This series aims to introduce theoretical calculations in simple language to experimental researchers who have not been exposed to computational methods or are interested in learning about them. It will cover basic questions like: What are theoretical calculations? What tools and skills are needed to perform calculations? And explain concepts experimentalists may have heard about, like DFT. The author's knowledge is limited, so please point out any errors or misunderstandings.
Chemistry is an experimental science, with observation and validation being critical for progress. However, experiments alone can no longer meet the needs of chemical research given today's rapid technological advances. The fast iteration of supercomputers, CPUs, GPUs and more has greatly increased computational power and speed. An increasing number of studies are adopting a combined computational and experimental approach, injecting new vitality into chemical research.
For experimental researchers, the development of computational chemistry opens new opportunities. Various software can predict microscopic features of catalytic reactions that are hard to directly observe experimentally, providing possible ways to better understand reaction mechanisms. On one hand, computational results can provide theoretical guidance for experimental design by predicting possible intermediate structures, reaction pathways, electronic configurations and other information hard to directly capture. This allows experiments to achieve more with less effort. On the other hand, experimental observations can validate and refine computational methods, providing empirical evidence to make calculations more accurate and reliable. Theoretical and experimental sciences complement each other and work synergistically to further our understanding of mechanisms and microscopic structures. It's safe to say computational methods have become an important aid for experimental researchers, and proper use of calculations can assist in verifying or guiding exploratory experiments, greatly improving research efficiency and leading to more valuable findings. Experimentalists should thus actively learn computational skills and collaborate closely with theorists to jointly advance scientific discovery.
The software used in theoretical calculations is complex and varied. Different types suit different purposes and needs. Here I briefly introduce several common types of computational chemistry software for catalysis research:
First are first-principles software like VASP, ABINIT, Quantum Espresso etc. Based on quantum mechanics, these handle periodic systems like crystals, surfaces and nanotubes. First-principles calculations start from the beginning by treating systems of atoms as collections of electrons and nuclei, and make maximally "non-empirical" calculations based on fundamental quantum mechanical principles using just a few basic constants to determine system energies, electronic structures and other physical properties. This atomic-level modeling from the ground up can accurately predict fundamental physicochemical properties of materials. First-principles methods are thus important quantum mechanical simulation techniques, providing powerful theoretical support for understanding microscopic electronic structures and qualitatively predicting catalytic reactivity. In materials science, VASP is one of the most prevalent commercial packages used by researchers. Developed by the Hafner group at the University of Vienna, VASP performs electronic structure calculations and quantum mechanics-molecular dynamics simulations, with the full name Vienna Ab-initio Simulation Package. It employs periodic boundary conditions (or supercell models) to calculate geometric properties of materials (like bond lengths, angles, lattice constants), electronic properties (like electron density, band structure, electron density distribution, localized electronic functions), and performance of catalytic materials in thermal, electrochemical and photocatalytic reactions.
Second are quantum chemistry software like Gaussian and ORCA. These are commonly used to model non-periodic systems like molecules and clusters. They can predict molecular structures, energies, vibrational frequencies and properties in different chemical environments, as well as molecular interactions and catalytic performance, by examining molecular orbitals and electron densities. Gaussian is one of the pioneering quantum chemistry packages, first developed in 1970 by John Pople's team at Carnegie Mellon University and later commercialized and promoted by Gaussian Inc. Gaussian has released various versions including Gaussian03, Gaussian09 and Gaussian16, becoming an industry standard and leader.
A key advantage of Gaussian is its implementation of many accurate and efficient electronic structure theories like Hartree-Fock, density functional theory, Møller–Plesset perturbation theory, coupled cluster methods, CCSD etc. It has comprehensive basis set libraries, enabling simulations of diverse systems including small molecules, large molecules and transition metal complexes from the ground up. Gaussian has a broad user base across fields like organic, inorganic, biological and materials chemistry.
As mentioned, both first-principles and quantum chemistry software extensively employ density functional theory (DFT) to describe system electron structures. DFT is an important technique for studying material and molecular electronic structures, whose pioneer Walter Kohn was awarded the 1998 Nobel Prize in Chemistry. DFT substitutes electron density for wavefunctions, directly computing system energies and electronic states from electron density. By approximately treating exchange and correlation energies, DFT solves the Schrödinger equation with computational complexity orders of magnitude lower than direct solutions, allowing applications to larger periodic and non-periodic systems.
Writer: Fang Cong (fangcong@qibebt.ac.cn)
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