Uncertainty Quantification for In Silico Chemistry
Tom Frömbgen1, Elizaveta Surzhikova2, Jürgen Dölz3
1Mulliken Center for Theoretical Chemistry, Clausius-Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstraße 4, 53115 Bonn, Germany.
Chemical Reviews
|March 4, 2026
Summary
Uncertainty quantification (UQ) is becoming crucial for in silico chemistry. This review establishes a common language and surveys UQ methods to improve the reliability of computational chemistry predictions.
Area of Science:
- Computational Chemistry
- Data Science
Background:
- Worldwide computing power has advanced in silico chemistry, enabling property and process predictions.
- Abundant data from quantum chemistry, molecular dynamics, and machine learning necessitate robust error and uncertainty assessment.
- Uncertainty quantification (UQ) provides mathematical frameworks to address accuracy, precision, and reliability in computational chemistry.
Purpose of the Study:
- To establish a common language for UQ in the context of in silico chemistry.
- To introduce key mathematical formalisms for UQ.
- To survey the application of UQ across various in silico chemistry domains.
Main Methods:
- Literature review of uncertainty quantification in computational chemistry.
- Explanation of mathematical frameworks relevant to UQ.
- Categorization of UQ applications in different areas of in silico chemistry.
Main Results:
- A unified perspective on UQ for in silico chemistry is presented.
- Key mathematical approaches for quantifying uncertainty are detailed.
- A comprehensive overview of current UQ applications in computational chemistry is provided.
Conclusions:
- UQ is essential for enhancing the trustworthiness of in silico chemistry predictions.
- Standardized language and methods for UQ will accelerate its adoption.
- The integration of UQ offers deeper insights into chemical phenomena and improves decision-making in research.
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