Traditionally, sequential trial-and-error has been used to develop new alloy compositions but due to a large combinatorial design space, it quickly becomes infeasible to effectively search through all possibilities in a realistic time span. This obsolete approach can cause two primary problems: 1) Failed attempts can lead to a large number of iterations before a potential candidate is identified, and 2) parts of compositional space are left unexplored leaving out the possibility of a better material candidate.
Incorporating a computational workflow in the Materials R&D process allows for several advantages, some of which are listed as follows:
Unique Insights: With the ability to model a vast design space using ML, Phaseshift is able to provide a level of insight that is simply not obtainable through traditional experimental methods. Similarly, DFT and MD simulations provide a look into material properties before a sample of it can be created and tested in a lab. A combination of these two allows the researchers to make an informed choice, thus reducing the chances of failed experiments and better material selection.
Better Materials: The number of experiments that can be conducted in a designated time span restricts the extent to which a researcher can explore the design space – leaving out potentially better materials in the design space regions that are left unexplored. Phaseshift’s ML enables exploration of a vast design space in reasonable time spans opening up new possibilities. Phaseshift’s process also allows for incorporating processing conditions or use-case-specific information in the workflow making it possible to develop highly optimized materials from the get-go. Performance improvements coupled with cost considerations can enable capturing of a larger market share compared to competing materials.
Reduced R&D times: The ability to make an informed choice increases the chances of success in experimental trials, thus reducing the number of iterations required in the R&D process. In addition to this, the incorporation of ML enables the modeling of material properties much faster than by any other means.
Reduced R&D costs: The majority of the cost of R&D is associated with the time of the researchers involved as well as the cost of materials and equipment. Less number of failed experiments coupled with an expedited R&D timeline results in significant cost reductions.
Get to breakeven faster: Reduced initial investment together with the ability to generate more revenue through highly optimized materials means Phaseshift's clients can get to the breakeven point much faster and start generating profit on their initial investment.