Scientific simulation software - List of Manufacturers, Suppliers, Companies and Products

Presentation of Japanese Materials: Organic Electronics

Identifying promising candidate substances! Useful for selecting compounds that meet the conditions for device optimization.

This document introduces the applications of Schrodinger's 'Materials Science Suite' in organic electronics and organic EL. Through insights gained from computational results and theoretical interpretations, it is possible to identify promising candidate materials, enabling efficient development of organic light-emitting diodes (OLEDs) and organic semiconductors. Additionally, it is useful for selecting compounds that meet the conditions for device optimization. Specifically, using density functional theory (DFT), it is possible to calculate molecular properties related to organic EL material development, such as: - Oxidation potential - Reduction potential - Hole reorganization (rearrangement, reconfiguration) energy - Electron reorganization energy - Triplet energy - Triplet reorganization energy - Absorption spectrum - TADF S1-Tx gap - Fluorescence The structure of thin films can be predicted by simulating the actual deposition onto a substrate using molecular dynamics (MD). Basic information continues below.

Company：シュレーディンガー
Price：Other

Other electronic parts
simulator
Organic EL

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Integrated platform to support the development/analysis of semiconductor-related technologies.

An integrated platform that supports the development/analysis of semiconductors and related technologies with high speed and high precision.

We will clearly introduce Schrödinger's integrated platform that supports the development/analysis of semiconductors and related technologies. 【Product Overview】 ■ Prediction and analysis of semiconductor physical properties using quantum mechanical calculations - Electronic properties - Mechanical properties (elastic constant tensor, bulk modulus) - Dielectric properties - Reaction pathway exploration ■ Optimization of semiconductor film deposition processes (CVD, ALD, ALE) - Development of new precursors using quantum mechanical calculations and machine learning ■ Optimization of semiconductor packaging using classical molecular dynamics calculations - Construction of cross-linked structure models for resin encapsulants - Prediction of heat resistance through calculations of glass transition temperature - Prediction of gas barrier properties through calculations of absorption rates and diffusion coefficients of water and gas molecules - Analysis of physical property changes during the absorption of water/gas molecules *For more details, please refer to the PDF document or feel free to contact us.

Company：シュレーディンガー
Price：Other

Embedded OS
simulator
Other semiconductors

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Integrated platform supporting battery material development

Accelerating research and development of next-generation battery materials through atomic-level simulations and machine learning.

We would like to introduce Schrödinger's integrated platform that supports the development and analysis of next-generation battery materials. 【Product Features】 ■ Analysis of ion behavior within electrodes through quantum mechanical calculations ■ Analysis of the conduction mechanism of Li+ ions in polymer electrolytes using molecular dynamics simulations ■ Development of electrolytes through molecular simulations and machine learning *For more details, please refer to the PDF document or feel free to contact us.

Company：シュレーディンガー
Price：Other

Embedded OS
simulator
Secondary Cells/Batteries

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Utilization of the Schrödinger Platform at Panasonic

Towards the realization of faster new material development.

"By gaining access to Schrödinger's tools and unprecedented computational power, Panasonic Industry Co., Ltd.'s approach to innovation has changed." This article is based on an interview with Mr. Nobuyuki Matsuzawa, Principal Engineer at the Process Device Innovation Center of Panasonic Industry Co., Ltd. Please take a look. *For more details, please refer to the PDF document or feel free to contact us.*

Company：シュレーディンガー
Price：Other

Software (middle, driver, security, etc.)
aluminum
Memory

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

[Presentation of Materials] Machine Learning and Material Property Prediction

Quickly transform data into knowledge based on informatics! Contributing to the field of advanced materials development.

This document introduces the machine learning and material property prediction capabilities of the 'Materials Science Suite' handled by Schrodinger. This product features a powerful and user-friendly integrated informatics environment. With simple GUI operations, it allows for the analysis of experimental and simulation data using molecular structure fingerprints, visualizing the relationship between molecular structures and physical properties, and building machine learning models to predict the physical properties of new molecular structures. [Contents] ■ Background ■ Glass Transition Temperature ■ Prediction of Polymer Properties ■ KPLS Regression Using Fingerprints ■ Further Developments *For more details, please refer to the PDF document or feel free to contact us.

Company：シュレーディンガー
Price：Other

Software (middle, driver, security, etc.)
simulator

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

[Data] Materials Science Reaction Workflow

It can cover often overlooked conformers, streamline workflows, and enhance reproducibility and predictability.

In the Schrödinger materials science reaction workflow, automatic exploration of the conformational space allows for the coverage of often-overlooked conformers. Furthermore, the automation of quantum chemical calculations eliminates the challenging processes that require meticulous maintenance of hundreds of files and properties, as well as specialized training. This simplifies the workflow and enhances reproducibility and predictability. [Case Study] ■ Diels-Alder Reaction *For more details, please refer to the PDF document or feel free to contact us.

Company：シュレーディンガー
Price：Other

Software (middle, driver, security, etc.)

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Presentation of Case Studies: Machine Learning Force Fields for Material Modeling

Introduction of use cases for machine-learned force fields.

Machine-learned force fields (MLFF) are designed to improve traditional force fields by incorporating machine learning models to accurately model interactions between atoms and molecules. This technology is based on neural network potential energy surface (NN-PES) architecture, and the model is trained to reproduce the total electronic energy of the system with chemical accuracy. With the combination of OPLS4 for initial structure generation, fast DFT and MD engines, and key MLFF methods, Schrödinger has become a leading partner in MLFF generation. This application note introduces the application of QRNN technology in modeling across three different areas of materials science: liquid electrolytes, polymers, and ionic liquids.

Company：シュレーディンガー
Price：Other

Software (middle, driver, security, etc.)
plastic
Other polymer materials

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

[Japanese Example] Calculation and Analysis Tool for Environmentally Friendly Cosmetic Formulation Design

[L'Oréal Case] Molecular Dynamics and Coarse-Grained Simulations to Facilitate the Formulation Design of Eco-Friendly Cosmetics

L'Oréal, the world's number one cosmetics company, has gained a deeper understanding of the differences in shear behavior between synthetic polymers and polysaccharide polymers on biomimetic surfaces by utilizing Schrödinger's software. • New insights into the aggregation behavior of shampoo formulations were obtained using simulated hair surfaces. • The influence of polymer topology was demonstrated, linking observed polymer interactions to experimentally observable phenomena. • A framework was established for studying complex formulations in contact with biomimetic surfaces using molecular dynamics simulations. • The design of eco-friendly cosmetic formulations was rationally accelerated.

Company：シュレーディンガー
Price：Other

Embedded OS
Other polymer materials
Computational Chemistry

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Collection of Japanese Examples: Moisture Absorption Prediction and Its Effects on Amorphous Amylose Starch

Molecular dynamics simulations that promote the optimization of quality and processing in food and beverages, packaging, and pharmaceuticals.

Schrödinger provides a powerful and user-friendly integrated software solution for the research and development of consumer goods. Schrödinger's platform is designed for a wide range of users, from beginners to experts in computational chemistry, offering a simple workflow to build, simulate, and analyze real systems using advanced physics-based modeling and machine learning technologies. ■ Accurately predicts key physical properties such as the glass transition temperature (Tg) of amorphous amylose polymers in both wet and dry states. ■ Effectively models water absorption and transport by investigating the impact of moisture content on Tg and the diffusion of water within starch polymers. ■ The OPLS3e force field provides high accuracy for amorphous starch models. ■ Detailed studies of the interactions between water and amylose, along with further research on the effects of components on complex starch formulations.

Company：シュレーディンガー
Price：Other

Embedded OS
plastic
Organic Natural Materials

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

[Presentation of Japanese Materials] Enhancing the Precision and Speed of Material Development with High-Performance Computational Tools

[Japanese Flyer] Overview of Schrödinger's Materials Science Platform

Schrödinger provides a software platform for innovation in the development of diverse materials, including polymer materials, organic electronics, catalysis and reactivity, thin film processes, energy recovery and storage, pharmaceutical formulations, consumer goods, metals, alloys, and ceramics. By exploring vast compound spaces and predicting molecular properties with high precision, it supports the rapid design of new materials and enhances cost efficiency. This document provides an overview of the platform for materials development. *For more details, please feel free to contact us.*

Company：シュレーディンガー
Price：Other

Embedded OS
Composite Materials
Contract Analysis

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Scientific simulation software - List of Manufacturers, Suppliers, Companies and Products

Scientific simulation software Product List