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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
From Lab to Deployment: Three Computational Trends Shaping the Future of Industry at TechConnect 2026
3月初旬、Matlantis USチームはノースカロライナ州ローリーで開催されたTechConnect World 2026に参加しました。TechConnectは、先端材料、エネルギー、サステナビリティ、AIまで幅広いテーマをカバーする、米国最大級の応用イノベーションカンファレンスです。TMS（TechConnectの直後に参加したもう一つの学会、レポー
Conference Report
Three Computational Trends Reshaping Materials Science from TMS 2026
2026年3月15-19日、晴天に恵まれたサンディエゴにて「TMS 2026年次大会」が開催され、数千人規模の材料科学者やエンジニアが一堂に会して最新の研究成果を共有しました。私たちMatlantisチームも、4,000人を超える参加者の輪の中に入って、研究成果を発表してきました。数多くのセッションに参加し、材料モデリングコミュニティを牽引するリーダーたち
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 Potential Explainer
Explainer : Why Did the AI Predict That ? Uncovering Atomic-Level Interpretability through PFP Descriptors and Shapley Values
The online proceedings for the Spring Meeting of the Japan Society of Applied Physics have been published, and it feels like the academic conference season has finally arrived. I'm sure there are many people who have submitted their proceedings and are busy preparing their presentations, and many others who are looking at the published proceedings and planning to attend. At the 86th Autumn Meeting of the Japan Society of Applied Physics last year, I gave a presentation entitled "Interpretation of predictions at the atomic level by combining PFP descriptors and Shapley values."
Materials Informatics Explainer 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 Interatomic Potentials Explainer
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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
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
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