with code
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells
The discovery of new functional and stable materials is a big challenge due to its complexity.
Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences.
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation.
Ranked #1 on
Graph Classification
on HYDRIDES
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.
