EdX

Introduction to Computational Materials Design (edX)

Offered by Osaka University, OsakaUx,
Introduction to Computational Materials Design (edX)

The purpose of this course is to analyze the present status and possibilities of computational materials design and to implement a new paradigm of material science by learning basic cutting-edge computational methods and designing materials using quantum simulation program codes.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

Material science plays a central role in the development of technical foundations even in the 21st century. The traditional empirical methodology of research alone does not meet the modern requirement for a rapidly changing society to ensure society that is environmentally friendly and resource-conserving. The computational materials design approach is expected to be a breakthrough to overcome these barriers.
Computational materials design refers to the theoretical design and optimization of materials with the desired property and function. It involves the efficient use of computational techniques to simulate materials based on the basic quantum theory.

The purpose of this course is to analyze the present status and possibilities of computational materials design and to implement a new paradigm of material science by learning basic cutting-edge computational methods and exercising materials design using quantum simulation program codes.
This course will focus on the basics of quantum simulations and their application to chemical reactions, semiconductor spintronics, carbon functional nanomaterials, dynamics at surfaces, strongly correlated and superconducting materials, materials informatics, and parallel computing on the world’s fastest supercomputers.
The layout of the course and the presenters of the modules are listed as follows.

  1. Yoshitada Morikawa: Introduction
  2. Yoshitada Morikawa: Design of Chemical Reactions at Interfaces
  3. Kazunori Sato: Design of Magnetic Materials for Spintronics
  4. Koichi Kusakabe: Carbon Functional Materials
  5. Wilson Agerico Dino: Surface/Interface as a Playground/Foundation

for Realizing Designer Materials & Processes

  1. Kazuhiko Kuroki: Strongly Correlated and Superconducting Materials
  2. Tamio Oguchi: Development of Materials Informatics Tools
  3. Masaaki Geshi: Introduction to High-Performance Computing

What you'll learn
You will learn the basics of quantum simulations and their application to chemical reactions, semiconductor spintronics, carbon functional nanomaterials, dynamics at surfaces, strongly correlated and superconducting materials, materials informatics, and parallel computing on the world’s fastest supercomputers.

Syllabus

The course consists of eight lectures and one session on discussion and debate.

  1. Introduction
  2. Design of Chemical Reactions at Interfaces
  3. Design of Magnetic Materials for Spintronics
  4. Carbon Functional Materials
  5. Surface/Interface as a Playground/Foundation for Realizing Designer Materials & Processes
  6. Strongly Correlated and Superconducting Materials
  7. Development of Materials Informatics Tools
  8. Introduction to High-Performance Computing
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Medir y optimizar campañas de marketing en redes sociales (Coursera) Coursera
Facebook

Medir y optimizar campañas de marketing en redes sociales (Coursera)

Este curso te proporcionará las habilidades para optimizar tus acciones de marketing en redes sociales. Aprende a evaluar e interpretar los resultados de tus campañas publicitarias. Aprende a evaluar la efectividad de la publicidad a través de estudios de mejora y optimiza tus campañas con pruebas divididas. Comprende cómo la efectividad de la publicidad se mide en todas las plataformas y dispositivos, aprende a evaluar el ROI de tu actividad de marketing y perfecciona el modo de comunicar tus resultados de marketing en redes sociales a otros en la empresa.

Jan 13th 2025
4 Weeks
Machine Learning Operations 2 (MLOps2-AWS): Data Pipeline Automation & Optimization using Amazon Web Services (AWS) (edX) EdX
Statistics.comX,Statistics.com

Machine Learning Operations 2 (MLOps2-AWS): Data Pipeline Automation & Optimization using Amazon Web Services (AWS) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course - MLOp2s: Data Pipeline Automation & Optimization using Amazon Web Services (AWS).

Self Paced
Self-Paced
Machine Learning Operations 2 (MLOps2-GCP): Data Pipeline Automation & Optimization using Google Cloud Platform (GCP) (edX) EdX
Statistics.comX,Statistics.com

Machine Learning Operations 2 (MLOps2-GCP): Data Pipeline Automation & Optimization using Google Cloud Platform (GCP) (edX)

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOp2s: Data Pipeline Automation & Optimization using Google Cloud Platform (GCP).

Self Paced
Self-Paced
Experimentation for Improvement (Coursera) Coursera
McMaster University

Experimentation for Improvement (Coursera)

We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best? In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system.

Nov 9th 2026
5-12 Weeks
Building Modern Java Applications on AWS (edX) EdX
AWS

Building Modern Java Applications on AWS (edX)

In this course, we will be covering how to build a modern, greenfield serverless backend on AWS. In modern cloud native application development, it’s often times the goal to build out serverlessarchitectures that are scalable, are highly available, and are fully managed. This mean, less operational overhead for you and your business, and more focusing on the applications and business specific projects that differentiate you in your marketplace. In this course, we will be covering how to build a modern, greenfield serverless backend on AWS.

Self PAced
Self-Paced
Cómputo evolutivo (Coursera) Coursera
Universidad Nacional Autónoma de México

Cómputo evolutivo (Coursera)

La computación evolutiva (evolutionary computation, EC), aplica la teoría de la evolución natural y la genética en la adaptación evolutiva de estructuras computacionales, proporcionando un medio alternativo para atacar problemas complejos en diversas áreas, como la ingeniería, economía, química, medicina y, porque no, las artes. Una población de posibles soluciones de un problema dado es análoga a una población de organismos vivos que evolucionan cada generación, al recombinar los mejores individuos de la población y transmitir sus características de dichos individuos padres, a sus descendientes.

Nov 2nd 2026
4 Weeks
Supply Chain Optimization (Coursera) Coursera
University of California, Irvine

Supply Chain Optimization (Coursera)

Optimization is an important piece of an agile supply chain. In this course, we will explore the components of optimization and how to set up an optimization problem in Excel. We will also practice capacity and resource optimization and explore examples of both in the supply chain. Building off of our optimization practice, we will next learn how to use a Monte Carlo simulation to make the least risky decision in uncertain supply chain situations.

Nov 2nd 2026
4 Weeks
Manufacturing Process Control II (edX) EdX
MIT,MITx

Manufacturing Process Control II (edX)

Learn how to control process variation, including methods to design experiments that capture process behavior and understand means to control variability. As part of the Principles of Manufacturing MicroMasters program, this course will build on statistical process control foundations to add process modeling and optimization.Building on formal methods of designed experiments, the course develops highly applicable methods for creating robust processes with optimal quality.

Oct 13th 2026
5-12 Weeks
Calculus for Machine Learning and Data Science (Coursera) Coursera
DeepLearning.AI

Calculus for Machine Learning and Data Science (Coursera)

After completing this course, learners will be able to: analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients; approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods; visually interpret differentiation of different types of functions commonly used in machine learning; perform gradient descent in neural networks with different activation and cost functions.

Nov 2nd 2026
3 Weeks
Reacciones químicas y cálculos estequiométricos (edX) EdX
Universitat Politècnica de València,UPValenciaX

Reacciones químicas y cálculos estequiométricos (edX)

Aprenderás los conceptos básicos de las reacciones químicas y profundizarás en su estudio cuantitativo (estequiometría). Con este curso aprenderás los conceptos básicos relacionados con las reacciones químicas y profundizarás en su estudio desde el punto de vista cuantitativo, es decir su estequiometría. Entenderás el comportamiento de los gases y las disoluciones y aplicarás las leyes que regulan su comportamiento en los procesos químicos en los que participan.

Self Paced
Self-Paced