3 papers
cs.LG2026
MatBind: A Shared Embedding Space for Multimodal Materials Characterization
Le Yang, Anoop K. Chandran, Jona Östreicher +8
Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural la…
cs.LG2023
A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables
Sébastien Bompas, Stefan Sandfeld
In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Additionally,…
cs.NE2020
Accuracy of neural networks for the simulation of chaotic dynamics: precision of training data vs precision of the algorithm
S. Bompas, B. Georgeot, D. Guéry-Odelin
We explore the influence of precision of the data and the algorithm for the simulation of chaotic dynamics by neural networks techniques. For this purpose, we simulate the Lorenz s…