The CSD-Materials suite now includes a CSD Python API feature based on the FIDEL method, known as AutoFIDEL, which allows for matching and optimizing crystal structures against experimental powder diffraction patterns, without requiring high resolution transmission data and with preferred orientation.
The FIDEL method can compensate for the preferred orientation frequently seen in powder data. It involves optimising the match between the experimental pattern and the simulated pattern of a selected structure, through varying the structure’s lattice parameters. It has proven effective for factors like preferred orientation and sample height displacement.
During this webinar, we will show:
- What is AutoFIDEL and how to access it;
- The process of powder matching and optimization of crystal structures;
- Live demonstration of the process.
Who should attend:
- Solid-state computational chemists
- Data scientists working with chemical data
- Solid form scientists in academia or industry
- Cheminformaticians
The FIDEL method can compensate for the preferred orientation frequently seen in powder data. It involves optimising the match between the experimental pattern and the simulated pattern of a selected structure, through varying the structure’s lattice parameters. It has proven effective for factors like preferred orientation and sample height displacement.
During this webinar, we will show:
- What is AutoFIDEL and how to access it;
- The process of powder matching and optimization of crystal structures;
- Live demonstration of the process.
Who should attend:
- Solid-state computational chemists
- Data scientists working with chemical data
- Solid form scientists in academia or industry
- Cheminformaticians
