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 would like to express our congratulations on the continued prosperity of your esteemed company. Our company will be exhibiting at the following trade show, where we will be showcasing a number of products, including our new product, the Packaging Container Air Leak Test (Pin Hole Inspection) Device | MSQ-2003 series. We sincerely apologize for the inconvenience during your busy schedule, but we would like to cordially invite you to attend this event. 【 Trade Show Name 】 【 Dates 】 【 Venue/Booth Number 】

Recently, we held technical training sessions for the MilkoScan FT3 at five locations nationwide. In this training, we provided a wide range of useful information for operating the equipment, including explanations of the principles and structure of the device, how to create measurement products, daily maintenance, and how to respond to issues when they arise. Additionally, this year we introduced new methods for utilizing digital services (such as FossCalibrator and IQX) and also prepared hands-on sessions where participants could experience operating the equipment using a PC. Thank you to everyone who participated. Please feel free to contact us with any questions after the training. ------- At Foss Japan, in addition to training sessions and workshops to ensure you can use our equipment with confidence, we hold many events throughout the year. In seminars and open houses tailored to specific industries and analytical techniques, we showcase Foss's latest analytical solutions. -------

We will be exhibiting at the "Monozukuri Fair 2026" held at Marine Messe Fukuoka from October 14 (Wednesday) to 16 (Friday). We will have a working model of a "small winch" powered by an electric driver equipped with our uniquely developed self-locking reduction mechanism "shoji-lock," and we will set up a corner where you can experience the actual lifting and lowering of workpieces. Please stop by. (Hall A, Booth Number AS-57)

[Lecturers] EY Strategy and Consulting Co., Ltd. Infrastructure Advisory Partner Kenichiro Fukuda Strategy Partner Ken Fujiyama Infrastructure Advisory Director Takashi Matsumura [Key Lecture Content] The water supply and sewage business is facing structural challenges such as facility aging, a shortage of workforce, and a decrease in revenue due to population decline. The government is promoting the introduction of Water PPPs, regional consolidation, and the digital transformation of water supply and sewage systems, marking a significant turning point in the way these businesses are operated. This change is expanding business opportunities for private companies in various areas, including operation and maintenance, pipeline renewal, digital technology, and the utilization of smart meters and data. Additionally, as a means of expanding business domains and enhancing competitiveness, there has been an increase in capital and operational partnerships and M&A between companies, leading to industry restructuring. In this seminar, based on the latest trends in policy and the market, we will introduce business strategies for private companies to enter and expand in the water supply and sewage market, including the use of M&A and capital operational partnerships, drawing on achievements from both public and private support.

[Lecturer] Koichi Iwama, Professor, Faculty of Economics and Management, Wako University / Chairman, Resource Economics Committee, Petroleum Technology Association [Key Lecture Content] In light of the prolonged crisis in the Strait of Hormuz, what will happen to the development of shale gas and shale oil in the United States moving forward? After the midterm elections, what will be the international LNG prices under the Trump administration after winter 2027? How will Japan's business strategies related to LNG projects grow? A leading expert on the shale gas and shale oil revolutions will clearly explain the future outlook.