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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.
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Writing SMILES from scratch
With the spread of materials informatics (MI), DFT calculations, and molecular dynamics simulations, the number of opportunities to input molecular structures on computers is rapidly increasing. In these cases, the SMILES (Simplified Molecular Input Line Entry System) notation, which treats molecular structures as character strings, is often used. Many chemical software programs allow you to input molecular structures and SMILES.
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
Nagoya University's Graduate School of Engineering's "Cutting-Edge Science and Engineering Experiments" for the second half of the 2025 academic year included a chemical simulation experiment class incorporating the AI atomic-level simulator "Matlantis." By utilizing the power of AI simulation, the quality and speed of research can be dramatically improved. Experimental students who are not experts in computational chemistry experienced this potential with their own hands.
Interview 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 force field 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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Writing SMILES from scratch
Explainer computational chemistry
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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 force field 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
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)
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