1 code implementation • 15 Aug 2023 • Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Lan Zhou, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson
X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes.
no code implementations • 21 Aug 2021 • Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra, Lan Zhou, Bart Selman, R. Bruce van Dover, John M. Gregoire, Carla P. Gomes
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
2 code implementations • 4 Jun 2021 • Shufeng Kong, Dan Guevarra, Carla P. Gomes, John M. Gregoire
To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
BIG-bench Machine Learning Generative Adversarial Network +4
no code implementations • 19 Jan 2021 • Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, R. Bruce van Dover
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery.
no code implementations • 25 Sep 2019 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving pattern de-mixing problems, typically in an unsupervised or weakly-supervised setting.
no code implementations • 3 Jun 2019 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
At a high level, DRNets encode a structured latent space of the input data, which is constrained to adhere to prior knowledge by a reasoning module.
no code implementations • 27 Nov 2014 • Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla Gomes, Bart Selman, Robert B. van Dover
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining.