Data Structures and Workflows for ICME
49
7 Summary
We have presented an overview of the ICME software applications available to a
modern materials researcher, with a focus on simulation and analytics tools that
generate and process field data. In order to properly handle the myriad kinds of
information these tools produce, we have sketched an outline of the requirements
that an ICME workflow manager should aspire to address. We define requirements
on such a tool’s data structure, modularity, data access, and workflow capabilities. As an example of one tool that satisfies these requirements, we showcased
SIMPL, the Spatial Information Management Protocol Library, along with its
most prominent user, the Digital Representation Environment for the Analysis
of Microstructure in 3D, or DREAM.3D. To demonstrate how DREAM.3D can
leverage SIMPL’s features, a case study was outlined that shows how to address a
common problem in ICME workflows: quantitatively relating process simulation to
measure microstructure. This worked example showed how process variables from
a forging simulation of a Ti-6242Si cylinder could be directly coupled with resulting
microstructure characterized using EBSD and BSE imaging.
As a community, materials research and development must still make progress on
developing easily shareable toolsets for analysis, driven by the continuing adoption
of ICME techniques and data-driven methods. Other fields, such as bioinformatics,
have made this transition successfully; the materials community should heed the
lessons learned from these other sister fields and seek to grow tools that foster development on those problem spaces unique to materials. Additionally, teaching the next
generation of materials researchers how to reason through materials problems with
an ICME lens is paramount. Developing robust, standardized, and open tools for the
growing community ensures that the goals of ICME are achievable.
Acknowledgments The authors would like to acknowledge Mike Jackson, for his vision and
programming expertise in enabling the implementation of the SIMPL architecture; Dennis
Dimiduk, for his consistent support and fruitful discussions; Adam Pilchak, for motivating the
demonstrated ICME use case and providing the data and material; Mike Uchic, for providing
characterization support and contribution to the vision of SIMPL; and Chris Woodward, for his
unyielding support in the early stages of designing DREAM.3D.
References
1. National Research Council, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (The National
Academies Press, Washington, DC, 2008). https://doi.org/10.17226/12199.
2. B. Puchala, G. Tarcea, E.A. Marquis, M. Hedstrom, H.V. Jagadish, J.E. Allison, The materials
commons: a collaborative platform and information repository for the global materials
community. JOM 68(8), 2035–2044 (2016). https://doi.org/10.1007/s11837-016-1998-7
3. B. Blaiszik, K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, I. Foster, The materials data
facility: data services to advance materials science research. JOM 68(8), 2045–2052 (2016).
https://doi.org/10.1007/s11837-016-2001-3
49
7 Summary
We have presented an overview of the ICME software applications available to a
modern materials researcher, with a focus on simulation and analytics tools that
generate and process field data. In order to properly handle the myriad kinds of
information these tools produce, we have sketched an outline of the requirements
that an ICME workflow manager should aspire to address. We define requirements
on such a tool’s data structure, modularity, data access, and workflow capabilities. As an example of one tool that satisfies these requirements, we showcased
SIMPL, the Spatial Information Management Protocol Library, along with its
most prominent user, the Digital Representation Environment for the Analysis
of Microstructure in 3D, or DREAM.3D. To demonstrate how DREAM.3D can
leverage SIMPL’s features, a case study was outlined that shows how to address a
common problem in ICME workflows: quantitatively relating process simulation to
measure microstructure. This worked example showed how process variables from
a forging simulation of a Ti-6242Si cylinder could be directly coupled with resulting
microstructure characterized using EBSD and BSE imaging.
As a community, materials research and development must still make progress on
developing easily shareable toolsets for analysis, driven by the continuing adoption
of ICME techniques and data-driven methods. Other fields, such as bioinformatics,
have made this transition successfully; the materials community should heed the
lessons learned from these other sister fields and seek to grow tools that foster development on those problem spaces unique to materials. Additionally, teaching the next
generation of materials researchers how to reason through materials problems with
an ICME lens is paramount. Developing robust, standardized, and open tools for the
growing community ensures that the goals of ICME are achievable.
Acknowledgments The authors would like to acknowledge Mike Jackson, for his vision and
programming expertise in enabling the implementation of the SIMPL architecture; Dennis
Dimiduk, for his consistent support and fruitful discussions; Adam Pilchak, for motivating the
demonstrated ICME use case and providing the data and material; Mike Uchic, for providing
characterization support and contribution to the vision of SIMPL; and Chris Woodward, for his
unyielding support in the early stages of designing DREAM.3D.
References
1. National Research Council, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (The National
Academies Press, Washington, DC, 2008). https://doi.org/10.17226/12199.
2. B. Puchala, G. Tarcea, E.A. Marquis, M. Hedstrom, H.V. Jagadish, J.E. Allison, The materials
commons: a collaborative platform and information repository for the global materials
community. JOM 68(8), 2035–2044 (2016). https://doi.org/10.1007/s11837-016-1998-7
3. B. Blaiszik, K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, I. Foster, The materials data
facility: data services to advance materials science research. JOM 68(8), 2045–2052 (2016).
https://doi.org/10.1007/s11837-016-2001-3