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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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The Lab Is (Not) All You Need: Why autonomous laboratories make atomistic simulation more valuable, not less
Picture a laboratory at 3 am where the lights are on, but no one is there. A robotic arm lifts a crucible from a furnace and carries it to an X-ray diffractometer. Software reads the diffraction pattern, decides the run missed its target, adjusts the recipe, and queues the next attempt. By morning, the system has worked through more experimental iterations than a graduate student completes in a month,
Lab Automation Machine Learning Interatomic Potentials
Changing the rules of materials development with Physics-Grounded AI: how Matlantis PFP came to be
DFT calculations are the backbone of computational materials science: highly accurate, but notoriously expensive to run. Machine-learning interatomic potentials were meant to change that. Yet for a long time, each model could only handle a narrow, purpose-built slice of chemistry. The breakthrough that finally reconciled accuracy with generality is Matlantis PFP, a proprietary AI model with world-leading performance.
Interview
How to Choose Molecular Dynamics (MD) Simulation Software: 6 Key Points and 14 Tools Compared
In the field of materials development, we often encounter atomic-level questions such as, "Why do ions diffuse so easily in this material?", "What is happening near the interface?", and "How do defects affect the properties?". A powerful tool for answering these questions is molecular dynamics (MD) simulation, which visualizes the movement of atoms and molecules through calculations.
Molecular Dynamics Explainer
[Presentation Report] Tokyo University of Agriculture and Technology Doctor's Café #37 & AI Salon #14
"Interdisciplinary Innovation through AI ~Challenges to Sustainable Materials Development~" Event details: https://www.tuat-flourish.jp/topics/event/3832/ July 22, 2026, Tokyo University of Agriculture and Technology, Future Value Creation Research and Education Special Zone (FLOuRISH) presents "Doctor's Café #37 & AI Salon #14" (Kogane
Conference Report
Micro-Physical AI: Another invisible form of physical AI and the future of materials development
What is Physical AI? Current LLMs (Large Language Models) learn about the world from text. Through this, they incorporate the knowledge accumulated by humans so far and are already having a major impact on the world. However, to borrow Plato's words, text is merely a "shadow" of reality. Text only records a portion of what humans have been able to verbalize, and the overwhelming majority of phenomena occurring in the physical world have not yet been turned into words.
CEO Blog
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TMS 2026が明らかにした材料科学を変革する3つの計算トレンド
Conference Report
What Is Digital Transformation in Materials R&D? Accelerating Materials Discovery
Materials Informatics computational chemistry
Crystal Structure Prediction Using Optuna in Matlantis CSP
AI pioneers materials development through computational chemistry
DFT Molecular Dynamics Machine Learning Interatomic Potentials Explainer
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
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