The Exascale is enabled by a new generation of heterogenous high-performance computers. Consequently, many simulation codes and methods traditionally used in computational materials science need to be rewritten or replaced by specialized variants to fully leverage the upcoming computing resources. This is challenging for the community as many of the established simulation codes have developed whole ecosystem of tools, making it difficult for newly developed codes to compete or for scientists to switch simulations codes.
pyiron addresses these challenges by implementing an abstraction layer which allows combining simulation codes and tools like building blocks. These building blocks, named pyiron objects, combine (1) an interface to the job management, with (2) the data storage and (3) an interactive jupyter notebook-based user-interface. For material scientists this enables rapid prototyping of simulation protocols, up-scaling of parameter studies and sharing of their workflows. In analogy pyiron provides mathematicians with easy access to existing workflows in materials science, allowing one to benchmark new methods or accelerate these workflows with new approaches.
pyiron was initially developed at the Max-Planck Institut für Eisenforschung, and, following its open-source release in 2019, it has been used by a growing community to address a wide-range of challenges, ranging from ab-initio thermodynamics, to teaching computational chemistry, and more recently at Los Alamos National Laboratory, to actinide chemistry, fitting machine learning interatomic potentials for materials under extreme conditions and prototyping workflows for the Exascale. In this talk, I will discuss (1) how the interface method - to calculate melting temperatures for interatomic potentials - can be implemented in pyiron, (2) how it is up-scaled to iterate over the NIST database of interatomic potentials and (3) how we derived a coarse grained model to predict melting temperatures based on this parameter study.
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