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  6. Utilization of the Schrödinger Platform at Panasonic

Utilization of the Schrödinger Platform at Panasonic

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last updated:May 26, 2025

シュレーディンガー
シュレーディンガー
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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.*

    MemoryaluminumSoftware (middle, driver, security, etc.)
Panasonic

Utilization of the Schrödinger Platform at Panasonic

Panasonic Novel Materials Faster_ページ_1.jpg
Panasonic Novel Materials Faster_ページ_1.jpg
  • Related Link - https://www.schrodinger.com/materials-science
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Our Materials Science Suite is capable of addressing a wide range of materials research fields. ■ Property predictions using Density Functional Theory (DFT) calculations and first-principles calculations for periodic systems HOMO/LUMO/pKa/solvent effects/IR/Raman/UV-vis/VCD/NMR/oxidation-reduction potential/triplet excited state energy/TADF S1-Tx gap/fluorescence/phosphorescence/vibrational calculations/structure optimization/transition state calculations/reaction pathway analysis/adsorption energy/bond dissociation energy/electron and hole mobility/reorientation (rearrangement, reconfiguration) energy ■ Property predictions using Molecular Mechanics (MM), Molecular Dynamics (MD), and coarse-grained MD methods Density/conformation analysis/cross-linked structures/Young's modulus/viscosity/surface tension/glass transition temperature (Tg)/molecular diffusion/thermal expansion/crystal morphology/swelling/stress-strain curves/solubility parameters Methods available for use in machine learning Generation of various descriptors and fingerprints/Partial Least Squares (PLS) regression/multiple linear regression (MLR)/Principal Component Regression (PCR)/Kernel PLS/Bayesian classification/Recursive Partitioning (RP) analysis/Self-Organizing Maps/Tg, dielectric constant, boiling point, vapor pressure prediction models/genetic algorithms/active learning

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Utilization of the Schrödinger Platform at Panasonic

Utilization of the Schrödinger Platform at Panasonic

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[Case Study] Panasonic and Schrödinger Design Over 50 New Molecules to Improve Hall Mobility

[Case Study] Panasonic and Schrödinger Design Over 50 New Molecules to Improve Hall Mobility

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June 4 (Tuesday) to June 7 (Friday) - Free Web Seminar: Introduction of case studies on simulation software for materials science and the use of materials informatics.

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Schrödinger, Inc. will hold a materials science web seminar titled 'Schrodinger Materials Science Seminar Japan 2024' from June 4 (Tuesday) to June 7 (Friday). In recent years, computational chemistry has established itself as an essential foundational technology in the development of various materials, ranging from semiconductors and electronic components to everyday products. Since its founding in 1990, Schrödinger has continuously worked on enhancing software and informatics for molecular design, providing practical solutions for the efficiency of various material developments. We will be hosting a seminar to introduce case studies utilizing these software tools. The content will be satisfying for both those with experience in simulations and machine learning, as well as those who are just starting out. We welcome participation in any sessions of interest, so please feel free to join us.

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Schrödinger Co., Ltd. is the Japanese subsidiary of Schrödinger Inc., headquartered in New York, USA. Schrödinger has a history of about 30 years in developing software that integrates advanced technologies in chemistry and computer science, primarily in the fields of materials science and life sciences, providing advanced solutions for drug discovery, biologics, and materials research and development.

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