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

Nitto Kohki will hold the "2025 Attack Fair in Meiko" for two days from July 23 (Wednesday) to July 24 (Thursday), 2025, at the "Kowa Seminar Plaza 2F Training Room NO.12 (inside Kowa Sports Land)." The Attack Fair is an exhibition where you can actually "see, touch, and operate" Nitto Kohki's products. Additionally, there will be wonderful souvenirs for attendees and many fun activities planned. We look forward to welcoming everyone!

[Lecturers] Daisuke Tateno, Partner, Arthur D. Little Japan K.K. Akihiro Nagayama, Principal [Key Lecture Content] Drones, AI, software, satellites, and electronic warfare are fundamentally changing the way wars are fought, and the defense industry is currently transitioning from a heavy and traditional model to one centered around AI, software, data, and unmanned systems. In the United States, emerging companies like Palantir and Anduril, which are software-driven, are challenging existing defense firms. In Japan, structural changes are beginning with the Defense Capability Development Plan, the Defense Industry Base Strengthening Act, and the entry of startups. This seminar will discuss the changes in the global security environment and industrial structure, the strategies of advanced players both domestically and internationally, and the areas where Japanese companies can succeed, including dual-use, collaboration with software companies, M&A and alliances, and talent strategies. We will consider how to view change not as a threat but as a growth opportunity, and how to construct a new Japanese defense industry from a management perspective.

[Speaker] Kazuki Miyamoto, Representative/Analyst, VentureClef, USA [Key Lecture Content] The frontier model in the United States is evolving at an unexpected speed. AI agents based on the frontier model have completely transformed business forms in companies. Chinese companies are developing large-scale open source projects, and their performance is approaching that of the U.S. model. Meanwhile, U.S. companies have begun developing open source, leading to a direct confrontation between the U.S. and China. The seminar will examine the frontier models of both countries and explain the latest technologies and important trends. It will organize points on how Japanese companies should perceive the U.S.-China AI development competition from technological and business perspectives. It will also consider strategies for companies to succeed in the AI era, including the differentiation between the U.S. and Chinese models, as well as the advantages and challenges of open source versus closed source. [Lecture Items] <1> Frontier Model (USA) <2> Frontier Model (China) <3> Technology Trends and AI Strategies for Japanese Companies <4> Q&A

[Speaker] Chief Expert Ji Hui, Future Creation Center, Nomura Research Institute, Inc. [Key Lecture Content] *We plan to update with the latest information based on field research as necessary.* At the World Artificial Intelligence Conference held in Shanghai in July 2026, Huawei unveiled its next-generation computing infrastructure products based on its proprietary AI chips, attracting significant attention. Amid ongoing U.S. export restrictions on advanced semiconductors, Huawei demonstrated its approach to overcoming the performance limits of standalone chips through "systems engineering," showcasing its commitment to leading the development of an autonomous AI ecosystem in China, encompassing computing infrastructure, cloud, OS, and AI models. Based on the latest field research results, this lecture will explain the latest trends in Huawei's AI strategy (computing infrastructure, AI models: AI social implementation, AI ecosystem, etc.) in the context of the overall landscape of China's AI industry. [Lecture Topics] 1. Current State of China's AI Industry 2. Progress in Domestic Production of Infrastructure 3. Huawei's Full-Stack AI Ecosystem 4. Future Prospects 5. Q&A

[Key Lecture Content] 1. Overview of the Law Amending Part of the Act on the Protection of Personal Information (Amendment of 2023) Kento Kido, Deputy Director, Personal Information Protection Commission 1. Background leading to the enactment of the amended law 2. Amendment Content [1] (Promotion of Proper Data Utilization) 3. Amendment Content [2] (Regulations Responding Appropriately to Risks) 4. Amendment Content [3] (Prevention of Improper Use, etc.) 5. Amendment Content [4] (Regulations to Ensure the Effectiveness of Compliance) 6. Towards the Implementation of the Amended Law (Status of Consideration for Ordinances, Regulations, Guidelines, etc.) 7. Q&A / Business Card Exchange 2. Key Points for Practical Response to the Implementation of the Amended Law Jun Okada, Partner Attorney, Mori Hamada & Matsumoto Law Firm 1. Overall Perspective on Practical Response Based on the Amended Law 2. Specific Discussion [1]: Statistical Exceptions and AI 3. Specific Discussion [2]: Handling of Children's Personal Information, etc. 4. Specific Discussion [3]: Administrative Fines 5. Specific Discussion [4]: Other Individual Issues 6. Timeline and To-Do List for Full Implementation 7. Q&A / Business Card Exchange