Predicting Molecular Laser Properties from First-Principles Using Machine Learning-Based Nuclear Ensemble Approach...

Area of Science:

• Computational Chemistry
• Materials Science
• Quantum Mechanics

Background:

• Accurate prediction of molecular absorption and emission spectra is crucial for laser material design.
• Experimental data can be limited, time-consuming, or resource-intensive to obtain.

Purpose of the Study:

• Develop a numerical framework to simulate laser properties of molecular compounds from first principles.
• Provide a predictive tool for *in-silico* screening of novel laser materials.

Main Methods:

• Integration of quantum mechanical (QM) calculations with thermal sampling and Gaussian Mixture Model-based Nuclear Ensemble Approach (GMM-NEA).
• Extension of GMM-NEA to include spontaneous and stimulated emission for laser modeling input.
• High-level multireference multiconfigurational QM calculations (CASSCF/MS-CASPT2).

Main Results:

• The framework accurately predicts laser properties and shows excellent agreement with experimental data for boron hydrides.
• Identified excited-state absorption as the cause for lack of lasing in Et4-anti-B18H18.
• Demonstrated contrasting laser behaviors in compounds with similar spectral properties.

Conclusions:

• The developed framework offers a powerful tool for *in-silico* prediction and design of laser materials.
• Provides deeper physical insight into the behavior of novel laser compounds.
• Validates the importance of excited-state absorption in laser material performance.