Table Of ContentARTIFICIAL
INTELLIGENCE-AIDED
MATERIALS DESIGN
ARTIFICIAL
INTELLIGENCE-AIDED
MATERIALS DESIGN
AI-Algorithms and Case Studies
on Alloys and Metallurgical
Processes
Rajesh Jha and Bimal Kumar Jha
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First edition published 2022
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Library of Congress Cataloguing-in-Publication Data
Names: Jha, Rajesh, author. | Jha, B. K. (Bimal K.), author.
Title: Artificial intelligence-aided materials design : AI-algorithms and case studies on alloys and
metallurgical processes / Rajesh Jha and Bimal Kumar Jha.
Description: First edition. | Boca Raton, FL : CRC Press, 2022. | Includes bibliographical references and
index. | Summary: "This book describes the application of artificial intelligence (AI)/machine learning
(ML) concepts to develop predictive models that can be used to design alloy materials. Readers new to
AI/ML algorithms can use the book as a starting point and use the included MATLAB and Python
implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers
who want to try new algorithms can use this book and study the case studies for reference. This book is
written for materials scientists and metallurgists interested in the application of AI, ML, and data science
in the development of new materials"– Provided by publisher.
Identifiers: LCCN 2021047601 (print) | LCCN 2021047602 (ebook) | ISBN 9780367765279 (hbk) |
ISBN 9780367765286 (pbk) | ISBN 9781003167372 (ebk)
Subjects: LCSH: Metallurgy–Data processing. | Alloys–Data processing. | Artificial intelligence–
Industrial applications.
Classification: LCC TA483 .J48 2022 (print) | LCC TA483 (ebook) | DDC 620.1/6–dc23/eng/
20211117
LC record available at https://lccn.loc.gov/2021047601
LC ebook record available at https://lccn.loc.gov/2021047602
ISBN: 978-0-367-76527-9 (hbk)
ISBN: 978-0-367-76528-6 (pbk)
ISBN: 978-1-003-16737-2 (ebk)
DOI: 10.1201/9781003167372
Typeset in Times
by MPS Limited, Dehradun
Dedication
Essentially, all models are wrong, but some are useful.
George E. P. Box
A man would do nothing if he waited until he could do it so
well that no one could find fault.
John Henry Newman
The most important thing is to find a problem and start working with the
data in hand. Every algorithm, models and software will have flaws. New
programming languages will be launched, and software will always have
new versions. One cannot wait for the most perfect algorithm that will
help them develop the most perfect model by using the most uniformly
distributed data in the variable space. In the domain of metallurgical
processes and materials design, one has to adapt and work with certain
degree of uncertainty.
“What does your Master teach?” asked a visitor.
“Nothing…” said the disciple.
“Then why does he give discourses?”
“He only points the way; he teaches nothing”
One-minute wisdom
In this book, we have pointed ways by which a person can use the data
generated through experiments, industries, simulations, or from the open
literature and efficiently process it through a set of computational tools. In
the case studies, our emphasis was on analyzing patterns in the data set
along with developing accurate predictive models. We have presented
case studies where we developed in-house code, used proprietary
software, and performed calculations on a supercomputer. We have also
listed a number of ways through which a user can perform lots of those
complex tasks without having to write a single line of code, by working on
a normal laptop/desktop, on a web browser, on a normal Android phone.
The content of this book is in accordance with the Industry 4.0 and
MGI/ICME initiatives and will be helpful in realizing the “Virtual
Materials Design” paradigm.
Contents
Foreword.................................................................................................................xix
Preface.....................................................................................................................xxi
Acknowledgments..................................................................................................xxv
Authors Biographies............................................................................................xxvii
Chapter 1 Introduction: Artificial Intelligence and Materials Design................1
1.1 Data-Driven Materials Science: Initiatives, Goals and
Progress.....................................................................................1
1.2 Multiscale-Materials Modeling................................................2
1.2.1 Initiatives in the United States....................................3
1.2.2 The Integrated Computational Materials
Engineering Expert Group (ICMEG)..........................4
1.3 Virtual Materials Design (VMD).............................................4
1.4 Challenges (MGI/ICME/VMD)................................................5
1.4.1 Potential Solution through Data Science/Artificial
Intelligence..................................................................5
1.5 This book and Its Contribution Towards MGI/ICME and
Virtual Materials Design (VMD).............................................6
1.6 Aim and Scope of Current Book.............................................6
1.6.1 Discovering and Optimizing Chemistry/Processing-
Structure-Property (C/PSP) Linkages.........................6
1.6.1.1 State of the Art............................................6
1.6.1.2 Challenge......................................................7
1.6.1.3 Research Plan...............................................7
1.6.2 Data Science/Artificial Intelligence Techniques
for Accelerating the Discovery and Deployment
of New Materials.........................................................7
1.6.2.1 State of the Art............................................7
1.6.2.2 Challenge......................................................7
1.6.2.3 Research Plan...............................................8
1.6.3 Data from Production: Industrial Furnaces and
Rolling Mill.................................................................8
1.6.3.1 Iron-Making Blast Furnace..........................8
1.6.3.2 Basic Oxygen Furnace (BOF) or LD
(Linz Donawitz) Steelmaking Furnace.......8
1.6.3.3 Rolling Mill..................................................8
1.6.4 Nano Mechanics and Atomic Force Microscope
(AFM) Imaging...........................................................9
1.6.5 Thermo-Mechanical Treatment...................................9
vii
viii Contents
1.6.6 Phase Field..................................................................9
1.6.7 Atomistic Simulations.................................................9
1.6.7.1 Vienna Atomistic Simulation Package
(VASP).........................................................9
1.6.7.2 Schrodinger Materials Design Suite............9
1.6.8 Efficient Way of Choosing a Supervised
Machine Learning Algorithm....................................10
1.6.8.1 State of the Art..........................................10
1.6.8.2 Challenge....................................................10
1.6.8.3 Research Plan.............................................10
1.6.9 CALPHAD Approach...............................................11
1.7 Case Study #1: Uncertainty Quantification and
Propagation Studied Under the Framework of CALPHAD
Approach Using PyCalphad and ESPEI Software................11
1.7.1 Uncertainty Quantification........................................11
1.7.2 Uncertainty Propagation............................................11
1.7.3 Conclusion: Uncertainty Quantification and
Propagation................................................................15
Chapter 2 Metallurgical/Materials Concepts.....................................................17
2.1 Materials/Alloy Design.........................................................17
2.1.1 Effect of Chemical Composition............................17
2.1.2 Effect of processing................................................17
2.1.3 Effect of Structure...................................................19
2.1.4 Effect of Composition, Processing, and
Structure on Properties (C/P-S-P)...........................20
2.2 Calphad Approach................................................................20
2.2.1 Precipitation Kinetics: Nucleation and Growth
of Grains/Crystals of Desired Phase.......................20
2.2.2 Calphad-Based Software and Database:
Application and Limitations....................................23
2.2.2.1 Thermocalc...............................................23
2.2.2.2 Factsage....................................................23
2.2.2.3 JMatPro®..................................................25
2.2.2.4 PyCalphad/ESPEI.....................................26
2.3 Nanomechanics and Nanotribology.....................................27
2.3.1 Nanoindentation.......................................................27
2.3.2 Atomic Force Microscopy (AFM) Imaging...........28
2.3.3 Modeling and Simulation: Case Study in
this Book..................................................................29
2.4 Magnetism and Magnetic Terminologies............................30
2.4.1 Magnetization (M).................................................30
2.4.2 Magnetic Field (H)................................................30
2.4.3 Magnetic Flux Density..........................................31
2.4.4 Core Loss (P )......................................................31
cv
Contents ix
2.4.5 Coercivity (H )......................................................31
c
2.4.6 Magnetic Anisotropy.............................................32
2.4.7 Magnetostriction (λ).............................................33
s
2.4.8 Permeability (Μ)...................................................33
2.4.9 Curie Temperature.................................................33
2.4.10 Hard Magnets........................................................33
2.4.11 Soft Magnets..........................................................33
2.5 Extractive/Process Metallurgy..............................................34
2.5.1 Blast Furnace Iron Making.....................................35
2.5.1.1 Hot Metal Production..............................35
2.5.1.2 Furnace Variables....................................35
2.5.1.3 Blast Furnace Process Description..........36
2.5.1.4 Productivity..............................................38
2.5.2 Steelmaking.............................................................39
2.5.2.1 Basic Oxygen Furnace (BOF): LD
(Linz Donawitz) Furnace.........................39
2.6 Nickel-Base Superalloys.......................................................41
2.6.1 Critical Phases.........................................................41
2.6.2 Effect of Alloying Elements...................................42
2.7 Aluminum Alloys.................................................................42
2.8 Titanium Alloys....................................................................43
2.8.1 High-Temperature Applications..............................43
2.8.1.1 Phases.......................................................44
2.8.1.2 Titanium Aluminides...............................45
2.8.2 Biomaterials.............................................................46
2.9 Utilizing Data From Other Approaches...............................47
2.9.1 Density Functional Theory/AB-Initio Method.......47
2.9.1.1 VASP........................................................47
2.9.1.2 Schrodinger Materials Design Suite........47
2.10 Case Study # 2: Inverse Determination of Phase-Field
Model Parameters for a Targeted Grain Size Through
AI Algorithms.......................................................................47
2.10.1 Phase-Field Approach...........................................47
2.10.1.1 Background..........................................47
2.10.1.2 Phase-Field Variables Identification
for This Study......................................48
2.10.1.3 Application of AI Algorithms.............49
2.10.1.4 Application of AI-Based Algorithm
on Phase-Field Data.............................49
2.10.1.5 Parallel Coordinate Chart (PCC).........53
2.11 Conclusions...........................................................................53
Chapter 3 Artificial Intelligence Algorithms.....................................................55
3.1 Introduction...........................................................................55
3.2 Data Mining..........................................................................55
