Data from: Autonomous synthesis of metastable materials
| dc.contributor.author | Ament, Sebastian | |
| dc.contributor.author | Amsler, Maximilian | |
| dc.contributor.author | Sutherland, Duncan R. | |
| dc.contributor.author | Chang, Ming-Chiang | |
| dc.contributor.author | Guevarra, Dan | |
| dc.contributor.author | Connolly, Aine B. | |
| dc.contributor.author | Gregoire, John M. | |
| dc.contributor.author | Thompson, Michael O. | |
| dc.contributor.author | Gomes, Carla P. | |
| dc.contributor.author | van Dover, R. Bruce | |
| dc.date.accessioned | 2021-08-13T15:27:37Z | |
| dc.date.available | 2021-08-13T15:27:37Z | |
| dc.date.issued | 2021-08-13 | |
| dc.description | Data is distributed under the Open Data Commons Attribution License (ODC-By; https://opendatacommons.org/licenses/by/1-0/). Users may share, use, or modify this data, but attribution to the original authors is required. | |
| dc.description.abstract | 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 be a watershed for materials discovery and development. The mapping of non-equilibrium synthesis phase diagrams has recently been accelerated via high throughput experimentation but still limits materials research because the parameter space is too vast to be exhaustively explored. We demonstrate accelerated synthesis and exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and characterization along with a hierarchy of AI methods that efficiently reveal the structure of processing phase diagrams. SARA designs lateral gradient laser spike annealing (lg-LSA) experiments for parallel materials synthesis and employs optical spectroscopy to rapidly identify phase transitions. Efficient exploration of the multi-dimensional parameter space is achieved with nested active learning (AL) cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments as well as end-to-end uncertainty quantification. With this, and the coordination of AL at multiple scales, SARA embodies AI harnessing complex scientific tasks. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for kinetically stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies such as solid oxide fuel cells. This data supports the research described above. | |
| dc.description.sponsorship | The authors acknowledge the Air Force Office of Scientific Research for support under award FA9550-18-1-0136. This work is based upon research conducted at the Materials Solutions Network at CHESS (MSN-C) which is supported by the Air Force Research Laboratory under award FA8650-19-2-5220, and the National Science Foundation Expeditions under award CCF-1522054. This work was also performed in part at the Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant NNCI-2025233). MA acknowledges support from the Swiss National Science Foundation (project P4P4P2-180669). This research was conducted with support from the Cornell University Center for Advanced Computing. | |
| dc.identifier.doi | https://doi.org/10.7298/h63q-9r54 | |
| dc.identifier.uri | https://hdl.handle.net/1813/104247 | |
| dc.language.iso | en_US | |
| dc.relation.isreferencedby | Autonomous synthesis of metastable materials", 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, arXiv:2101.07385 | |
| dc.subject | Laser spike annealing | |
| dc.subject | Bi2O3 | |
| dc.title | Data from: Autonomous synthesis of metastable materials | |
| dc.type | dataset |