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Found 7 papers, 5 papers with code
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
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.
Ranked #5 on
Band Gap
on Materials Project
Band Gap
Formation Energy
Materials Science
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges.
Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology.
A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations
We create a public analysis library at https://github. com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models.
Charge density and redox potential of LiNiO2 using ab initio diffusion quantum Monte Carlo
Electronic structure of layered LiNiO2 has been controversial despite numerous theoretical and experimental reports regarding its nature.
Materials Science
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both.
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth.
