Design of Electroceramic Materials Using Artificial Neural Networks and Multiobjective Evolutionary Algorithms
Abstract
We describe the computational design of electroceramic materials with optimal permittivity for application as electronic components. Given the difficulty of large-scale manufacture and characterization of these materials, including the theoretical prediction of their materials properties by conventional means, our approach is based on a recently established database containing composition and property information for a wide range of ceramic compounds. The electroceramic materials composition-function relationship is encapsulated by an artificial neural network which is used as one of the objectives in a multiobjective evolutionary algorithm. Evolutionary algorithms are stochastic optimization techniques which we employ to search for optimal materials based on chemical composition. The other objectives optimized include the reliability of the neural network prediction and the overall electrostatic charge of the material. The evolutionary algorithm searches for materials which simultaneously have high relative permittivity, minimum overall charge, and good prediction reliability. We find that we are able to predict a range of new electroceramic materials with varying degrees of reliability. In some cases the materials are similar to those contained in the database; in others, completely new materials are predicted.
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Self-citation
- Authors
- Scott, D. J., Manos, S., & Coveney, P. V.
- Affiliations
- University College London (GB)
- Corresponding author
- Coveney, P. V.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-01-25
- Volume
- 48
- Issue
- 2
- Pages
- 262-273
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2013666512
- Cited by
- 20
- References
- 30
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2021: 2 · 2019: 1 · 2018: 1 · 2016: 3 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Imperial College London
- University College London
- Engineering and Physical Sciences Research Council (GR/S85269/01)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Ferroelectric and Piezoelectric Materials
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- materials design, dielectric constant, artificial neural networks, multi-objective evolutionary algorithms, composition-property relationships, static electricity, forecast reliability, materials discovery
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