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AI pioneers materials development through computational chemistry
This article is a blog post based on an article written by Zhang, Customer Success Engineer Matlantis Corporation, which was published in the February 2026 issue of the technical magazine "Monthly Material Stage" in the special feature "Improving the Efficiency of Materials Development Using AI and Automated Experiments." 1. Materials Development to Date Our lives have been enriched by numerous innovative materials. However,
DFT Molecular Dynamics Machine Learning Potential Explainer
Explainer : Why Did the AI Predict That ? Uncovering Atomic-Level Interpretability through PFP Descriptors and Shapley Values
The online proceedings for the Spring Meeting of the Japan Society of Applied Physics have been published, and it feels like the academic conference season has finally arrived. I'm sure there are many people who have submitted their proceedings and are busy preparing their presentations, and many others who are looking at the published proceedings and planning to attend. At the 86th Autumn Meeting of the Japan Society of Applied Physics last year, I gave a presentation entitled "Interpretation of predictions at the atomic level by combining PFP descriptors and Shapley values."
Materials Informatics Explainer computational chemistry
Introduction to Machine Learning Interatomic Potentials (MLIPs): A Game Changer in Materials Simulation
Introduction In the fields of materials science, chemistry, and drug discovery R&D, atomic and molecular level simulations have become established as a fundamental technology for elucidating the properties and reaction mechanisms of materials from a microscopic perspective. However, full-scale application in R&D has always been hindered by the trade-off between "accuracy," "computational cost," and "versatility." For example, first-principles calculations are quantum
Machine Learning Interatomic Potentials Explainer
High-Accuracy and High-Speed MOF Calculations with Matlantis - Benchmark Results of Machine Learning Interatomic Potentials -
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
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
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Presentation given at the 86th The Japan Society of Applied Physics autumn meeting 2025
Conference Report
[Kyoto Univ. Prof. Kitagawa Wins the Nobel Prize in Chemistry]What is PCP / MOF? Explaining Their Impact and Significance
Explainer computational chemistry
The Future of Materials Science: Three Key Trends from ACS Fall 2025
Conference Report
[Event Report] Exhibited and presented at ACS Fall 2025
Conference Report
![[Event Report] Exhibited and presented at ACS Fall 2025](https://matlantis.com/wp-content/uploads/2025/08/title-image-scaled.jpg)
[For Beginners] What is Density Functional Theory (DFT)? | Basics
DFT Explainer computational chemistry
![[For Beginners] What is Density Functional Theory (DFT)? | Basics](https://matlantis.com/wp-content/uploads/2025/08/top_eyecatch-01-scaled.jpg)
Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-
MD Explainer computational chemistry
How to Choose DFT Software: Representative Software by Application and Implementation Steps
DFT Explainer computational chemistry
An Introduction to Materials Informatics: From Fundamental Concepts to Cutting-Edge Research.
Materials Informatics Explainer computational chemistry
Accelerating free energy calculations using molecular dynamics with the MN-Core 2 deep learning processor
Free energy estimation using a diffusion model
Development of excited state neural network potential
Application of deep learning to chemical reactions
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