QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

Area of Science:

• Computational chemistry
• Materials science
• Machine learning

Background:

• Quantum chemical (QC) computations are essential for predicting molecular properties like ionization potential and fluorescence.
• Current QC methods require multi-step, human-guided calculations, hindering efficient database construction and material design.
• Automating these processes is crucial for integrating QC with machine learning in black-box optimization frameworks.

Purpose of the Study:

• To develop a Python library, QCforever, for automating the computation of molecular properties.
• To streamline the process of obtaining observable physical and molecular properties from molecule files.
• To facilitate the use of QC computations within black-box optimization for accelerated material discovery.

Main Methods:

• The study introduces QCforever, a Python library designed for automated molecular property computation.
• The library automates multi-step QC calculations, including ionization potential and fluorescence prediction.
• It processes molecule files to output multi-values for property evaluation, acting as a computational black box.

Main Results:

• QCforever automates the calculation and analysis of molecular properties, simplifying data acquisition.
• The library integrates seamlessly with black-box optimization, enabling efficient exploration of desired molecular properties.
• This automation reduces the human intervention needed for QC computations.

Conclusions:

• QCforever significantly simplifies and automates the process of obtaining molecular properties via QC computations.
• The library facilitates rapid material design and database construction by making QC calculations accessible.
• By integrating QCforever into black-box optimization, researchers can efficiently discover molecules with specific desired properties.