[Course information]
Skill Up NeXT for Matlantis users
Providing a human resource development program in the field of materials development
~Supporting the development of human resources with knowledge of materials chemistry and information science~

PDF version available here>>

Preferred Computational Chemistry, Inc. (Head office: Chiyoda-ku, Tokyo; President and CEO: Daisuke Okanohara; hereinafter referred to as PFCC) will begin offering training support to users of the general-purpose atomic-level simulator Matlantis™, subsidizing half the cost of attending materials informatics (MI) and related basic courses.

PFCC will provide an educational support service to subsidize part of the cost of MI courses offered by Skill Up Next to support Matlantis users who are working to develop MI personnel with knowledge of materials science and information science so that they can use Matlantis efficiently and effectively. By taking this training service, you can learn the basics of MI, such as Python, mathematics, machine learning, and compound analysis. 50% of the course fee will be subsidized from the course slots starting in December 2023.
*Once the capacity of the course support is reached, applications will be closed, so please consider taking the course as soon as possible.

Materials Informatics Course Overview

This program allows students to systematically learn various compound data analyses, such as property prediction and compound structure generation. The technologies used in compound data analysis are diverse, including not only classical machine learning methods but also optimization algorithms and graph neural networks. Students will master these technologies through hands-on activities and practical assignments.
https://www.skillupai.com/materials-informatics/
*Once the capacity of the course support is reached, applications will be closed, so please consider taking the course as soon as possible.

Online information session on MI course training support

We will hold an online information session for those who are interested in taking the MI course and receiving training cost subsidies from PFCC. We will introduce the details of this course and how to use the subsidy, so please read the overview of the event below and apply using the form.

[Event Summary]

Seminar title: Revolutionizing materials research with materials simulation using the latest AI technology
- Use of the high-speed general-purpose atomic level simulator Matlantis and its future -
Date and time: Wednesday, January 24, 2024, 17:00-18:00
Format: Online (held via Zoom)
Content: "Matlantis™," a high-speed general-purpose atomic-level simulator that utilizes AI technologies such as deep learning,
We will explain how it contributes to the discovery of new materials.
Speaker: Yusuke Asano, Senior Manager, Preferred Computational Chemistry, Inc.
Moderator: Ken Mineda, Skill Up AI Consultant, Skill Up Next Inc.
Cost: Free (pre-registration required)

[Contact information for this seminar]
Person in charge: SkillUpNext Inc. Enokido
Email address: k_enokido@skillupai.com
Inquiry form: https://www.skillupai.com/question/

* Applications accepted until 12:00 on Wednesday, January 24, 2024

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Curriculum (partial excerpt)

Chapter 1 Introduction to Materials Informatics
-What is Materials Informatics?
・Open datasets related to materials informatics

Chapter 2 Fundamentals of Compound Analysis
・Tools for handling compound data
・Five topics in total, including how to visualize compound data

Chapter 3 Compound Data Analysis Methods
・Machine learning methods commonly used in materials informatics
・4 topics in total, including model interpretation

Chapter 4: Examples of Compound Data Analysis
・Analysis of compound data
・Analysis of compound data 1 Boiling point
・Analysis of Compound Data 2 Environmental Toxicity
・Analysis of Compound Data 3: Pharmacological Activity (Regression) and 11 other topics

Chapter 5 Graph Neural Networks
・What is a graph?
・3 topics in total, including Graph Neural Networks (GNN)

Chapter 6 Proposal of Experimental Conditions
・Proposal of experimental conditions
・5 topics in total, including experimental design

Chapter 7 Bayesian Optimization
・Type of optimization
- 4 topics in total, including components of Bayesian optimization

Chapter 8: Materials Informatics Case Studies

To attend this course, you will need the following basic knowledge. These multiple courses are also eligible for half-price subsidies.
*Once the capacity of the course support is reached, applications will be closed, so please consider taking the course as soon as possible.
● Basic knowledge of mathematics required for machine learning (equivalent to completing the Mathematics Course for Machine Learning and Deep Learning) https://www.skillupai.com/math/
● Basic knowledge of Python programming (equivalent to completing an introductory course on Python for machine learning)
https://www.skillupai.com/python/
● Basic knowledge of machine learning + implementation experience (equivalent to completing the Basics of Machine Learning and Data Analysis for Use in the Workplace)
https://www.skillupai.com/machine-learning/

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About SkillUpNext

Skill Up Next provides services to support learning in cutting-edge fields and organization building. Since its founding, the company has provided education to over 750 companies, mainly major corporations, and over 80,000 working adults and university students in its DX/AI human resource development program. Recently, the company has also launched a GX (Green Transformation) human resource development program, focusing on supporting companies in promoting GX. In addition, the company has begun human resource development efforts with a focus on cutting-edge fields such as quantum and Web3. In the future, the company will continue to systematize knowledge in cutting-edge fields as quickly as possible to keep up with the ever-accelerating pace of technological innovation and social issues, and contribute to the growth of Japanese companies.
https://skillup-next.co.jp/