Enrolment options

PHYS-E0510 - Advanced Computational Methods in Physics D, Hybrid course, 3.9.2024-26.11.2024

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

  • Familiar with basic theory and applications of molecular/magnetic dynamics.
  • Familiar with basic theory and applications of density functional theory.
  • Familiar with matrix product states and tensor networks.
  • Understand how to apply basic machine learning approaches in the context of these methods.

Credits: 5

Schedule: 03.09.2024 - 26.11.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jose Lado Villanueva, Adam Foster

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    1. Classical molecular and magnetic dynamics
      1. Basic theory of molecular and magnetic dynamics
      2. Time-dependent simulations and correlation functions
      3. Static and dynamical phase transitions in classical models
      4. Machine learning of static and dynamical phase transitions
      5. Independent work
    3. Density Functional Theory
      1. Basic theory and limitations
      2. Simulations and high-throughput workflow
      3. Neural networks and neural network potentials
      4. Practical fitting of neural network potential and simulation benchmarks
      5. Independent work
    5. Matrix product states and tensor networks
      1. Basic theory of tensor networks and density matrix renormalization algorithm
      2. Simulation of 1D quantum many-body models
      3. Quantum phase transitions and many-body correlators
      4. Machine learning quantum phase transitions
      5. Independent work

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Assessment is based on the exercises only.

Workload

  • valid for whole curriculum period:

    Exercises based on the lecture topics.

DETAILS

Study Material

  • valid for whole curriculum period:

    Full materials available in MyCourses, including lecture recordings, written background material and exercises.

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    4 Quality Education

    5 Gender Equality

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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