AB Initio Atomistic Thermodynamics and Statistical Mechanics of Surface Properties and Functions

By Karsten Reuter, C. Stampfl, Matthias Scheffler. 2004

141 citations recorded by the source corpus.

Previous and present “academic” research aiming at atomic scale understanding is mainly concerned with the study of individual molecular processes possibly underlying materials science applications. In investigations of crystal growth one would, for example, study the diffusion of adsorbed atoms at surfaces, and in the field of heterogeneous catalysis it is the reaction path of adsorbed species that is analyzed. Appealing properties of an individual process are then frequently discussed in terms of their direct importance for the envisioned material function, or reciprocally, the function of materials is often believed to be understandable by essentially one prominent elementary process only. What is often overlooked in this approach is that in macroscopic systems of technological relevance typically a large number of distinct atomic scale processes take place. Which of them are decisive for observable system properties and functions is then not only determined by the detailed individual properties of each process alone, but in many, if not most cases, also the interplay of all processes, i.e., how they act together, plays a crucial role. For a predictive materials science modeling with microscopic understanding, a description that treats the statistical interplay of a large number of microscopically well-described elementary processes must therefore be applied. Modern electronic structure theory methods such as density-functional theory (DFT) have become a standard tool for the accurate description of the individual atomic and molecular processes.

Subfields: Atmospheric Science, Atomic and Molecular Physics, and Optics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/9d5390da12c290e59f09a5b286db97475e549720

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In Atomic and Molecular Physics, and Optics

In Materials Chemistry

Citations in the corpus

Machine learning for heterogeneous catalyst design and discovery (2018, background): “49 Surface phase diagrams are difﬁcult to obtain by experiment, thus QM modeling is advantageous to predict stable surface structures under reaction conditions.”
Growth of Epitaxial Graphene: Theory and Experiment (2014, background): “KMC simulations are capable of providing a realistic platform for studying system dynamics (kinetics) related to performing activation events, and this is modeled as a sequence of consecutive jumps between free energy minima, in real time [217, 218, 219, 220].”