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【登壇レポート】東京農工大学 Doctor’s Café #37 & AI Salon #14
「AIによる学際融合イノベーション ～持続可能な材料開発への挑戦～」 イベント詳細：https://www.tuat-flourish.jp/topics/event/3832/ 2026年7月22日、東京農工大学 未来価値創造研究教育特区（FLOuRISH）主催「Doctor's Café #37 & AI Salon #14」（小金
Conference Report
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
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DFTソフトウェアの選び方｜用途別代表ソフトと導入ステップ
DFT 解説記事 計算化学
An Introduction to Materials Informatics: From Fundamental Concepts to Cutting-Edge Research.
Materials Informatics Explainer Computational Chemistry
Accelerating free energy calculations using molecular dynamics with the MN-Core 2 deep learning processor
Free energy estimation using a diffusion model
Development of excited state neural network potential
Application of deep learning to chemical reactions
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
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