Advanced simulation of materials

The subject "Simulations of Advanced material" aims to provide students with advanced computer simulation techniques that allow the characterization of materials at different time scales and size, providing the essential foundations of these and showing various applications of them. In the course some of the most advanced methods of Monte Carlo simulations, molecular dynamics and ab initio (MD and DFT) are presented, overcoming the treatment of introductory courses at the level of a degree in Physics, expanding and complementing the contents taught in other master's subjects such as Computational Physics or Electronic Solid Structure. Special attention is paid to advanced simulation techniques with the previous methods and some of its most recent applications in the field of simulations of materials.

LEARNING OUTCOMES
The learning results are both theoretical and practical in nature, since it is intended that students know not only the theoretical bases of this subject, but also concrete applications to systems of diverse nature. In particular, it is expected that, after completing the subject, students will be able to:
1. Analyze the concepts of computer simulation, especially the time and size scales characteristic of each technique.
2. Apply the basic principles and knowledge of the discipline.
3. Ability to learn autonomously and have an entrepreneurial spirit.
4. Communicate one's own points of view persuasively.
5. Contextualize the state of evolution of the matter in the current historical moment.
6. Handle conventional and kinetic Monte Carlo methods, and know some of their main advanced applications.
7. Understand simulation methods using fully-atomistic and coarsed grained molecular dynamics.
8. Understand the fundamentals and some of the main applications of ab initio simulation techniques (DFT in real and Fourier spaces, ab initio molecular dynamics).
9. Manage bibliographic and documentary resources: databases, navigation, etc.

1. Introduction. Theoretical foundations of simulations.
2. Monte Carlo methods: time dependent Monte Carlo. Quantum Monte Carlo.
3. Molecular dynamics: force fields. Polarizable potentials. Coarse-grained simulation methods and reduced representations.
4. Ab initio simulation using density functional theory (DFT): time-dependent DFT. Simulation of excited states. DFT in real space. Ab initio molecular dynamics.
5. Advanced simulations of materials: state of the art. Nanostructures, nanostructured liquids and biological systems.

Basic:

1. L.M. Varela, H. Montes y T. Méndez, Mecánica Estadística, USC Editora, 2024
2. Teacher's notes of the subject and collections of solved exercises, which will be available to students in the Virtual Campus of the USC.


Complementary:

1. A Guide to Monte Carlo Simulations in Statistical Physics (Cambridge University Press, 2015) D. P. Landau, K. Binder.
2. Molecular Modelling. Principles and Applications (Ed Pearson Education, 2001), Andrew R. Leach
3. Introduction to Computational Chemistry (Ed Wiley), Frank Jensen.
4. Understanding Molecular Simulation. From algorithms to Applications (Ed Academic Press, 2001), Daan Frenkel, Berend Smit.
5. Simulating the Physical World: Hierarchical Modeling from Quantum Mechanics to Fluid Dynamics (Ed. Cambridge University Press, 2007), Herman J. C. Berendsen.
6. Computer Simulation of Liquids (2nd ed), (Ed. Oxford University Press, 2017), Michael Allen & Dominic Tildesley
7. GROMACS Reference Manual. http://www.gromacs.org/Documentation/Manual
8. Density functional theory: a practical introduction (John Wiley & Sons, 2011) Sholl, David, and Janice A. Steckel.
9. Materials modelling using density functional theory: properties and predictions. (Oxford University Press, 2014) Giustino, Feliciano
10. Marques, Miguel AL, et al., eds. Fundamentals of time-dependent density functional theory. Vol. 837. Springer Science & Business Media, 2012.

- -Introducción a Linux y Bash:
-https://computernewage.com/2018/09/16/scripting-linux-introduccion/
-https://www.howtoforge.com/tutorial/linux-shell-scripting-lessons/
-https://linuxconfig.org/bash-scripting-tutorial-for-beginners
- Introducción a Python:
-https://www.python.org/about/gettingstarted/
-https://www.learnpython.org/es/
Visualizadores moleculares:
-http://cheminf.cmbi.ru.nl/molden/
-http://www.cambridgesoft.com/support/ProductHomePage.aspx?KBCatID=112
-http://www.ks.uiuc.edu/Training/Tutorials/vmd-index.html
-http://pymol.sourceforge.net/newman/user/toc.html
-https://avogadro.cc/
-https://pymol.org

COMPETENCES OF BASIC AND GENERAL TYPE

CG01 - Acquire the ability to perform team research work.
CG02 - Be able to analyze and synthesize.
CG03 - Acquire the ability to write texts, articles or scientific reports according to publication standards.
CG04 - Become familiar with the different modalities used to disseminate results and disseminate knowledge in scientific meetings.
CG05 - Apply knowledge to solve complex problems.
CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context
CB7 - That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
CB8 - That students are able to integrate knowledge and face the complexity of making judgements based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgements
CB9 - That students know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialized and non-specialized audiences in a clear and unambiguous way
CB10 - That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.

COMPETENCES OF TRANSVERSAL AND SPECIFIC TYPE

TRANSVERSAL
CT11 - Ability to interpret texts, documentation, reports and academic articles in English, scientific language par excellence.
CT12 - Develop the capacity to make responsible decisions in complex and / or responsible situations.

SPECIFIC
CE13 - To know at an advanced level the main techniques of material simulations: Monte Carlo, molecular dynamics, ab initio molecular dynamics and simulation by density functional theory ...
CE14 - Calculate, by means of simulation methods, electronic, structural and dynamic properties of materials (nanostructures, liquids, macromolecules, etc.).
CE15. - Know the state of the art in simulation of materials using the previous techniques.

WORK IN CLASSROOM:

The program will be developed through lectures and interactive seminary and laboratory classes with and without computer tools. The student will be given all the necessary materials for the study of the subject, as well as for the realization of the work topics proposed by the professors in charge of it. The student will have the corresponding tutoring hours, which may be in person or telematic.

AUTONOMOUS WORK OF THE STUDENTS:

The autonomous work will consist of the study of the theoretical contents and the making of codes.

A course will be activated on the Moodle platform of the Virtual Campus, to which information of interest to students will be uploaded, as well as various teaching materials.


The general methodological guidelines established in the memory of the USC Master in Physics will be followed. The classes will be face-to-face and the distribution of expository and interactive hours follows that specified in the memory of the master.
The tutorials can be face-to-face or online. If they are online, they will require an appointment, which is also recommended in person.

During the course, students will have the opportunity to review and present research articles assigned by the professors (thus covering the CG04, CB9 and CT01 competences of the master's memory), as well as to develop small projects individually or in small teams (CG01 competence), which require the use of specific software and the development of small programs for data analysis (thus covering the CG03, CG05, CB6, CB7, CB8, CT02, CE01, CE02 and CE03 competencies of the * master's memory ). The development of the projects, as well as the oral and/or written presentation of their results will be endorsed by the teachers.

The continuous evaluation will take into account the resolution of collections of problems and tasks as well as the contributions of the students to the discussions that will take place during the presentations of the rest of the students and also during the professors' lectures.

In the first opportunity, the weighted average between continuous assessment (25%) and the presentation of papers (75%) will allow students to pass the subject. The student's grade in the second opportunity will correspond to the grade obtained in the corresponding official exam.

The qualification of "not presented" will be granted in accordance with the provisions of the regulations on the permanence in the Bachelor's and Master's degrees at the University of Santiago.

In cases of fraudulent completion of exercises or tests, the following will apply to the provisions of the "Regulations for evaluating students' academic performance and reviewing grades":

"Article 16. Fraudulent performance of exercises or tests.

The fraudulent performance of any exercise or test required in the evaluation of a subject will imply the qualification of failed in the corresponding call, regardless of the disciplinary process that may be followed against the offending student. It is considered fraudulent, among other things, the realization of plagiarized works or obtained from sources accessible to the public without re-elaboration or reinterpretation and without citations to the authors and the sources ”.

The subject consists of 3 ECTS credits, so the total workload for the student is 75 hours, which are distributed as follows:

- Weekly classroom hours: Approximately 30 hours along the semester.
- Expositive hours: 20 h.
- Interactive hours: 10 h.
- Personal work of the student: 44 hours in the semester.
- Individual study: 24 hours in the semester.
- Individual work: 20 hours in the semester
- Other tasks: 1 hour in the semester.

(1) Participation in the lectures.

(2) Preparation of the marked tasks following the indicated guidelines.

(3) Consultation of doubts in the class or in the tutorials.

(4) Read, study, write and program.