Publications

Displaying 1 - 14 of 14

A Roadmap for LIMS at NIST Material Measurement Laboratory

April 11, 2022

Author(s)

Gretchen Greene, Jared Ragland, Zachary Trautt, June W. Lau, Raymond Plante, Joshua Taillon, Adam Abel Creuziger, Chandler A. Becker, Joe Bennett, Niksa Blonder, Lisa Borsuk, Carelyn E. Campbell, Adam Friss, Lucas Hale, Michael Halter, Robert Hanisch, Gary R. Hardin, Lyle E. Levine, Samantha Maragh, Sierra Miller, Chris Muzny, Marcus William Newrock, John Perkins, Anne L. Plant, Bruce D. Ravel, David J. Ross, John Henry J. Scott, Christopher Szakal, Alessandro Tona, Peter Vallone

Instrumentation generates data faster and in higher quantity than ever before, and interlaboratory research is in historic demand domestically and internationally to stimulate economic innovation. Strategic mission needs of the NIST Material Measurement

Towards improved FAIRness of the ThermoML Archive

February 28, 2022

Author(s)

Demian Riccardi, Zachary Trautt, Ala Bazyleva, Eugene Paulechka, Vladimir Diky, Joe W. Magee, Andrei F. Kazakov, Scott Townsend, Chris Muzny

The ThermoML archive is a subset of Thermodynamics Research Center (TRC) data holdings corresponding to cooperation between NIST TRC and five journals: Journal of Chemical Engineering and Data (ISSN: 0021-9568), The Journal of Chemical Thermodynamics (ISSN

FAIR Digital Object Demonstrators 2021

January 18, 2022

Author(s)

Peter Wittenburg, Ivonne Anders, Christophe Blanchi, Merret Buurman, Carole Goble, Jonas Grieb, Alex Hardisty, Sharif Islam, Thomas Jejkal, Tibor Kalman, Christine Kirkpatrick, Laurence Lannom, Thomas Lauer, Giridhar Manepalli, Karsten Peters-von Gehlen, Andreas Pfeil, Robert Quick, Mark van de Sanden, Ulrich Schwardmann, Stian Soiland-Reyes, Rainer Stotzka, Zachary Trautt, Dieter Van Uytvanck, Claus Weiland, Philipp Wieder

This paper gives a summary of implementation activities in the realm of FAIR Digital Objects (FDO). It gives an idea which software components are robust and used for many years, which components are comparatively new and are being tested out in pilot

Materials Data: a Landscape Analysis and Potential Roadmap for the NIST Material Measurement Laboratory

April 1, 2021

Author(s)

Debra L. Kaiser, Robert Hanisch, James A. Warren, Zachary Trautt

In the past decade, numerous public and private sector documents have highlighted the need for materials data to facilitate advanced technologies in myriad industrial and economic sectors. These documents have been analyzed to identify prevalent gaps in

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

November 12, 2020

Author(s)

Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

July 14, 2020

Author(s)

Brian L. DeCost, Jason R. Hattrick-Simpers, Zachary T. Trautt, Aaron G. Kusne, Martin L. Green, Eva Campo

Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific

An Inter-Laboratory Comparative High Throughput Experimental Materials Study of Zn-Sn-Ti-O Thin Films

March 19, 2019

Author(s)

Jason R. Hattrick-Simpers, Zachary T. Trautt, Kamal Choudhary, Aaron G. Kusne, Feng Yi, Martin L. Green, Sara Barron, Andriy Zakutayev, Nam Nguyen, Caleb Phillips, John Perkins, Ichiro Takeuchi, Apurva Mehta

High throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many possible applications. However, there are very few publications on the reproducibility of the HTE results

Evaluating error with atomistic simulations: the effect of potential and calculation methodology on the modeling of lattice and elastic constants.

May 9, 2018

Author(s)

Lucas M. Hale, Zachary T. Trautt, Chandler A. Becker

Atomistic simulations using classical interatomic potentials are powerful investigative tools linking atomic structures to dynamic properties and behaviors. It is well known that different interatomic potentials produce different results, thus making it

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

May 8, 2018

Author(s)

Kamal Choudhary, Qin Zhang, Sugata Chowdhury, Nhan V. Nguyen, Zachary T. Trautt, Marcus W. Newrock, Faical Y. Congo, Andrew C. Reid, Francesca M. Tavazza

We perform high-throughput density functional theory (DFT) calculations for optoelectronic properties (electronic bandgap and frequency dependent dielectric function) using the OptB88vdW functional (OPT) and the Tran-Blaha modified Becke Johnson potential

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

March 31, 2017

Author(s)

Martin L. Green, Joshua B. Martin, Zachary T. Trautt

The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of

An Informatics Infrastructure for the Materials Genome Initiative

July 6, 2016

Author(s)

Alden A. Dima, Sunil K. Bhaskarla, Chandler A. Becker, Mary C. Brady, Carelyn E. Campbell, Philippe J. Dessauw, Robert J. Hanisch, Ursula R. Kattner, Kenneth G. Kroenlein, Adele P. Peskin, Raymond L. Plante, Guillaume Sousa Amaral, Zachary T. Trautt, James A. Warren, Sharief S. Youssef, Sheng Yen Li, Pierre Francois Rigodiat, Marcus W. Newrock

A materials data infrastructure that enables the sharing and transformation of a wide range of materials data is an essential part of achieving the goals of the Materials Genome Initiative. We describe two high-level requirements of such an infrastructure

Facilitating the selection and creation of accurate interatomic potentials with robust tools and characterization

September 18, 2015

Author(s)

Zachary T. Trautt, Chandler A. Becker, Francesca M. Tavazza

The Materials Genome Initiative seeks to significantly decrease the cost and time of development and integration of new materials. Within the domain of atomistic simulations, several roadblocks stand in the way of reaching this goal. While the NIST

Application of Data Science Tools to Quantify and Distinguish between Structures and Models in Molecular Dynamics Datasets

August 3, 2015

Author(s)

Surya R. Kalidindi, Joshua Gomberg, Zachary Trautt, Chandler Becker

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional

Considerations for choosing and using force fields and interatomic potentials in materials science and engineering

October 31, 2013

Author(s)

Chandler A. Becker, Francesca M. Tavazza, Zachary T. Trautt, Robert A. Buarque de Macedo

Atomistic simulations are increasingly important in scientific and engineering application. However, the chosen potential affects the simulation answer, sometimes significantly. Here we give some examples of this dependence and outline a set of

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