Ritchie, Daniel, Gaultois, Michael W 
ORCID: 0000-0003-2172-2507, Gusev, Vladimir V ORCID: 0000-0002-2815-607X, Kurlin, Vitaliy ORCID: 0000-0001-5328-5351, Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 and Dyer, Matthew S ORCID: 0000-0002-4923-3003
(2025)
Probabilistic Isolation of Crystalline Inorganic Phases
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 65 (24).
pp. 13226-13237.
ISSN 1549-9596, 1549-960X
Abstract
We present Probabilistic Isolation of Crystalline Inorganic Phases (PICIP), a tool to accelerate materials discovery by automating the process of isolating unknown crystalline inorganic phases that have been experimentally detected. PICIP can be used by any lab worker, is well suited to both traditional as well as automated high-throughput exploratory workflows, and is a novel approach to isolating unknown phases based on experimental information from sampled compositions. PICIP infers the composition of an unknown phase in a mixed phase sample from the average composition of the sample and the weighted average composition of the known phases in that sample, relying on experimental phase identification and quantification. We implement a novel algorithm that infers the probability density for the unknown phase over a linear representation of compositional phase space. The accuracy of the suggested target compositions can be increased by systematically combining information from different sampled compositions across multiple experiments. This allows for the effective adoption of an iterative sampling strategy that suggests target compositions that converge to the composition of the unknown phase. The linear representation used for compositional phase space can exploit chemical constraints such as charge neutrality to reduce the dimension of the space, while implicitly ensuring only valid compositions are suggested. Simulated exploration of phase fields shows that after four sequential samples, or two batches of five samples, the median purity of the unknown crystalline phase is above 90%. PICIP’s probabilistic construction makes it robust to moderate levels of experimental error in phase quantification (13 wt %), and allows for the identification of scenarios where there are significant levels of experimental error.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Inorganic Chemicals, Crystallization, Probability, Algorithms |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Physical Sciences Faculty of Science & Engineering > School of Physical Sciences > Chemistry Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Algorithms and Computing Systems |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 08 Dec 2025 15:41 |
| Last Modified: | 16 Jun 2026 20:06 |
| DOI: | 10.1021/acs.jcim.5c02256 |
| Open Access URL: | https://doi.org/10.1021/acs.jcim.5c02256 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3195977 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
Altmetric
Altmetric