We know that computational methods can be incredibly powerful tools when it comes to predicting a material's properties and behaviour... But are we using them to their utmost advantage?
We believe the answer to this question is to push beyond one time analysis and utilise statistical, optimization, advanced CAD methodologies, machine learning algorithms and neural networks to predict the best design for any given application.
We developed a set of tools around classical MD/DFT methodologies to allow for true design and optimization of molecular compounds. Our optimization toolkit relies on both topology and parametric optimization methodologies to create the best possible chemical composition and morphology for any given number of objective functions subject to any number of constraints. It allows you to figure the most important variables in your design.
The flexibility of our underlying methodologies means that our optimization and development tools can be applied to a number of applications: electromechanical properties of semiconductors and energy storage applications, organic-inorganic interactions of bio-mechanical applications, molecular printing, and coating depositions. The breadth of the applications is only limited by the material models at your disposal.
Once you have decided on what to make, how will you make it? Our software allows you to accurately simulate manufacturing processes, including deposition, printing, chemical reactions, and self-assembly.
Our software can also be used to perform molecular dynamics studies but now with a great flexibility in defining the models and full pre and post processing suite.
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