ContactAngleCalculator: An Automated, Parametrized, and Flexible Code for Contact Angle Estimation in Visual...

Area of Science:

• Computational chemistry
• Materials science
• Surface science

Background:

• Accurate contact angle estimation is vital for characterizing surface wettability in molecular dynamics (MD) simulations.
• Current methods for contact angle evaluation are often human-intensive, limiting throughput for applications like machine learning (ML).

Purpose of the Study:

• To develop a flexible and automated tool for contact angle estimation in MD simulations.
• To address the need for fast and efficient computational contact angle evaluations, particularly for ML model training.

Main Methods:

• Developed ContactAngleCalculator, an automated tool for contact angle estimation.
• The tool utilizes coarse-graining techniques and concepts of equivalent contact area and volume.
• Enables automated estimation for multiple time points and cases with minimal user intervention.

Main Results:

• The ContactAngleCalculator provides a streamlined and automated approach to contact angle estimation.
• Significantly reduces human labor and time required for computational contact angle analysis.
• Facilitates integration with machine learning workflows for enhanced efficiency.

Conclusions:

• ContactAngleCalculator offers a flexible, automated solution for contact angle estimation in MD simulations.
• The tool is well-suited for ML applications, accelerating the characterization of surface wettability.
• Automating this process is key to enabling large-scale computational studies.