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High-throughput first principles search for new ferroelectrics.

ABSTRACT: We use a combination of symmetry analysis and high-throughput density functional theory calculations to search for new ferroelectric materials. We use two search strategies to identify candidate materials. In the first strategy, we start with non-polar materials and look for unrecognized energy-lowering polar distortions. In the second strategy, we consider polar materials and look for related higher symmetry structures. In both cases, if we find new structures with the correct symmetries that are also close in energy to experimentally known structures, then the material is likely to be switchable in an external electric field, making it a candidate ferroelectric. We find sixteen candidate materials, with variety of properties that are rare in typical ferroelectrics, including large polarization, hyperferroelectricity, antiferroelectricity, and multiferroism.

SUBMITTER: Garrity KF

PROVIDER: S-EPMC6459619 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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High-throughput first principles search for new ferroelectrics.

Garrity Kevin F KF

Physical review. B 20180101

We use a combination of symmetry analysis and high-throughput density functional theory calculations to search for new ferroelectric materials. We use two search strategies to identify candidate materials. In the first strategy, we start with non-polar materials and look for unrecognized energy-lowering polar distortions. In the second strategy, we consider polar materials and look for related higher symmetry structures. In both cases, if we find new structures with the correct symmetries that a ...[more]

PMID: 30984897

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Project description:BackgroundThe functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.ResultsOptimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.ConclusionOur results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.

Dataset Information

High-throughput first principles search for new ferroelectrics.

Publications

High-throughput first principles search for new ferroelectrics.

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