Cases
We've highlighted some of the most notable examples from user interviews, simulation cases, and research papers. Browse by category to explore the details that interest you.
Featured Cases
Featured Customer Stories
It was impossible to predict based on past experience or intuition.
Creating new combinations and encounters
Kuraray Co., Ltd.Chemicals
Combined with AI and experimental data, computational chemistry contributes to raising the level of research and development. “We expect that it can be a powerful tool to compete globally.”
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From left to right: Dr. Kamata, Senior Manager of the Planning and Administration Department And Digital Solution Department, Research and Development Division, Mr. Sugoh, General Manager of the Research and Development Division, and Dr. Miura, Manager of the Digital Solution Department established within the Research and Development Division
Featured Calculation Examples
Direct derivation of atomic displacement parameters using NNP-MD
Atomic displacement parameter (ADP）
thermoelectric materials
Atomic Displacement Parameters (ADPs), which describe the thermal vibration and positional fluctuation of atoms, play a crucial role in crystallographic structural analysis. While ADPs have conventionally been derived through lattice dynamics calculations, this approach faces difficulty when applied to complex crystals containing substitutional disorder or split sites.
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Featured Published Papers
Graph Neural Network-Based Interatomic Potential Calculations Combined With Grand Canonical Monte Carlo Simulation to Predict the Electrochemical Potential Profile: A Model Study Using Spinel-Type Titanates
Papers using Matlantis
ceramics
電池
Density functional theory (DFT) is an effective approach for the in silico design of battery materials; however, DFT calculations are usually performed at 0 K, and the resulting electrochemical potential profiles are stepwise and do not resemble the continuous profiles obtained experimentally. Therefore, the estimation of temperature effects on electrochemical potential profiles remains challenging. Recently, theoretical calculations based on graph-neural-network-based interatomic potentials (GNNPs) have garnered attention because of their computational …
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List of customer cases
We will introduce the background, challenges, usage methods, and results obtained by companies that have introduced Matlantis. Through real voices from the field, we will provide you with hints that will be useful when considering introducing Matlantis.
List of calculation examples
We will introduce specific simulation examples using Matlantis for various material systems. You can see how high speed and high accuracy calculations are achieved.
List of published papers
This site mainly features papers published by Matlantis users about research using Matlantis, as well as papers about machine learning potential and PFP, which are core technologies of Matlantis.
How to cite
For information on how to cite Matlantis papers and case studies, please see the link below.
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