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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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AI pioneers materials development through computational chemistry
DFT Molecular Dynamics Machine Learning Potential Explainer
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Learning AI materials simulation to accelerate research at the Fukui Kenichi Memorial Research Center, Kyoto University - Working with ENEOS to design the "best CO2 adsorbent"
Explainer : Why Did the AI Predict That ? Uncovering Atomic-Level Interpretability through PFP Descriptors and Shapley Values
Materials Informatics Explainer computational chemistry
Writing SMILES from scratch
Explainer computational chemistry
Nagoya University × Matlantis Case Study:“Advanced Experiments for Frontier Technologies and Sciences” —A Four-Day Intensive Course That Sparked Experimental Students’ Curiosity Through AI Simulation
Interview computational chemistry
Introduction to Machine Learning Interatomic Potentials (MLIPs): A Game Changer in Materials Simulation
Machine Learning Interatomic Potentials Explainer
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 gave a presentation at the 26th Asian Workshop
Conference Report
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
High-Accuracy and High-Speed MOF Calculations with Matlantis - Benchmark Results of Machine Learning Interatomic Potentials -
Explainer computational chemistry
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
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