Ab Initio Prediction of Vapor Pressure for Diverse Atomic Layer Deposition Precursors.
J Chem Theory Comput
; 20(14): 6144-6151, 2024 Jul 23.
Article
em En
| MEDLINE
| ID: mdl-38991151
ABSTRACT
Understanding the saturated vapor pressure (Pvap) is vital for evaluating atomic layer deposition (ALD) precursors, as it directly influences the ALD temperature window and, by extension, the processability of compounds. The early estimation of vapor pressure ranges is crucial during the initial stages of novel precursor design, reducing the reliance on empirical synthesis or experimentation. However, predicting vapor pressure through computer simulations is often impeded by the scarcity of suitable empirical force fields for molecular dynamics simulations. This challenge is further compounded by the diverse chemical substances and the introduction of new elements into modern ALD processes, necessitating robust force fields that can accommodate metals, organics, and halides. In response, this study introduces a novel approach utilizing a quantum mechanically derived force field for the prediction of vapor pressure across a wide spectrum of potential ALD precursors. This approach enables the creation of system-specific force fields through parametrization based on ab initio calculations for a single molecule. We develop a comprehensive workflow to simulate both liquid and gaseous equilibrium phases, allowing the calculation of vapor pressure across a wide temperature range. Our methodology has been validated with a diverse set of ALD precursors, demonstrating its robustness in predicting Pvap at specified temperatures. The approach yields a Pearson's correlation coefficient (R2) greater than 0.9 on a logarithmic scale and a root-mean-squared deviation in self-solvation-free energies as low as 1.3 kcal mol-1. This innovative workflow, which does not require any prior experimental data, marks a significant advancement in the computer-aided design of novel ALD precursors, paving the way for accelerating developments in technology.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article