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Micro-Physical AI: The Atomic-Scale Frontier of Physical AI and the Future of Materials Discovery
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
What Is the Semiconductor Front-End Process? A Materials Simulation Guide to Deposition, Lithography, Etching, and CMP
Semiconductor devices are manufactured by forming nanoscale structures on silicon wafers. This involves repeating numerous processes such as film deposition, photolithography, etching, ion implantation, and planarization to create fine circuit structures on the wafer. In these manufacturing processes, the physical properties of the materials and the reactions occurring at the surface and interface significantly impact device performance and yield. In recent years, the micro-scale of devices has been a key factor.
Explainer Computational Chemistry
From Lab to Deployment: Three Computational Trends Shaping the Future of Industry at TechConnect 2026
In early March, the Matlantis US team headed down to Raleigh, NC, for TechConnect World 2026. It’s one of the largest conferences in the country for applied innovation, covering everything from advanced materials and energy to sustainability and AI. While a conference like TMS (which we headed to right afterwards, see our report here
Conference Report
Three Computational Trends Reshaping Materials Science from TMS 2026
From March 15-19, 2026, the TMS 2026 Annual Meeting was held in San Diego under clear skies, bringing together thousands of materials scientists and engineers to share the latest research findings. Our Matlantis team also joined the circle of over 4,000 participants and presented our research results. Attending numerous sessions and directly interacting with leaders who are driving
Conference Report
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 Interatomic Potentials Explainer
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Micro-Physical AI: The Atomic-Scale Frontier of Physical AI and the Future of Materials Discovery
CEO Blog
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What is lab automation? Concepts and latest trends in experimental automation in materials development.
Lab Automation Explainer
What Is the Semiconductor Front-End Process? A Materials Simulation Guide to Deposition, Lithography, Etching, and CMP
Explainer Computational Chemistry
From Lab to Deployment: Three Computational Trends Shaping the Future of Industry at TechConnect 2026
Conference Report
Three Computational Trends Reshaping Materials Science from TMS 2026
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
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