### Course Syllabus

### Titolo

Modelling and simulation of complex macromolecular systems, in the age of machine learning

### Docente(i)

Prof. Pietro Faccioli

### Lingua

English

### Breve descrizione

**COURSE OBJECTIVES:**

The problem of understanding the structural dynamics of macromolecular systems provides an

ideal framework to develop and validate new paradigms for advanced multi-scale computing.

In this short graduate-level course, I will discuss how the integration of advanced statistical

mechanics techniques with machine learning algorithms and (very recently) quantum computing

hardware is opening new research avenues in this field, paving the way to accurately simulating

complex transitions with an unprecedented level of accuracy and resolution.**STRUCTURE OF THE COURSE**

The course consists in a set of 8 two-hour lectures intended to cover concepts in advanced

statistical mechanics and theory of stochastic processes, embedded in the framework of

molecular simulation of rare macromolecular transitions**PREREQUISITES:**

A background in equilibrium statistical mechanics and basic (undergraduate level) quantum

mechanics will be assumed.**SYLLABUS:**

- LECT 1: Macromolecules as complex systems: classical and quantum degrees of freedom,

frustration, metastability, and multi-scale rare event problems. - LECT 2: Stochastic dynamics of macromolecules: Langevin equation and Fokker-Plank

Equation - LECT 3: Renormalizing the Stochastic Dynamics: Markov State modelling
- LECT 4: Stochastic path integrals and their applications to macromolecular systems. Statistical

mechanics of transition paths - LECT 5: Machine learning based enhanced sampling methods.
- LECT 6: Machine learning schemes for data reduction and intrinsic manifold exploration
- LECT 7: Introduction to quantum annealing and QUBO
- LECT 8: Quantum encoding and quantum computing of paradigmatically hard statistical

mechanical problems

### CFU / Ore

2 CFU / 16 hours

### Periodo di erogazione

March

### Title

Modelling and simulation of complex macromolecular systems, in the age of machine learning

### Teacher(s)

Prof. Pietro Faccioli

### Language

English

### Short description

**COURSE OBJECTIVES:**

The problem of understanding the structural dynamics of macromolecular systems provides an

ideal framework to develop and validate new paradigms for advanced multi-scale computing.

In this short graduate-level course, I will discuss how the integration of advanced statistical

mechanics techniques with machine learning algorithms and (very recently) quantum computing

hardware is opening new research avenues in this field, paving the way to accurately simulating

complex transitions with an unprecedented level of accuracy and resolution.**STRUCTURE OF THE COURSE**

The course consists in a set of 8 two-hour lectures intended to cover concepts in advanced

statistical mechanics and theory of stochastic processes, embedded in the framework of

molecular simulation of rare macromolecular transitions**PREREQUISITES:**

A background in equilibrium statistical mechanics and basic (undergraduate level) quantum

mechanics will be assumed.**SYLLABUS:**

- LECT 1: Macromolecules as complex systems: classical and quantum degrees of freedom,

frustration, metastability, and multi-scale rare event problems. - LECT 2: Stochastic dynamics of macromolecules: Langevin equation and Fokker-Plank

Equation - LECT 3: Renormalizing the Stochastic Dynamics: Markov State modelling
- LECT 4: Stochastic path integrals and their applications to macromolecular systems. Statistical

mechanics of transition paths - LECT 5: Machine learning based enhanced sampling methods.
- LECT 6: Machine learning schemes for data reduction and intrinsic manifold exploration
- LECT 7: Introduction to quantum annealing and QUBO
- LECT 8: Quantum encoding and quantum computing of paradigmatically hard statistical

mechanical problems

### CFU / Hours

2 CFU / 16 hours

### Teaching period

March