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A Bayesian framework for materials knowledge systems
Surya R. Kalidindi
Journal: MRS Communications / Volume 9 / Issue 2 / June 2019
Published online by Cambridge University Press: 07 May 2019, pp. 518-531
This prospective offers a new Bayesian framework that could guide the systematic application of the emerging toolsets of machine learning in the efforts to address two of the central bottlenecks encountered in materials innovation: (i) the capture of core materials knowledge in reduced-order forms that allow one to rapidly explore the vast materials design spaces, and (ii) objective guidance in the selection of experiments or simulations needed to identify the governing physics in the materials phenomena of interest. The framework builds on recent advances in the low-dimensional representation of the statistics describing the material's hierarchical structure.
Solidification in 4D: from Dendrites to Eutectics
Yue Sun, Peter W. Voorhees, A. Shahani, Ahmet Cecen, John W. Gibbs, Surya R. Kalidindi, X. Xiao
Journal: Microscopy and Microanalysis / Volume 23 / Issue S1 / July 2017
Published online by Cambridge University Press: 04 August 2017, pp. 320-321
Role of materials data science and informatics in accelerated materials innovation
Surya R. Kalidindi, David B. Brough, Shengyen Li, Ahmet Cecen, Aleksandr L. Blekh, Faical Yannick P. Congo, Carelyn Campbell
Journal: MRS Bulletin / Volume 41 / Issue 8 / August 2016
Print publication: August 2016
The goal of the Materials Genome Initiative is to substantially reduce the time and cost of materials design and deployment. Achieving this goal requires taking advantage of the recent advances in data and information sciences. This critical need has impelled the emergence of a new discipline, called materials data science and informatics. This emerging new discipline not only has to address the core scientific/technological challenges related to datafication of materials science and engineering, but also, a number of equally important challenges around data-driven transformation of the current culture, practices, and workflows employed for materials innovation. A comprehensive effort that addresses both of these aspects in a synergistic manner is likely to succeed in realizing the vision of scaled-up materials innovation. Key toolsets needed for the successful adoption of materials data science and informatics in materials innovation are identified and discussed in this article. Prototypical examples of emerging novel toolsets and their functionality are described along with select case studies.
The materials innovation ecosystem: A key enabler for the Materials Genome Initiative
David L. McDowell, Surya R. Kalidindi
Journal: MRS Bulletin / Volume 41 / Issue 4 / April 2016
The US Materials Genome Initiative (MGI) has emphasized the need to accelerate the discovery and development of materials to maintain industry competitiveness in new and existing markets. While largely interpreted as an initiative arising from the materials community, it is important to address the coupling of materials with manufacturing and all other relevant aspects of product development in order to maximize its impact. The dual thrusts of Integrated Computational Materials Engineering and the MGI represent a long-term vision of industry, academic, and government stakeholders. The goal is to build a new kind of coupled experimental, computational, and data sciences infrastructure. The emphasis is on high-throughput methods to accelerate historical sequential processes of serendipitous materials discovery and largely empirical materials development by leveraging computation and modern data sciences and analytics. The notion of a materials innovation ecosystem is introduced as the framework in which to pursue acceleration of discovery and development of materials consisting of various elements of data sciences, design optimization, manufacturing scale-up and automation, multiscale modeling, and uncertainty quantification with verification and validation.
Importance of surface preparation on the nano-indentation stress-strain curves measured in metals
Siddhartha Pathak, Dejan Stojakovic, Roger Doherty, Surya R. Kalidindi
Journal: Journal of Materials Research / Volume 24 / Issue 3 / March 2009
Published online by Cambridge University Press: 31 January 2011, pp. 1142-1155
Print publication: March 2009
In this work, we investigated experimentally the various factors influencing the extraction of indentation stress-strain curves from spherical nanoindentation on metal samples using two different tip radii. In particular, we focused on the effects of (i) the surface preparation techniques used, (ii) the presence of a surface oxide layer, and (iii) the occurrence of pop-ins at the elastic-plastic transition on our newly developed data analysis methods for extracting reliable indentation stress-strain curves. Rough mechanical polishing was shown to introduce a large scatter in the measured indentation yield strengths, whereas electropolishing or vibropolishing produced consistent results reflective of the pristine sample. The data analysis techniques used were able to discard the portions of the raw data affected by a thin oxide layer, present on most metal surfaces, and yield reasonable indentation stress-strain curves. Experiments with different indenter tip radii on annealed and cold-worked samples indicated that pop-ins are caused by delayed nucleation of dislocations in the sample under the indenter.
Bond Graphs For Cvd Processes
Surya R. Kalidindi, Seshu B. Desu
Journal: MRS Online Proceedings Library Archive / Volume 168 / 1989
A unified approach to model the general CVD process, utilizing the versatility and flexibility of bond graph methods, is presented. The bond graph representations for important CVD sub-processes are derived. The application of these elements is illustrated through hypothetical examples of CVD processes. It is also shown that several different CVD processes can be handled by a single bond graph computer program.
Contributions of Gas Phase Reactions to CVD Processes
S. B. Desu, Surya R. Kalidindi
An analytical model to account for the effects of gas phase reactions in CVD processes is presented. In this model a system with only two reactions, gas phase production of an intermediate and reaction of the intermediate on the surface of the substrate to form the film is considered. The reaction kinetics, convective and diffusive transport mechanisms are coupled to analyse the thickness distribution over the length of the reactor.
Theoretical deposition rate profiles in the direction of gas flow are shown with varying deposition temperatures and gas flow velocities for both surface and gas phase reaction controlled mechanisms. It has been shown that by analyzing the nature of the deposition rate profiles, the rate limiting step in a CVD process can be identified.