Accurate and Ultrafast Simulation of Molecular Recognition and Assembly on Metal Surfaces in Four Dimensions.
Cheng ZhuSamuel E HoffMiryana HémadiHendrik HeinzPublished in: ACS nano (2023)
Understanding molecular interactions with metal surfaces in high reliability is critical for the development of catalysts, sensors, and therapeutics. Obtaining accurate experimental data for a wide range of surfaces remains a critical bottleneck and quantum-mechanical data remain speculative due to high uncertainties and limitations in scale. We report molecular dynamics simulations of adsorption energies and assembly of organic molecules on elemental metal surfaces using the INTERFACE force field (IFF). The force field-based simulations reach up to 8 times higher accuracy than density functional calculations at a million-fold faster speed, as well as more than 1 order of magnitude higher accuracy than other force fields relative to accurate measurements by single-crystal adsorption calorimetry. Uncertainties of prior computational methods are effectively reduced from on the order of 100% to less than 10% and validated by experimental data from multiple sources. Specifically, we describe the molecular interactions of benzene and naphthalene with even and defective platinum surfaces across a wide range of surface coverage in depth. We discuss molecular-scale influences on the heat of adsorption and clarify the definition of surface coverage. The methods can be applied to 18 metals to accurately predict binding and assembly of organic molecules, ligands, electrolytes, biological molecules, and gases without additional fit parameters.
Keyphrases
- molecular dynamics simulations
- single molecule
- biofilm formation
- molecular dynamics
- electronic health record
- density functional theory
- high resolution
- aqueous solution
- machine learning
- mass spectrometry
- staphylococcus aureus
- human health
- pseudomonas aeruginosa
- cystic fibrosis
- climate change
- drinking water
- solid state
- candida albicans
- quantum dots
- transcription factor