Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy
Open accessAbstract
Computational statistical disciplines, such as machine learning, are leading to a paradigm shift in the way we conceive the design of new compounds, offering a way to directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure–property maps. Here, we show that machine learning offers such a possibility by designing a model that predicts both the energy and magnetic properties as a function of the molecular structure of a single-ion magnet. This model is then used to explore the molecular conformational landscapes in search of structures that maximize magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a ∼50% increase in the anisotropy. This approach can be applied to any structure–property relation and paves the way for a machine-learning-driven optimization of chemical compounds.
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Low-cost, topological, three-dimensional, one-hot chemical encoding, and geometric descriptors are often employed for this purpose in transition-metal chemistry.
“Low-cost, topological (5, 7, 32, 92-94), three-dimensional (95-100), one-hot chemical encoding (92, 101), and geometric (e.g., for MOF pores) (102-104) descriptors are often employed for this purpose in transition-metal chemistry.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [97]
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- Authors
- Lunghi, A., & Sanvito, S.
- Affiliations
- Trinity College Dublin (IE)
- Corresponding author
- Lunghi, Alessandro
- Venue
- The Journal of Physical Chemistry C
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-02-13
- Volume
- 124
- Issue
- 10
- Pages
- 5802-5806
- ISSN
- 1932-7447 (ISSN-L)
- 1932-7455
- OpenAlex
- W3006100765
- Cited by
- 35
- References
- 30
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 6 · 2020: 7
- FWCI
- 4.31
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- The Journal of Physical Chemistry C (publishedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion, cc-by-nc-sa)
- Funders
- Science Foundation Ireland (14/IA/2624)
- Topics
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Magnetism in coordination complexes
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- single-ion magnets, magnetic anisotropy, conformational landscape, structure-property relationships, machine learning, reverse engineering, molecular energy prediction, compound design
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