In the ever-evolving landscape of materials science, a new tool is emerging that could revolutionize the way we design and manufacture advanced materials, particularly for the energy sector. Developed by Alexander M. Richter from the Department of Materials Science and Engineering at The Pennsylvania State University, the AMMap tool is set to streamline the process of additive manufacturing (AM) design, alloy discovery, and path planning.
Additive manufacturing, or 3D printing, has opened up new possibilities for creating complex, high-performance materials. However, the process is not without its challenges. Compositionally complex materials (CCMs), such as functionally graded materials (FGMs), often form undesired phases or cracks during the manufacturing process, negatively impacting the final product. To mitigate these issues, researchers have traditionally relied on empirical models or machine learning algorithms to predict material properties. However, these methods often lack the necessary constraints to ensure manufacturability.
Enter AMMap, an open-source tool that leverages equilibrium thermodynamic calculations and solidification simulations to predict feasible compositions and compositional paths. “AMMap allows us to define and navigate high-order chemical systems of CCMs/FGMs with unprecedented ease,” says Richter. “By using open models and CALPHAD methods for thermodynamic computation, we can automate the path-design process with minimal prior bias.”
The tool utilizes the nimplex library to represent high-dimensional systems as graphs, which can be joined into homogeneous structures and explored with graph traversal algorithms. This innovative approach enables the use of existing high-performance gradient descent, graph traversal search, and other path optimization algorithms to automate the path-design process.
The implications of this research are significant, particularly for the energy sector. The ability to design and manufacture advanced materials with tailored properties could lead to more efficient and reliable energy systems. For instance, the development of high-performance, crack-resistant materials could enhance the durability of wind turbine blades, solar panels, and other energy infrastructure.
Moreover, the automation of the path-design process could significantly reduce the time and cost associated with materials development. “By minimizing the need for trial-and-error experimentation, AMMap can accelerate the discovery and deployment of new materials, ultimately driving innovation in the energy sector,” Richter explains.
Published in the journal JPhys Materials (which stands for Journal of Physics Materials), this research represents a significant step forward in the field of computational materials science. As the energy sector continues to evolve, tools like AMMap will play a crucial role in shaping the materials of the future.
In the words of Richter, “The future of materials design lies in our ability to harness the power of computational tools to navigate the complex chemical landscapes of advanced materials. AMMap is a significant step in that direction.” As we look to the future, the potential applications of this tool are vast, and its impact on the energy sector could be profound.