Zobrazeno 1 - 10
of 444
pro vyhledávání: '"Gregoire, John"'
Autor:
Chang, Ming-Chiang, Ament, Sebastian, Amsler, Maximilian, Sutherland, Duncan R., Zhou, Lan, Gregoire, John M., Gomes, Carla P., van Dover, R. Bruce, Thompson, Michael O.
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. Howe
Externí odkaz:
http://arxiv.org/abs/2308.07897
Autor:
Du, Yuanqi, Wang, Yingheng, Huang, Yining, Li, Jianan Canal, Zhu, Yanqiao, Xie, Tian, Duan, Chenru, Gregoire, John M., Gomes, Carla P.
We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machin
Externí odkaz:
http://arxiv.org/abs/2307.05378
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property predictions, while more challenging spectral properties remain less e
Externí odkaz:
http://arxiv.org/abs/2302.01486
Autor:
Kong, Shufeng, Ricci, Francesco, Guevarra, Dan, Neaton, Jeffrey B., Gomes, Carla P., Gregoire, John M.
Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of
Externí odkaz:
http://arxiv.org/abs/2110.11444
Autor:
Chen, Di, Bai, Yiwei, Ament, Sebastian, Zhao, Wenting, Guevarra, Dan, Zhou, Lan, Selman, Bart, van Dover, R. Bruce, Gregoire, John M., Gomes, Carla P.
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems but cannot
Externí odkaz:
http://arxiv.org/abs/2108.09523
Publikováno v:
In Matter 5 June 2024 7(6):2294-2312
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the under
Externí odkaz:
http://arxiv.org/abs/2106.02225
Autor:
Ament, Sebastian, Amsler, Maximilian, Sutherland, Duncan R., Chang, Ming-Chiang, Guevarra, Dan, Connolly, Aine B., Gregoire, John M., Thompson, Michael O., Gomes, Carla P., van Dover, R. Bruce
Publikováno v:
Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams, Science Advances, Vol 7, Issue 5, 2021
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would
Externí odkaz:
http://arxiv.org/abs/2101.07385
Autor:
Sutherland, Duncan R., Connolly, Aine Boyer, Amsler, Maximilian, Chang, Ming-Chiang, Gann, Katie Rose, Gupta, Vidit, Ament, Sebastian, Guevarra, Dan, Gregoire, John M., Gomes, Carla P., van Dover, R. B., Thompson, Michael O.
Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful characterization me
Externí odkaz:
http://arxiv.org/abs/2008.06419
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving complex tasks, typically in an unsupervised or weakly-supervised setting. DRNets exploit problem structure and prior knowled
Externí odkaz:
http://arxiv.org/abs/1906.00855