Determining charge transport regimes in organic molecular crystals: a machine learning framework

Area of Science:

• Materials Science
• Condensed Matter Physics
• Nanotechnology

Background:

• Charge transport in organic molecular crystals (OMCs) dictates nanotechnology advancements.
• Two primary regimes exist: metallic-like (wave-like charge propagation via Bloch-like oscillations) and semiconducting (quasi-particle transport via polaronic states).
• Conditions favoring polaronic states are known, but those enabling Bloch-like oscillations (BOs) require further elucidation.

Purpose of the Study:

• To identify the electronic and structural properties of OMCs that promote either polaronic states or BOs.
• To analyze linear and wave transport properties to understand these charge transport mechanisms.
• To map parameter spaces for polaron vs. BO formation using simulations and machine learning.

Main Methods:

• Semiempirical non-adiabatic dynamical simulations at the picosecond scale.
• Machine learning techniques to map parameter spaces.
• Utilized a general model Hamiltonian tailored for OMCs.

Main Results:

• Increased electronic transfer rates, crystal speed of sound, and metallicity favor BO formation.
• BOs in OMCs can achieve ~2 THz frequencies and current amplitudes up to 3000 |e| ps⁻¹.
• Large polarons predominantly form due to electron-lattice interactions.

Conclusions:

• Identified key factors promoting wave-like (BOs) versus quasi-particle (polarons) charge transport in OMCs.
• Demonstrated potential for high-frequency applications using OMCs exhibiting BOs.
• Highlighted the critical role of electron-lattice interactions in polaron formation.