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Density functional theory (DFT) and machine learning (ML) accelerate materials discovery. This study quantifies density functional approximation (DFA) bias in transition-metal complexes (TMCs) and develops ML models for reliable property prediction.

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Area of Science:

  • Computational Materials Science
  • Quantum Chemistry
  • Machine Learning in Chemistry

Background:

  • Virtual high-throughput screening (VHTS) accelerates materials discovery using density functional theory (DFT) and machine learning (ML).
  • DFT-based workflows typically use a single density functional approximation (DFA), which can introduce bias, especially for challenging electronic structures like transition-metal complexes (TMCs).
  • Accurate benchmarks for these complex systems are often unavailable, hindering reliable screening.

Purpose of the Study:

  • To quantify the impact of DFA bias on computed properties of over 2000 TMCs.
  • To develop robust ML models that provide DFA-invariant design rules for materials discovery.
  • To improve the accuracy and reliability of VHTS for TMCs by leveraging consensus predictions across multiple DFAs.

Main Methods:

  • Rapidly computed property predictions for over 2000 TMCs using 23 representative DFAs across various families and basis sets.
  • Trained independent ML models for each DFA and analyzed feature importance to identify universal design rules.
  • Developed artificial neural network (ANN) models trained on data from all 23 DFAs to predict TMC properties.

Main Results:

  • Despite variations in computed properties (e.g., spin state splitting, frontier orbital gap) across DFAs, high linear correlations were observed.
  • Convergent trends in ML model feature importance across DFAs provided universal, DFA-invariant design rules.
  • Consensus predictions from ANN models improved the correspondence of computational lead compounds with experimental data compared to single-DFA approaches.

Conclusions:

  • DFA bias can be quantified, and ML models can extract universal design principles from diverse DFT data.
  • A consensus-based ANN approach significantly enhances the reliability of VHTS for discovering novel TMCs.
  • This strategy offers a more accurate and robust pathway for computational materials discovery, particularly for complex systems.