We will be exhibiting at "Kansai Plastic Japan" from May 11 (Wednesday) to May 13 (Friday).

Support for property prediction, analysis, and design based on molecular structure and nanoscale structure through large-scale statistical analysis of experimental data and high-precision nanoscale simulations.

We will introduce the features of Schrödinger's Materials Science Solutions (MSS) in an easy-to-understand manner. 【Product Features】 ■ Molecular design using quantum calculations ■ Prediction of liquid and polymer physical properties ■ Crystals, surfaces, and interfaces: First-principles calculations for periodic systems, chemical reactions on electrodes and catalysts, and a wide range of applications to semiconductors/molecular crystals/MOFs ■ Statistical analysis and machine learning ■ Flexible and powerful GUI/CUI user interface *For more details, please feel free to contact us.

• simulator
• Embedded OS
• EAI/ETL/WEB application server

A GPU-assisted high-speed molecular dynamics engine that supports the rapid and high-precision prediction of physical property values of polymers and resins.

We would like to introduce Schrödinger's software that supports the prediction of physical properties of polymers and resins. 【Product Features】 ■ Accelerates MD calculations with high-efficiency GPU code Tens of thousands of atoms x hundreds of nanoseconds/day = lGPU ■ Unique high-precision force field parameter OPLS4 ■ Diverse polymer structure builder including cross-linked resins ■ Physical property prediction and analysis tools *For more details, please feel free to contact us.

• Composite Materials
• simulator
• Embedded OS

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.

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

Predict the evaporation or sublimation temperature with an accuracy of ±9°C on average, calculating hundreds of complexes per second.

A New Path to Precursor Development: Schrödinger's Machine Learning This predictive model opens a new avenue for designing new precursors with improved performance, optimizing not only the deposition and chemistry but also the temperature at which they can evaporate or sublime to be supplied as vapor. This advancement allows for a much broader range of structural changes to be screened computationally than before, enabling the generation of candidate precursors for experimental synthesis and testing that are less risky and more innovative. With this volatility model and the computational workflow for reactivity and decomposition based on Schrödinger's quantum mechanics, a complete design kit for vapor phase deposition and etching is provided, accelerating research on materials and processes for new technologies. *For 50 common metal and metalloid complexes, the evaporation or sublimation temperature at a given vapor pressure is predicted with an accuracy of ±9°C (about 3% of absolute temperature). *It can compute hundreds of complexes per second, resulting in a fast turnaround time. *For more details, please refer to the PDF document or feel free to contact us.

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

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.

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

Supporting material research and development through high-speed molecular simulations! Here is an overview of our products.

Our Materials Science Suite is capable of addressing a wide range of materials research fields. ■ Property predictions through 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) methods, Molecular Dynamics (MD) methods, and coarse-grained MD Density/conformation analysis/crosslinked structures/Young's modulus/viscosity/surface tension/glass transition temperature (Tg)/molecular diffusion/thermal expansion/crystal morphology/swelling/stress-strain curves/solubility parameters Methods usable 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

• simulator
• Embedded OS
• EAI/ETL/WEB application server

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.

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

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.

• Other electronic parts
• Organic EL
• simulator

We will be exhibiting at the joint company information session hosted by Mynavi on the following schedule. We will clearly explain the four core businesses of Takaya. If you are interested in "monozukuri" (manufacturing), please come to our booth!

At this exhibition, we will introduce our materials equipped with new functions and values that contribute to solving our customers' challenges. Our mid-term management plan includes "securing competitive advantage through technology leadership" as one of its basic strategies, and our engineers, who embody this strategy, will explain and propose the exhibited products in their own words. We would love to hear your feedback, so please feel free to stop by our booth.

[Speaker] Naoki Toda, Chief Economist, Management Strategy Research Office, Tokyo Electric Power Company Holdings, Inc. [Key Lecture Content] Since the 1990s, the global advancement of power system reforms has aimed for efficiency and optimization in both the operation and investment of facilities through active market competition. However, with the progress of DX/GX, the anticipated scenarios for electricity demand have led to the emergence of underinvestment issues worldwide. The IEA's report "Electricity Market Design," published last year, provides a comprehensive review of the reform situations in various countries and indicates that the underinvestment problem is a common issue across the globe. In this lecture, I will explain the current state of the underinvestment issue and potential responses from a business system perspective, referencing the IEA report and others. [Lecture Items] 1. Background of power system reforms and the underinvestment issue 2. About IEA "Electricity Market Design" 3. Reflecting on the thoughts of Akihiro Sawa on the 10th anniversary of his passing 4. Possible solutions to the Tenor Gap issue 5. Outlook for Japan's power system 6. Q&A / Business card exchange

[Instructor] Shinichiro Takiguchi, Senior Specialist, Innovation Strategy Center, Japan Research Institute, Inc. [Key Lecture Content] With the increase in renewable energy, the introduction of energy storage systems has progressed in two forms: renewable energy co-located systems to adjust power generation fluctuations, and demand co-located systems to secure electricity during grid outages. However, the landscape is changing significantly. The "Supply and Demand Adjustment Market" has been established by OCTTO (Organization for Cross-regional Coordination of Transmission Operators), and particularly due to the continued high pricing agreements in the short-term response market (primary adjustment power responding within 10 seconds), there have been numerous cases where battery storage profits are secured through market transactions. In the grid energy storage business, two capabilities are required: "trading power" and "financial power." This lecture will explain the business structure and key points of profitability. Additionally, based on this business structure, we will classify the companies entering the grid energy storage business and organize the market structure of this sector. As this business is influenced by regulatory trends, we will also discuss regulatory outlooks. Finally, we will talk about future prospects.

[Lecturer] Professor Yoshiharu Nishimura, Senior Researcher, Kansai Electric Power Co., Inc. Graduate School of Engineering, Osaka University, Business Engineering Program [Key Lecture Content] The examination of the next-generation power infrastructure conducted in 2025 has entered the implementation phase, including amendments to the Electricity Business Act, towards a new power policy standard (a sustainable framework by responsible players) in 2026. This lecture will outline the content and related policy adjustments for businesses involving renewable energy and storage batteries, and consider the correct business concepts, necessary timelines, and how to create business models moving forward. [Lecture Items] 1. What is the 2026 Next-Generation Power Infrastructure Reconstruction? 2. Renewable Energy and Decarbonization Policies and Issues with Chinese Products 3. The Latest Status of Storage Batteries and the Timeline and Vision for DER Business 4. Q&A / Business Card Exchange