竹内一郎, 2023 - Google Patents
Evolution of combinatorial materials science: From synthesis of large scale libraries to AI-driven materials discovery竹内一郎, 2023
View PDF- Document ID
- 6831373367785052443
- Author
- 竹内一郎
- Publication year
- Publication venue
- 応用物理
External Links
Snippet
With its ability to enable rapid screening of a large number of different materials, the combinatorial highthroughput approach has become an integral part of the experimental toolbox for materials exploration and discovery efforts across virtually all areas of materials …
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shimizu et al. | Autonomous materials synthesis by machine learning and robotics | |
| Koinuma et al. | Combinatorial solid-state chemistry of inorganic materials | |
| Xiang | Combinatorial materials synthesis and screening: an integrated materials chip approach to discovery and optimization of functional materials | |
| CA2431066C (en) | Methods and apparatus for designing high-dimensional combinatorial experiments | |
| Long et al. | Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization | |
| Xing et al. | Rapid construction of Fe–Co–Ni composition-phase map by combinatorial materials chip approach | |
| Long et al. | Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis | |
| Yuan et al. | Recent advances in high-throughput superconductivity research | |
| Kalinin et al. | Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication | |
| Bai et al. | Phase mapper: Accelerating materials discovery with AI | |
| Takeuchi et al. | Data management and visualization of x-ray diffraction spectra from thin film ternary composition spreads | |
| WO2001033585A1 (en) | Synthesis and magnetoresistance test system using double-perovskite samples for preparation of a magnetoresistance device | |
| Wu et al. | Twisted oxide lateral homostructures with conjunction tunability | |
| Butler et al. | Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data | |
| Creange et al. | Towards automating structural discovery in scanning transmission electron microscopy | |
| Xiang et al. | The combinatorial synthesis and evaluation of functional materials | |
| 竹内一郎 | Evolution of combinatorial materials science: From synthesis of large scale libraries to AI-driven materials discovery | |
| Qin et al. | High-throughput research on superconductivity | |
| Yamashita et al. | Direct feature extraction from two-dimensional X-ray diffraction images of semiconductor thin films for fabrication analysis | |
| US6640191B1 (en) | Library design in combinatorial chemistry by Monte Carlo methods | |
| Xiang | Combinatorial materials synthesis and high‐throughput screening: An integrated materials chip approach to mapping phase diagrams and discovery and optimization of functional materials | |
| Yoo et al. | Combinatorial material preparation | |
| Nishio et al. | High-throughput analysis of magnetic phase transition by combining table-top sputtering, photoemission electron microscopy, and Landau theory | |
| Chevrier et al. | Production and visualization of quaternary combinatorial thin films | |
| Zhang et al. | GFNet: A pioneering approach for precisely estimating ash content in coal through the fusion of graph convolution and feedforward network |