research at molecular level
research at molecular level
As an independent, owner-managed company, CreativeQuantum has been offering a wide range of services since 2008 that enable the customer to benefit from the most modern quantum chemical investigations, without being an expert in quantum mechanics and being in possession of the necessary soft- and hardware infrastructure.
We offer a wide range of simulation technologies, which address typical questions arising from the optimization of chemical syntheses or functional materials.
Our interdisciplinary teams of chemists, physicists and quantum chemists and our infrastructure is available to R&D teams of any kind on-demand.
With reaction mechanism analysis, the understanding of the synthesis is significantly increased, thus enabling a knowledge-based optimization of the conversion and the selectivity.
For this, we determine the thermodynamic data for all possible individual reaction steps of the chemical reactions. From these data, we identify the reaction steps that can be optimized and how to avoid side reactions.
We have the ability to determine a vast variety of physical and chemical material properties. In addition to the investigation of single molecules (e.g. redox potentials, HOMO-LUMO levels, NMR shifts, acidity, stability, etc.), we also offer the identification of macroscopic properties based on intermolecular interactions (e.g. density, melting points, glass transition temperatures, miscibility, swelling behavior).
The morphology and dynamics of material mixtures can also be measured.
By investigating systematic changes in reaction conditions, catalysts, functional materials or mixtures of materials we can identify promising new changes even before the cost- and time-intensive synthesis, characterization, testing and instrumental analysis have to be carried out in a laboratory.
Through high-throughput virtual screenings, we are capable of analyzing hundreds of reactions or thousands of materials in multiple dimensions and of determining the interaction between different variations in the same system. Often only the combination of several changes causes the desired effect.
If the variation space is too large to search for an improvement of a reaction or material using a virtual high-throughput screening, we offer an optimization method based on genetic algorithms (or evolutionary algorithms). In this case, a system, as in evolution, is randomly changed and depending on the success of the change, this new property is inherited to further generations of the system.
With the aid of genetic algorithms, systems which allow a great range of variations, can be optimized by natural selection. Within few generations, a global optimum can then be obtained for the selected variational parameters.
Due to the increase in processable data, the identification of a correlation with the reaction or material property can become too complex for humans to achieve reproducible and objective assessments within a reasonable period of time.
With the help of our own developed "chemical intelligence", we can analyze large amounts of data and make predictions about system changes within seconds.