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High-Accuracy and High-Speed MOF Calculations with Matlantis - Benchmark Results of Machine Learning Interatomic Potentials -

Matlantis can calculate a wide range of materials, but among them, metal-organic frameworks (MOFs) are important materials with a wide range of applications, including catalysts and CO2 storage. This material, discovered in the 1990s, has already been industrialized and is attracting attention worldwide as an essential material for maintaining a sustainable society. In fact, the 2025 Nobel Prize

Explainer computational chemistry

[Kyoto Univ. Prof. Kitagawa Wins the Nobel Prize in Chemistry]What is PCP / MOF? Explaining Their Impact and Significance

Hirotaka Yonezawa

Around 7pm on October 8th, 2025, breaking news broke that "Professor Kitagawa of Kyoto University has won the Nobel Prize in Chemistry." The Matlantis User Conference 2025 was being held on the same day, and we were able to celebrate this good news with many Matlantis users and material researchers who were in attendance.

Explainer computational chemistry

Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-

Yoshitaka Yamauchi

材料研究開発現場で注目される分子動力学シミュレーション原子や分子の動きをリアルタイムで追跡できる分子動力学シミュレーションは、近年の計算機性能の飛躍的な向上によって、材料、化学、創薬、バイオなど、さまざまな分野の研究開発において不可欠なツールとなっています。分子動力学シミュレーションは「圧倒的な分解能をも

MD Explainer computational chemistry

How to Choose DFT Software: Representative Software by Application and Implementation Steps

Makoto Sato

In research and development, particularly in material development, challenges arise regarding the time and cost of experiments and securing human resources. In this context, simulation-based approaches using computational chemistry are receiving attention. Specifically, DFT (Density Functional Theory), which calculates electronic states based on quantum mechanics, offers a balance between computational cost and accuracy, and its application in research and development is expanding.

DFT Explainer computational chemistry

An Introduction to Materials Informatics: From Fundamental Concepts to Cutting-Edge Research.

Bon Cho

In the fields of materials science and pharmaceutical research and development (R&D), significant challenges persist, including the time, cost, and labor associated with experimentation and evaluation, as well as the difficulty of securing skilled personnel. In particular, traditional R&D methods, which often depend on individual experience and intuition, are reaching their limits in simultaneously achieving both development speed and precision.

Materials Informatics Explainer computational chemistry

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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 Force Fields

Accelerating protein-compound docking simulations using GPUs