Nov 18, 2021 · In this work, we present a deep-learning model that can predict the synthesizability of hypothetical crystalline materials. The predictive ...
Based on the presented model, we can accurately classify materials into synthesizable crystals versus crystal anomalies across a broad range of crystal ...
Based on the presented model, we can accurately classify materials into synthesizable crystals versus crystal anomalies across a broad range of crystal ...
Aug 25, 2023 · In this work, we develop a deep learning synthesizability model (SynthNN) that leverages the entire space of synthesized inorganic chemical ...
Nov 29, 2021 · Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals ...
Dec 1, 2021 · Our model achieves accurate formation energy prediction by utilizing skip connections in a deep convolutional network and incorporating ...
Feb 22, 2023 · In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in ...
May 11, 2022 · Images derived from crystal structures help neural network running on Bridges-2 to predict ability to create a given crystal in the real world.
Based on the presented model, we can accurately classify materials into synthesizable crystals versus crystal anomalies across a broad range of crystal ...
Feb 22, 2023 · In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in ...