Atomic layer deposition is a precision technique for growing thin films with atomic-level control, where surface reactions between gaseous precursors and functionalized substrates occur in sequential, self-limiting half-reactions. Ab initio molecular dynamics simulations have emerged as a powerful tool for investigating these complex surface processes at the electronic structure level, providing insights that complement experimental observations. By solving the quantum mechanical equations of motion for nuclei and electrons, AIMD captures the dynamic evolution of ALD reactions without relying on predefined reaction coordinates or empirical potentials.

The simulation of precursor adsorption begins with modeling the interaction between the precursor molecule and surface functional groups. For example, in aluminum oxide ALD using trimethylaluminum and water, AIMD reveals the spontaneous formation of a coordinative bond between the aluminum atom and a surface hydroxyl group. The methyl ligands exhibit rapid vibrational motion prior to dissociation, with simulations showing proton transfer from the surface to a methyl group occurring within a few hundred femtoseconds. The adsorption energy and geometry depend critically on the surface coverage of hydroxyl groups, with isolated sites showing stronger binding than those in close proximity.

Ligand exchange processes are central to ALD surface chemistry. AIMD simulations of metalorganic precursors on oxide surfaces demonstrate a concerted mechanism where incoming precursor molecules simultaneously interact with multiple surface sites. For titanium nitride deposition using titanium tetrachloride and ammonia, simulations show that the first chlorine ligand exchanges with a surface hydrogen atom within 0.5 picoseconds, followed by sequential replacement of remaining ligands over several picoseconds. The reaction barriers extracted from the potential energy surface evolution typically range between 0.8 and 1.5 eV for common ALD chemistries, consistent with the experimentally observed temperature windows for efficient growth.

Byproduct desorption dynamics are equally important for completing the ALD cycle. AIMD captures the non-equilibrium process of small molecule liberation from the surface, such as methane or hydrogen chloride formation. The simulations show that byproducts often remain temporarily trapped in physisorbed states before escaping to the gas phase, with residence times on the order of picoseconds at typical ALD temperatures. The desorption energy profiles exhibit strong dependence on surface termination, with hydroxylated surfaces generally promoting faster byproduct release compared to fully oxygen-terminated surfaces.

Electronic structure analysis from AIMD provides atomic-scale explanations for reaction selectivity. Charge density difference plots during zirconium oxide ALD reveal electron accumulation between the zirconium atom of the precursor and surface oxygen atoms, while electron depletion occurs around the leaving organic ligands. The simulations quantify the degree of covalent versus ionic bonding character, which correlates with experimental measurements of growth rates. For platinum ALD using methylcyclopentadienyltrimethylplatinum, the simulations identify a critical charge transfer of approximately 0.3 electrons from the surface to the platinum center as necessary for complete ligand removal.

The application of AIMD to oxide ALD processes has successfully explained many experimental observations. In hafnium oxide deposition, simulations predicted the temperature-dependent transition from ligand exchange to precursor decomposition mechanisms, later confirmed by in situ infrared spectroscopy. For nitride ALD, simulations of silicon nitride growth from dichlorosilane and ammonia revealed the critical role of surface amino groups in facilitating complete chlorine removal, leading to improved process designs. Metal ALD simulations, particularly for copper and ruthenium, have elucidated the reducing agent chemistry responsible for obtaining pure metallic films.

System size limitations remain a challenge for AIMD studies of ALD. Typical simulations contain several hundred atoms, representing only a small portion of an actual deposition surface. This constraint affects the modeling of long-range surface restructuring effects and the statistical sampling of reaction pathways. Timescale limitations restrict most simulations to processes occurring within tens of picoseconds, while some ALD reactions may involve slower steps. These constraints necessitate careful selection of initial conditions and reaction coordinates to ensure physical relevance.

Hybrid quantum mechanics/molecular mechanics approaches have extended the capabilities of ALD simulations. The QM region, treated with density functional theory, typically includes the reacting precursor and nearby surface sites, while the MM region handles the bulk substrate with classical potentials. This partitioning allows simulation of larger surface areas up to several nanometers while maintaining quantum accuracy for the critical reactions. Recent implementations have achieved good agreement with experimental growth rates for aluminum oxide ALD while reducing computational cost by nearly two orders of magnitude compared to full AIMD.

Successful predictions from AIMD include quantitative estimates of growth per cycle for various ALD processes. Simulations of zinc oxide ALD from diethylzinc and water predicted a growth rate of 1.9 angstroms per cycle on hydroxylated surfaces, matching within 10% of experimental measurements. For conformality analysis, simulations of precursor diffusion in high-aspect-ratio structures have explained the observed dependence on precursor size and reactivity, leading to improved precursor design for coating complex geometries.

Comparison with in situ characterization data validates many AIMD predictions. X-ray photoelectron spectroscopy measurements during aluminum oxide ALD show the same intermediate surface species observed in simulations. Quadrupole mass spectrometry data on byproduct evolution matches the temporal profiles calculated from AIMD trajectories. The simulated vibrational spectra of surface intermediates agree with in situ infrared absorption measurements to within 20 wavenumbers for most peaks.

The continued development of AIMD methodologies promises further advances in ALD understanding and optimization. Improved density functionals for van der Waals interactions better describe precursor physisorption, while advanced sampling techniques extend the accessible timescales. Machine learning potentials trained on AIMD data enable larger-scale simulations without sacrificing quantum accuracy. These developments will further strengthen the synergy between computational predictions and experimental ALD research, enabling atomic-scale engineering of thin film materials.