Scientific simulation software - メーカー・企業と製品の一覧

[Data] Quantum ESPRESSO Interface

By performing it on a single graphical interface, calculations can be done efficiently!

This document introduces the Quantum ESPRESSO Interface handled by Schrodinger's "Materials Science Suite." Through an official partnership, integration between the molecular simulation environment "Maestro" and "Quantum ESPRESSO" has been realized. By performing advanced quantum simulations from crystal structure creation to execution and analysis on a single graphical interface, efficient computational work is possible. Furthermore, calculations using the Effective Screening Medium method allow for the electronic state calculations of various surface-solvent systems, including electrode surface reactions. [Contents] ■ Nanotechnology and Computational Science ■ About Quantum ESPRESSO ■ Main Features of the Quantum ESPRESSO Interface ■ Maestro and Python API ■ Effective Screening Medium Method (ESM Method) *For more details, please refer to the PDF document or feel free to contact us.

企業：シュレーディンガー
価格：Other

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

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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.

企業：シュレーディンガー
価格：Other

Embedded OS
simulator
Other semiconductors

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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.

企業：シュレーディンガー
価格：Other

Embedded OS
simulator
Secondary Cells/Batteries

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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.*

企業：シュレーディンガー
価格：Other

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

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Case Studies: Machine Learning for Materials Research

Case studies on inorganic solids and polymers! Designing new compounds in a cost-effective and time-efficient manner.

High-quality physics-based simulations and machine learning approaches accelerate the research of new materials and shorten the time to market. Through the workflow, it is possible to automatically create hundreds of predictive models using representative machine learning techniques (Partial Least Squares Regression (PLS), Multiple Linear Regression (MLR), Principal Component Regression (PCR), Kernel PLS) combined with descriptors and fingerprints, and select models with high predictive performance (AutoQSAR). For datasets with thousands of data points, similar to AutoQSAR, the workflow allows for the automatic creation of predictive models using deep learning (DeepAutoQSAR, DeepChem/AutoQSAR). To represent the properties of a wide range of materials (polymers, molecules, solids), effective descriptors customized for each system can be utilized.

企業：シュレーディンガー
価格：Other

Software (middle, driver, security, etc.)

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[User Case Presentation] Development of Next-Generation Lithium-Ion Batteries

We will introduce a case of innovative material search implemented in the development of next-generation lithium-ion batteries by the CEO of Eonix.

Eonix is a startup focused on the rapid design of next-generation materials for energy storage technologies targeting home appliances, grid storage, and electric vehicles. CEO Don DeRosa, Ph.D., explains how combining high-throughput screening and physics-based modeling can transform the material discovery process for building better batteries.

企業：シュレーディンガー
価格：Other

Software (middle, driver, security, etc.)

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Presentation of Materials: Molecular Simulation and Machine Learning for Daily Consumer Goods

Physics-based simulation and machine learning software for a wide range of users, from beginners to experts in computational chemistry.

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 technology. Here, we introduce Schrödinger's applications for consumer goods research and development. ■ Food and Beverage ■ Cosmetics and Personal Care ■ Cleaning Agents ■ Packaging Materials ■ Materials Informatics

企業：シュレーディンガー
価格：Other

Embedded OS
Cosmetic materials and raw materials
Cosmetic synthesis and fermentation

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Scientific simulation software - メーカー・企業と製品の一覧

Scientific simulation softwareの製品一覧

[Data] Quantum ESPRESSO Interface

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

Integrated platform supporting battery material development

Utilization of the Schrödinger Platform at Panasonic

Case Studies: Machine Learning for Materials Research

[User Case Presentation] Development of Next-Generation Lithium-Ion Batteries

Presentation of Materials: Molecular Simulation and Machine Learning for Daily Consumer Goods

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