（中 文）多场多尺度仿真

Teacher

Prof Yuzheng Guo,  Prof Zhaofu Zhang

First day of classes

7/1

Last day of classes

7/17

 Course Credit

   16

Course Description

Course Introduction

       This course is an undergraduate-level course that   covers the fundamentals of multi-field, multi-scale simulation methods,   including density functional theory (DFT), molecular dynamics (MD), and   finite element method (FEM). The course will also introduce the latest   developments in the application of machine learning (ML) to simulation and   modeling. Students will learn how to apply these methods to solve real-world   problems in science and engineering.

 Objective

· Understand   the principles of DFT, MD, and FEM

· Learn   how to apply these methods to solve problems in materials science and   electrical engineering

· Understand   the basics of machine learning and its application to simulation and modeling

· Develop   skills in programming and simulation using languages such as Python, Fortran,   and MATLAB

· Apply   multi-field, multi-scale simulation methods to solve real-world problems

Assignments (essay or other forms)

On-line programming   and reports.

Text Books and Reading Materials

Textbooks:

1. "Density Functional Theory: A Practical   Introduction" by R. M. Martin and J. M. Soler

2. "Molecular Dynamics Simulations: Elementary   Methods" by M. P. Allen and D. J. Tildesley

3. "The Finite Element Method: A Practical   Course" by T. J. R. Hughes

4. "Machine Learning for Materials Science"   by R. M. Wentzcovitch and J. R. Chelikowsky

5. "Computational Materials Science: From Ab   Initio to Multiscale" by J. R. Chelikowsky and M. I. Baskes

Journal   Articles:

1. "Density Functional Theory: An Overview"   by R. M. Martin, Rev. Mod. Phys. 68, 601 (1996)

2. "Molecular Dynamics Simulations of Materials   Properties" by M. P. Allen and D. J. Tildesley, J. Chem. Phys. 123,   104502 (2005)

3. "Finite Element Methods for Materials   Simulation" by T. J. R. Hughes, Comput. Meth. Appl. Mech. Engrg. 196, 1   (2006)

4. "Machine Learning for Materials Design"   by R. M. Wentzcovitch and J. R. Chelikowsky, NPJ Comput. Mater. 3, 22 (2017)

5. "Multiscale Modeling of Materials" by J.   R. Chelikowsky and M. I. Baskes, J. Phys.: Condens. Matter 29, 343201 (2017)

Online   Resources:

1. The Materials Project: A comprehensive online   resource for materials simulation and analysis

2. The Open Source Molecular Dynamics (OpenMM)   software: A free and open-source molecular dynamics software

3. The Finite Element Method (FEM) tutorial by the   University of California, Berkeley

4. The Machine Learning for Materials Science (MLMS)   workshop by the Materials Research Society

5. The Multiscale Modeling of Materials (MMMS)   workshop by the National Science Foundation

Software and   Tools:

1. NWChem: A free and open-source quantum chemistry   software

2. Quantum ESPRESSO: A free and open-source DFT   software

3. VASP: A free and open-source DFT software

4. OpenMM: A free and open-source molecular dynamics   software

5. COMSOL: A commercial FEM software

6. MATLAB: A commercial programming language and   software

7. Python: A free and open-source programming   language and software