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

[Lecturer] NTT Corporation, Network Service Systems Research Institute Network Infrastructure Technology Project, Project Manager Fujihiko Tamura [Key Lecture Content] The All-Photonics Network (APN), which is a core component of the IOWN concept proposed by NTT in 2019, is a high-capacity, low-latency, and low-power optical infrastructure that began commercial service as APN IOWN 1.0 in 2023. Currently, it is offered as a bandwidth-guaranteed communication service between user sites at speeds of up to 800 Gbps, and APN is steadily evolving. In the future, to respond to the rapid development of AI and data centers, as well as the diversifying user needs, we are advancing research and development for APN Step 3, which aims to provide on-demand, end-to-end optical connections on a broad scale. In this lecture, I will explain the world that APN Step 3 aims for, the technologies that support its realization, and our efforts in standardization activities and demonstration experiments at IGF (IOWN Global Forum) and ITU-T.

[Speaker] Hajime Hiraki, CEO of Chemistry Cube, Innovation Consultant [Key Lecture Content] Inventorying technology is not merely about listing and categorizing technologies or creating a technology inventory; in other words, it is not just about managing technology stock. It is a process that visualizes technology as a "structure" and reconstructs the researcher’s own thinking, fundamentally enhancing the strategic planning and theme creation capabilities in R&D settings. In this lecture, we will explain how technology generates value and how it can be utilized across organizations using the technology structuring method iMapR practiced by Chemistry Cube. We will also discuss how the speed and quality of inventorying can be improved through collaboration between generative AI and engineers/researchers. This is essential content for executives and technology leaders who want to accelerate transformation in R&D. [Lecture Items] 1. Introduction 2. What is technology inventorying? 3. How technology inventorying changes R&D settings 4. Practical application of the technology structuring method iMapR 5. Technology inventorying in the era of generative AI 6. Towards deployment in R&D settings 7. Q&A / Business card exchange

Announcement of a workshop hosted by Fukushima Prefecture High-Tech Plaza. Creating the Future of Manufacturing: Problem-Solving Workshop ~ Learn approaches to identify and solve on-site challenges ~ Are you interested in generative AI but haven't taken the plunge yet? Let's take that step together. We will hold an intensive workshop where you can learn the basics of "safely, smartly, and practically" using AI and problem-solving techniques in just 2.5 hours. <Recommended for> - Those who want to eliminate daily "minor inconveniences" and "vague inefficiencies" with AI - Those who want to quickly catch up on the basics of generative AI and safe usage - No restrictions on industry or job type. Those who want to work hands-on in teams and experience concrete outputs <What you will gain> - Experience a problem-solving framework starting from problem discovery → definition → visualization → solution proposals based on on-site challenges - Acquire the basics of generative AI prompts and risk management (copyright, confidentiality, hallucination) from a practical perspective - Take home improvement ideas that can be used starting tomorrow through group work <What to bring/wear> No specific items required. Dress is casual. Please feel free to join us. We sincerely look forward to your participation!

Ladies and gentlemen, do you have any concerns about using Excel for data aggregation and report creation in your daily work? - Spending hours every month matching multiple Excel files through copy-pasting or VLOOKUP - When opening large datasets, your PC freezes, and you have to wait until the processing is complete - The macros you've created are too complex for anyone other than the creator to fix (dependency on individuals) Alteryx is generally recognized as a "self-service data analytics platform" and a "no-code/low-code ETL tool." While the first benefits that come to mind when implementing Alteryx may be efficiency and automation, the advantages of adopting Alteryx go beyond just that. In this seminar, we will introduce how Alteryx can streamline and automate the tasks you struggle with daily in Excel, and how it can help you step up to more advanced analysis, along with specific use cases.

[Speaker] Tadahiro Maruyama, Director of the Business Creation Division, Chubu Electric Power Co., Inc. (Also Deputy COO of Chuden Telemetering LLC) [Key Lecture Content] Chubu Electric Power is expanding telemetering through a smart meter communication network to gas and water services. In the next phase, we aim to integrate these accumulated data into a multi-infrastructure DX business, merging with energy and water services, and striving for a circular and sustainable community across our entire group. This time, I will explain the actual initiatives related to these efforts. [Lecture Items] 1. Automatic Meter Reading by Chubu Electric Power 2. Current Status of Chubu Electric Power's Telemetry Business 3. Business Transformation through Infrastructure DX in the Electric Power Sector 4. Horizontal Expansion of Infrastructure DX to Water Services 5. Smart City Services through Data Utilization 6. Q&A / Business Card Exchange