Blog

We provide easy-to-understand explanations of Matlantis terminology and the latest technology trends from an expert's perspective. We deliver information that will help you solve your problems and make new discoveries.

Editor’s PICK

Introduction to Machine Learning Interatomic Potentials (MLIPs): A Game Changer in Materials Simulation

Yoshitaka Yamauchi Masataka Yamauchi

はじめに 材料科学、化学、そして創薬のR&D現場において、原子・分子レベルのシミュレーションは、物質の性質や反応メカニズムをミクロな視点から解明するための基盤技術として定着しています。しかし、研究開発現場での本格的な活用には、常に「精度」「計算コスト」「汎用性」のトレードオフという壁が立ちはだかっていました。 例えば、第一原理計算は量子

Machine learning force field Explainer

High-Accuracy and High-Speed MOF Calculations with Matlantis - Benchmark Results of  Machine Learning Interatomic Potentials - 

Junichi Ishida Junichi Ishida

Matlantis can calculate a wide range of materials, but among them, metal-organic frameworks (MOFs) are important materials with a wide range of applications, including catalysts and CO2 storage. This material, discovered in the 1990s, has already been industrialized and is attracting attention worldwide as an essential material for maintaining a sustainable society. In fact, the 2025 Nobel Prize

Explainer computational chemistry

[Kyoto Univ. Prof. Kitagawa Wins the Nobel Prize in Chemistry]What is PCP / MOF? Explaining Their Impact and Significance

Hirotaka Yonezawa Hirotaka Yonezawa

Around 7pm on October 8th, 2025, breaking news broke that "Professor Kitagawa of Kyoto University has won the Nobel Prize in Chemistry." The Matlantis User Conference 2025 was being held on the same day, and we were able to celebrate this good news with many Matlantis users and material researchers who were in attendance.

Explainer computational chemistry

Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-

Yoshitaka Yamauchi Masataka Yamauchi

Molecular dynamics simulation attracts attention in materials research and development. Molecular dynamics simulation, which can track the movement of atoms and molecules in real time, has become an indispensable tool in research and development in various fields, including materials, chemistry, drug discovery, and biology, thanks to the dramatic improvement in computer performance in recent years. Molecular dynamics simulation has "overwhelming resolution."

MD Explainer computational chemistry

How to Choose DFT Software: Representative Software by Application and Implementation Steps

Makoto Sato Makoto Sato

In research and development, particularly in material development, challenges arise regarding the time and cost of experiments and securing human resources. In this context, simulation-based approaches using computational chemistry are receiving attention. Specifically, DFT (Density Functional Theory), which calculates electronic states based on quantum mechanics, offers a balance between computational cost and accuracy, and its application in research and development is expanding.

DFT Explainer computational chemistry

Featured Tags

Blog article list

NEW

Introduction to Machine Learning Interatomic Potentials (MLIPs): A Game Changer in Materials Simulation

Yoshitaka Yamauchi Masataka Yamauchi

Machine learning force field Explainer

Introduction to Machine Learning Interatomic Potentials (MLIPs): A Game Changer in Materials Simulation

Matlantis, an AI materials simulation that accelerates research, is taught at the University of Tokyo's SPRING GX lectures. Doctoral students experience AI-based molecular design simulations with ENEOS.

Interview

Matlantis, an AI materials simulation that accelerates research, is taught at the University of Tokyo's SPRING GX lectures. Doctoral students experience AI-based molecular design simulations with ENEOS.

Matlantis gave a presentation at the 26th Asian Workshop

Conference Report

Matlantis gave a presentation at the 26th Asian Workshop

A new model for doctoral education pioneered through industry-academia collaboration: A "new pilot case" demonstrated by Institute of Science Tokyo and Taiyo Yuden Practice School

Interview

A new model for doctoral education pioneered through industry-academia collaboration: A "new pilot case" demonstrated by Institute of Science Tokyo and Taiyo Yuden Practice School

High-Accuracy and High-Speed MOF Calculations with Matlantis - Benchmark Results of  Machine Learning Interatomic Potentials - 

Junichi Ishida Junichi Ishida

Explainer computational chemistry

High-Accuracy and High-Speed MOF Calculations with Matlantis
- Benchmark Results of 
Machine Learning Interatomic Potentials - 

Presentation given at the 86th The Japan Society of Applied Physics autumn meeting 2025

Conference Report

Presentation given at the 86th The Japan Society of Applied Physics autumn meeting 2025

[Kyoto Univ. Prof. Kitagawa Wins the Nobel Prize in Chemistry]What is PCP / MOF? Explaining Their Impact and Significance

Hirotaka Yonezawa Hirotaka Yonezawa

Explainer computational chemistry

[Kyoto Univ. Prof. Kitagawa Wins the Nobel Prize in Chemistry]What is PCP / MOF? Explaining Their Impact and Significance

The Future of Materials Science: Three Key Trends from ACS Fall 2025

Conference Report

The Future of Materials Science: Three Key Trends from ACS Fall 2025

[Event Report] Exhibited and presented at ACS Fall 2025

Conference Report

[Event Report] Exhibited and presented at ACS Fall 2025

[For Beginners] What is Density Functional Theory (DFT)? | Basics

Makoto Sato Makoto Sato

DFT Explainer computational chemistry

[For Beginners] What is Density Functional Theory (DFT)? | Basics

Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-

Yoshitaka Yamauchi Masataka Yamauchi

MD Explainer computational chemistry

Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-

How to Choose DFT Software: Representative Software by Application and Implementation Steps

Makoto Sato Makoto Sato

DFT Explainer computational chemistry

How to Choose DFT Software: Representative Software by Application and Implementation Steps