Computational Material Science
Here is a proposed 200-module, year-long graduate or post-graduate level intensive computational materials science curriculum that covers both the theoretical background and ancillary topics necessary to perform high-level research in this field:
Fundamentals of Materials Science (30 modules):
1-5: Crystallography and Structure of Materials
6-10: Thermodynamics and Phase Diagrams
11-15: Defects, Diffusion, and Kinetics
16-20: Mechanical Properties and Deformation Mechanisms
21-25: Electronic, Optical, and Magnetic Properties
26-30: Characterization Techniques (XRD, SEM, TEM, SPM)
Computational Methods and Modeling Techniques (40 modules):
31-35: Density Functional Theory (DFT) and Ab Initio Methods
36-40: Molecular Dynamics (MD) and Monte Carlo (MC) Simulations
41-45: Phase Field and Continuum Modeling
46-50: Finite Element Analysis (FEA) and Multiscale Modeling
51-55: Machine Learning and Data-Driven Approaches
56-60: Uncertainty Quantification and Sensitivity Analysis
61-65: Optimization and Inverse Problem Solving
66-70: Concurrent Coupling of Length and Time Scales
Scientific Computing and High-Performance Computing (HPC) (30 modules):
71-75: Parallel Programming and Scalable Algorithms
76-80: Message Passing Interface (MPI) and OpenMP
81-85: GPU Computing and CUDA Programming
86-90: Workflow Management and Automation Tools
91-95: Data Management, I/O, and Visualization
96-100: Performance Analysis, Profiling, and Optimization
Computational Materials Science Software Packages (40 modules):
101-105: Quantum Espresso and VASP (DFT)
106-110: LAMMPS and GROMACS (MD)
111-115: CALPHAD and Thermo-Calc (Thermodynamics)
116-120: MOOSE and FEniCS (FEA and PDE Solving)
121-125: PyMatGen and Atomic Simulation Environment (ASE)
126-130: AiiDA and Fireworks (Workflow Management)
131-135: MatMiner and PyTorch (Machine Learning for Materials)
136-140: NanoHUB and Materials Project (Databases and Platforms)
Open Source Software Development and Sustainability (20 modules):
141-145: Version Control and Collaborative Development (Git, GitHub)
146-150: Software Testing, Continuous Integration, and Deployment
151-155: Documentation, Tutorials, and User Support
156-160: Licensing, Intellectual Property, and Commercialization
Research Planning and High-Performance Computing Strategies (20 modules):
161-165: Defining Research Objectives and Computational Requirements
166-170: Evaluating and Selecting HPC Resources (Cloud, Cluster, Supercomputer)
171-175: Budgeting, Cost Analysis, and Resource Allocation
176-180: Collaboration, Data Sharing, and Reproducibility
Case Studies and Emerging Trends (10 modules):
181-185: Materials Genome Initiative and Integrated Computational Materials Engineering
186-190: Quantum Computing and Quantum Materials Simulation
Capstone Project and Professional Development (10 modules):
191-195: Independent Research Project and Dissertation
196-200: Scientific Writing, Presentation Skills, and Career Planning
Throughout the course, students will engage in hands-on coding exercises, projects, and case studies that apply computational methods to real-world materials science problems. The curriculum emphasizes the development of a comprehensive skill set that spans from fundamental theory to practical implementation, with a strong focus on the use and development of open source software tools.
By the end of this intensive program, students will have a deep understanding of the principles and practices of computational materials science, as well as the ability to effectively plan, execute, and manage large-scale computational research projects. They will be well-prepared for leadership roles in academia, industry, or government, with the skills and knowledge needed to drive innovation and discovery in this rapidly evolving field.
The course also places a strong emphasis on the importance of open source software development and sustainability, as well as the strategic planning and management of high-performance computing resources. Through a combination of theoretical instruction, practical application, and exposure to cutting-edge research trends, this curriculum provides a comprehensive foundation for success in computational materials science at the highest levels.