With an ever-growing supply of computing power, the computational screening of chemical compounds is spreading its wings outside of the medical field, where its use is very common in drug discovery applications. As technology develops, organic compounds are also introduced to the composition of thin films, and thus the screening of organic molecules has found a new application.
Teksti Mario Mäkinen, kuvat Kalle Kataila ja CSC
The significance of computational methods in the field of chemistry is rising steadily, even though the development has been relatively slow due to the quantum mechanical nature of chemical bonds, which makes the computational requirements rather demanding. However, as the computational power has been increasing rapidly in recent years, the quantum chemistry methods are starting to become viable in a rising number of applications.
Very important and relatively recent phenomena is the emergence of predicting power of experimentally relevant chemical properties, which enables the computational discovery of trends within groups of materials and chemicals.
Currently, I am finalizing my doctoral thesis in the Computational Chemistry group of Professor Kari Laasonen at Aalto University. Our research focuses on gathering trends of different precursors used in the fabrication of hybrid thin films, which can be fabricated using a combination of atomic and molecular layer deposition (ALD/MLD).
The screening of atomic and molecular layer deposition
ALD/MLD is an expansion of the conventional atomic layer deposition (ALD), where the fabricated thin films consist of only a few elements, such as zinc and oxygen. In ALD/MLD, the precursor pool is increased to contain also aliphatic and aromatic organic compounds, so that the thin film can also contain organic components.
For example, a traditional zinc oxide thin films can be fabricated using ALD with diethyl zinc and water as precursors, whereas in ALD/MLD the water is switched to an organic compound such as aliphatic ethylene glycol or aromatic hydroquinone. As the final structure contains both inorganic and organic components, these films are called hybrid thin films.
The ALD/MLD hybrid thin films can have many desirable properties: they can be more flexible, or they can conduct electricity or heat differently than ALD thin films, depending on the organic precursor used in the deposition process.
”Possibilities of new precursor combinations are almost endless.”
The amount of feasible organic compounds is so large, that the possibilities of new precursor combinations are almost endless. As only 1-2 percent of precursors are viable for commercial applications, a computational screening of precursors can accelerate the discovery process significantly, as we can gather trends on the reactivity of different precursors and thus limit the pool of potential precursor candidates.
Our research focuses on looking for trends amongst the ALD/MLD precursors, so we can discover properties that would be preferable in the given application, and the top applicants can then be further researched experimentally. Examples of such properties are the reactivity of a precursor due to its functional groups, or the durability of the hybrid thin film in humid conditions.
The computational screening methods of today and the future
The screening utilizes density functional theory (DFT), and therefore we only need to know the structure of the precursor to screen it. As the use of DFT is computationally demanding, we use the national supercomputers of Finland, i.e. Mahti and Puhti supercomputers of CSC.
In addition to the demands in computing power, software development will be essential to the future of screening. For example, machine learning approaches hold a promise to accelerate the screening process, which would mean a growing number of properties and precursors available to study in the future research with significantly lower computational requirements.
Mario Mäkinen
Mario Mäkinen works as a doctoral researcher in the Computational Chemistry group of Professor Kari Laasonen at Aalto University. Throughout his pursuit of a doctorate, he has been focusing on screening of different materials and surface reactions by utilizing computational methods, of which mostly density functional theory.
Mario Mäkinen
Mario Mäkinen works as a doctoral researcher in the Computational Chemistry group of Professor Kari Laasonen at Aalto University. Throughout his pursuit of a doctorate, he has been focusing on screening of different materials and surface reactions by utilizing computational methods, of which mostly density functional theory.
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