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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.
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[Event Report] Exhibited and presented at ACS Fall 2025
As previously announced, Matlantis Corporation and Preferred Networks, Inc. participated in the "ACS Meetings & Expos / ACS Fall 2025," held from August 17th to 21st, 2025, where we hosted a booth and gave Oral and poster presentations. In this article, we'll share a look at the event with photos.
Conference Report
Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-
Molecular dynamics simulation attracts attention in materials research and development. Molecular dynamics simulation, which can track the movement of atoms and molecules in real time, has become an indispensable tool in research and development in various fields, including materials, chemistry, drug discovery, and biology, thanks to the dramatic improvement in computer performance in recent years. Molecular dynamics simulation is described as "a microscope with overwhelming resolution."
Molecular dynamics computational chemistry
How to Choose DFT Software: Representative Software by Application and Implementation Steps
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 Computational chemistry
An Introduction to Materials Informatics: From Fundamental Concepts to Cutting-Edge Research.
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.
MI Computational chemistry
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Generalization evaluation of machine learning scoring functions for structure-based virtual screening
MI Computational chemistry
Compound design using deep reinforcement learning
ケモインフォマティクス バイオインフォマティクス
Universal Neural Network Potential for Materials Discovery
マテリアルズインフォマティクス 機械学習力場
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
Crystal structure prediction using PFP
MI Crystal Structure Prediction
Surface energy benchmark using PFP
PFP benchmark
Lattice thermal conductivity calculation using PFP
PFP benchmark 熱物性
Application of LightPFP to enzyme reactions
バイオインフォマティクス 創薬 酵素
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