Publications
Selected publications
- Differential guest location by host dynamics enhances propylene/propane separation in a metal-organic framework (Journal article - 2020)
- Chemical control of structure and guest uptake by a conformationally mobile porous material (Journal article - 2019)
- High-throughput screening of metal-organic frameworks for kinetic separation of propane and propene (Journal article - 2020)
2025
Discovering trends in big data: general discussion.
Albornoz, R. V., Antypov, D., Blanke, G., Borges, I., Marulanda Bran, A., Cheung, J., . . . Wu, R. (2025). Discovering trends in big data: general discussion.. Faraday discussions, 256(0), 520-550. doi:10.1039/d4fd90063d
Digital Features of Chemical Elements Extracted from Local Geometries in Crystal Structures
Vasylenko, A., Antypov, D., Schewe, S., Daniels, L. M., Claridge, J. B., Dyer, M. S., & Rosseinsky, M. J. (n.d.). Digital Features of Chemical Elements Extracted from Local Geometries in Crystal Structures. Digital Discovery. doi:10.1039/d4dd00346b
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
Moran, M., Gaultois, M. W., Gusev, V. V., Antypov, D., & Rosseinsky, M. J. (n.d.). Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics. Digital Discovery, 4(3), 790-811. doi:10.1039/d4dd00202d
2024
Recognition and order of multiple sidechains by metal–organic framework enhances the separation of hexane isomers
Markad, D., Kershaw Cook, L. J., Pétuya, R., Yan, Y., Gilford, O., Verma, A., . . . Rosseinsky, M. J. (2024). Recognition and order of multiple sidechains by metal–organic framework enhances the separation of hexane isomers. Angewandte Chemie, 136(50). doi:10.1002/ange.202411960
Accelerating metal–organic framework discovery <i>via</i> synthesisability prediction: the MFD evaluation method for one-class classification models
Zhang, C., Antypov, D., Rosseinsky, M. J., & Dyer, M. S. (n.d.). Accelerating metal–organic framework discovery <i>via</i> synthesisability prediction: the MFD evaluation method for one-class classification models. Digital Discovery, 3(12), 2509-2522. doi:10.1039/d4dd00161c