Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu: Materials Genome
Sponsored by: TMS Functional Materials Division, TMS Structural Materials Division, TMS Materials Processing and Manufacturing Division, TMS: Alloy Phases Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Yu Zhong, Worcester Polytechnic Institute; Richard Otis, Jet Propulsion Laboratory; Bi-Cheng Zhou, University of Virginia; Chelsey Hargather, New Mexico Institute of Mining and Technology; James Saal, Citrine Informatics; Carelyn Campbell, National Institute of Standards and Technology

Tuesday 8:00 AM
March 21, 2023
Room: Sapphire L
Location: Hilton

Session Chair: James Saal, Citrine Informatics


8:00 AM  Invited
The Materials Genome Initiative: James Warren1; 1NIST
    Zi-Kui Liu has a special role in the history and development of the US Materials Genome Initiative (MGI). The MGI has entered its second decade of accelerating the discovery, design, development, and deployment of new materials via the creation of a materials innovation infrastructure. NIST has framed its support for the MGI around the need for a data infrastructure that enables the rapid discovery of existing data and models, the tools to assess and improve the quality of those data, and finally the development of new methods and metrologies based on that data. In November 2021, the MGI has released a new strategic plan, charting a plan for next 10 years of an evolving materials innovation infrastructure, and rallying the materials R&D community to address the pressing grand challenges facing society today.

8:30 AM  Invited
Alloy Design Based on Automated CALPHAD Composition Search and Machine Learning: Alan Luo1; 1The Ohio State University
    The last few decades have seen alloy design approach migrated from experimental exploration to CALPHAD-based computational methods. This talk introduces a Python-based program to automatically search for compositions that can meet user-defined requirements, such as phase constitution, transformation temperatures and phase fractions. This program can be coupled to CALPHAD software to access and calculate all thermodynamic variables for automated composition search. This powerful tool can be applied to conventional and high entropy alloy design. Furthermore, a machine learning algorithm has been developed to design lightweight high entropy alloys with promising strength and ductility.

9:00 AM  Invited
Design of Compositional Pathways for Functionally Graded Materials in Additive Manufacturing: Allison Beese1; 1Pennsylvania State University
    Additive manufacturing (AM) can be used to fabricate functionally graded materials (FGMs) through the selective deposition of different powder feedstock compositions in directed energy deposition. Ultimately, this could allow for spatially tailored properties within complex geometry components. However, the combination of liquid-phase mixing of multiple alloys and far from equilibrium processing conditions accessed in AM results in the presence of undesired phases that may result in cracking or resulting in failure-prone regions within the FGM. Additionally, current computational approaches are limited in their prediction of phase transformations due to several factors, including the lack of thermodynamic databases validated across the full compositional space for phase prediction. I will describe the collaborative efforts toward developing a computational-experimental framework for understanding and predicting phase transformation during AM of FGMs, which uses newly developed thermodynamic databases to carry out equilibrium and Scheil solidification simulations, which are then refined and validated using experimental findings.

9:30 AM Break

9:50 AM  Invited
CALPHAD-based ICME Design for Additive Manufacturing of Functionally Graded Alloys: Wei Xiong1; 1University of Pittsburgh
    Functionally graded materials (FGM) can be prepared using directed energy deposition (DED) techniques, enabling multifunctionality in alloy components. In this talk, I will share some experiences printing FGM using different DED techniques guided by ICME. The talk will present some case studies illustrating the advantages of using the CALPHAD-based ICME technique in additive manufacturing (AM) design. In addition, severe challenges to characterization experiments and computational modeling due to the design requirement in AM will be discussed. A case study that integrates the CALPHAD, ICME, machine learning, and high-throughput experiments will be highlighted since it demonstrates how DED is applied as part of the combined approach to alloy development through compositional screening. However, such a study also emphasizes the importance of location-specific design in AM.

10:20 AM  Invited
Thermodynamics of Iodine Terminated MXenes from First-principles Calculations and CALPHAD Modeling: Yong-Jie Hu1; Ervin Rems1; David Bugallo Ferron1; Yury Gogotsi1; 1Drexel University
    The 2D transition metal carbides and nitrides (MXenes) are promising for various energy-related applications due to their uniquely combined electronic, optical, and catalytic properties. The conventional synthetic route of MXenes relies on selective etching using highly concentrated hydrofluoric acid. New synthetic approaches such as vapor etching are being considered not only to overcome safety concerns related to hydrofluoric acid but also to provide the product with more versatile physical and (electro)chemical properties. The phase stability and synthesis thermodynamics of iodine terminated MXenes are theoretically investigated in the present work. A range of iodine terminated MXenes is systematically screened using first-principles quasi-harmonic calculations to predict the thermochemical properties of these compounds. The predicted thermochemical properties are further incorporated with the available thermodynamic database to investigate the thermodynamics associated with the vapor etching reactions and predict the most feasible synthesis conditions using the CALculation of PHAse Diagram (CALPHAD) approach.

10:50 AM  Invited
Big Data-Assisted Digital Twins for the Smart Design and Manufacturing of Advanced Materials: From Atoms to Products: William Yi Wang1; Jinshan Li1; Xingyu Gao2; Feng Sun3; Qinggong Jia3; Bin Tang1; Xi-Dong Hui4; Haifeng Song2; Zi-Kui Liu5; 1Northwestern Polytechnical University; 2Institute of Applied Physics and Computational Mathematics; 3Western Superconducting Technologies Co., Ltd; 4University of Science and Technology Beijing; 5The Pennsylvania State University
    Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven ICME, Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. Our recent works on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment.