Course content

This course provides an integrated introduction to selected computational approaches commonly employed in materials science and chemical engineering. The course is module-based, with three modules combining lectures, hands-on programming in Python, and the use of professional software. In each module, relevant numerical methods (e.g. Newton-Raphson methods to solve set of non-linear equations, Runge-Kutta methods for solving set of ordinary differential equations, machine learning methods) will be recalled so that students can apply them to solve a particular problem. This will ensure that students not only understand the scientific background but also acquire practical skills in implementing these computational methods across diverse scenarios, ranging from fundamental thermodynamic principles to their industrial extensions, spanning molecular-level analysis to process-scale applications.

Each module consists of a project in which students connect theory with computational practice. Projects include developing their own Python code, using established python libraries (e.g. pycalphad, tensorflow), applying professional software packages (e.g. Thermo-Calc, FactSage), and analyzing results in light of published scientific data. The course emphasizes problem-based and project-driven learning, with assessment based on reports, code, and presentations instead of a final written exam.

Soft skills are also an integrated part of the course: students will get guidance in scientific communication, project leadership, and presentation techniques. This ensures that candidates are not only technically proficient but also able to collaborate effectively and communicate their results in academic and industrial settings.

Tentative Module Overview

• Module 1: Thermodynamics of Alloys - Lectures on ideal, regular, and real solutions; numerical implementation of phase equilibria; programming of binary/ternary phase diagrams in Python, with validation against Thermo-Calc.

• Module 2: Modelling at microscale - Introduction to modelling techniques focused on microscale (as e.g. molecular dynamics, Monte Carlo methods, Ising model, phase field, etc.), relevant theory, algorithms (time integration, pseudo-random number generators, etc.), and simulation methods. Project involves designing and analyzing simulations at microscale using relevant Python-based tools and, where relevant, comparing with results from professional packages.

• Module 3: Machine learning in Chemical Applications - Based on well-established industrial chemical processes, the students should be able to propose modifications to improve the process performance using the provided thermodynamic data, literature and software tools. The project proposes the use of machine learning tools (e.g., PCA, PLS, k-means) and computational thermodynamics to assess the performance of different design and operating conditions, and extend this evaluation to understand their performance at process level.

Learning outcome

At the end of the course, students will have developed an integrated competence in computational materials and chemical engineering, combining theoretical understanding, numerical implementation, and practice in professional software. Specifically, they will be able to:

• Describe and apply core theoretical concepts in thermodynamics of solutions, atomistic modelling, and selected chemical engineering applications, and connect these principles to computational modeling tasks.

• Implement and analyze numerical and machine learning methods to solve scientific problems, including equilibrium calculations, time integration of dynamic systems, and data-driven modeling.

• Develop and validate Python-based codes for solving materials and chemical engineering problems and effectively combine these with established professional software.

• Critically evaluate computational results, comparing outcomes from self-written code, professional software, and published scientific data, and interpret those in a scientifically sound way.

• Reflect on the role and limitations of computational methods in advancing understanding, innovation, and decision-making within materials science and chemical engineering.

• Extending thermodynamic and computational principles to industrial applications, evaluating their implications for process design, optimization, and innovation in real-world engineering contexts.

• Produce professional presentations, communicating methods, results, and interpretations clearly and concisely in the format of research articles and oral project defenses.

• Collaborate effectively in interdisciplinary project teams, demonstrating project management, leadership, and communication skills relevant for both academic and industrial research environments.

Learning methods and activities

• Lectures on theory, numerical methods, and software usage.

• Tutorials in Python and specialized software.

• Professional skills sessions: communication, project management, and teamwork.

• Project work in groups

• Oral presentations and discussions after each module.

Each module involves working on a modelling project, including preparing a presentation, and ends with plenary presentations followed by individual questioning. The total workload is estimated to be about 200 hours (including independent homework).

Specific conditions

Recommended previous knowledge

Basic knowledge of materials science and engineering is recommended. Basic knowledge of thermodynamics and phase diagrams is expected. The course will include 3 modelling projects, which require basic knowledge of and experience with numerical methods as well as Python or a similar programming language.

Course materials

Porter, D. A., Easterling, K. E., & Sherif, M. Y. (2021). Phase transformations in metals and alloys (4th ed.). Boca Raton, FL: CRC Press. ISBN 978-0-367-43034-4

ThermoCalc documentation and tutorials (2025)

FactSage (factsage.com)

Free, M. L. (2022). Hydrometallurgy: Fundamentals and applications (2nd ed.). Springer Cham. https://doi.org/10.1007/978-3-030-88087-3

Habashi, F. (2001). Textbook of Hydrometallurgy. Metallurgie Extractive Quebec.

Information about additional course materials will be given at the beginning of the semester.