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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.
Editor’s PICK
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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化学反応におけるDeep learningの適用
GraphNVP
Cheminformatics Bioinformatics Drug Discovery
High-speed X-ray crystallography using PFP
Materials informatics crystal structure prediction
High-speed crystal structure prediction using PFP and development of its efficient algorithm
Materials informatics crystal structure prediction
Generalization evaluation of machine learning scoring functions for structure-based virtual screening
Materials Informatics computational chemistry
Compound design using deep reinforcement learning
Chemoinformatics Bioinformatics
Universal Neural Network Potential for Materials Discovery
Materials Informatics Machine Learning Interatomic Potentials
Accelerating protein-compound docking simulations using GPUs
Cheminformatics Bioinformatics Drug Discovery
Neural Network Potential Considering Charge Transfer
Computational Chemistry
Searching for SARS-CoV-2 protease inhibitors using AI drug discovery technology
Cheminformatics Bioinformatics Drug Discovery
We are releasing the open source Pytorch implementation of DFT Dispersion.
Computational Chemistry
Improving Protein-Drug Binding Free Energy Calculations Using Neural Network Potential
Cheminformatics Bioinformatics Drug Discovery
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