Machine learning in crystallography and structural science

Billinge, Simon J. L.; Proffen, Thomas

doi:10.1107/s2053273324000172

Acta Cryst Sect A

2024

DOI: 10.1107/s2053273324000172

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Machine learning in crystallography and structural science

Simon J. L. Billinge,

Thomas Proffen

Abstract: An overview of the virtual collection on machine learning (ML) in crystallography and structural science, as represented in Acta Crystallographica Sections A, B and D, IUCrJ and Journal of Synchrotron Radiation, is presented. Some terms and concepts related to artificial intelligence and machine learning are briefly introduced and described, and a short history of ML in structural science as it appeared in these IUCr journals is given to whet the appetite for the rest of the collection.

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Cited by 4 publications

References 29 publications

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Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning

Riesel,

Mackey,

Nilforoshan

et al. 2024

J. Am. Chem. Soc.

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Powder X-ray diffraction (PXRD) is a cornerstone technique in materials characterization. However, complete structure determination from PXRD patterns alone remains timeconsuming and is often intractable, especially for novel materials. Current machine learning (ML) approaches to PXRD analysis predict only a subset of the total information that comprises a crystal structure. We developed a pioneering generative ML model designed to solve crystal structures from real-world experimental PXRD data. In addition to strong performance on simulated diffraction patterns, we demonstrate full structure solutions over a large set of experimental diffraction patterns. Benchmarking our model, we predicted the structure for 134 experimental patterns from the RRUFF database and thousands of simulated patterns from the Materials Project on which our model achieves state-of-the-art 42 and 67% match rate, respectively. Further, we applied our model to determine the unreported structures of materials such as NaCu 2 P 2 , Ca 2 MnTeO 6 , ZrGe 6 Ni 6 , LuOF, and HoNdV 2 O 8 from the Powder Diffraction File database. We extended this methodology to new materials created in our lab at high pressure with previously unsolved structures and found the new binary compounds Rh 3 Bi, RuBi 2 , and KBi 3 . We expect that our model will open avenues toward materials discovery under conditions which preclude single crystal growth and toward automated materials discovery pipelines, opening the door to new domains of chemistry.

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Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra

Parackal,

Goodall,

Faber

et al. 2024

Phys. Rev. Materials

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The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we present a scheme to resolve powder diffraction data into crystal structures with precise atomic coordinates by screening the space of all possible atomic arrangements, i.e., structural prototypes, including those not previously observed, using a pre-trained machine learning (ML) model. This involves (i) enumerating all possible symmetry-confined ways in which a given composition can be accommodated in a given space group, (ii) ranking the element-assigned prototype representations using energies predicted using the Wyckoff representation regression ML model [Goodall , ], (iii) assigning and perturbing atoms along the degree of freedom allowed by the Wyckoff positions to match the experimental diffraction data, and (iv) validating the thermodynamic stability of the material using density-functional theory. An advantage of the presented method is that it does not rely on a database of previously observed prototypes and is, therefore capable of finding crystal structures with entirely new symmetric arrangements of atoms. We demonstrate the workflow on unidentified x-ray diffraction spectra from the ICDD database and identify a number of stable structures, where a majority turns out to be derivable from known prototypes. However, at least two are found not to be part of our prior structural data sets. Published by the American Physical Society 2024

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Machine learning in crystallography and structural science

Cited by 4 publications

References 29 publications

Do materials have a genome, and if they do, what can be done with it?

Do materials have a genome, and if they do, what can be done with it?

Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning

Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra

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