We will be exhibiting at the "Spring Academic Conference of the Applied Physics Society" from March 22 (Tuesday) to March 26 (Saturday).

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.)

A quick solution to your materials informatics problems! The evolved AI platform LiveDesign accelerates new material development.

Do you have any of the following concerns in materials informatics? Schrödinger's LiveDesign solves these issues and accelerates MI, from data recording, completion, automation of machine learning, to sharing analytical methods and results. 【Concern 1】 Data quality issues: Formats and terminology are inconsistent ➡ We register data in a unified language on the same spreadsheet. 【Concern 2】 Data quantity issues: The data is full of missing values ➡ We complete the data using physical chemistry calculations and machine learning. 【Concern 3】 Undemocratic AI, machine learning, and analytical methods: Not knowing where to start ➡ We automatically generate suitable machine learning models while accumulating data, creating high-precision models without relying on computational chemists. 【Concern 4】 Internal sharing issues: A good predictive model has been created, but there is no system to deploy it internally ➡ Analytical methods and results can be shared within a group via a web interface. *For more details, please feel free to contact us.

• Embedded OS

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

[Instructor] Paulstar Communications Inc. Representative Director Koji Kita [Key Lecture Content] Have you ever felt that, despite entrusting work to Generation Z and young employees, they "don't seem to get started," "don't lead to the expected results," or "it's ultimately faster to do it myself"? Behind these situations are various factors, including the recognition gap between supervisors and young employees regarding "entrusting and being entrusted," how to communicate the purpose of the work, the approach, and the scope of discretion, as well as how to follow up after delegating tasks. In this seminar, we will practically explain "how to delegate work" to elicit proactive behavior, taking into account the perspectives and behavioral characteristics of Generation Z and young employees. We will share specific management techniques to help supervisors break free from the burden of work, encourage the autonomy and growth of young employees, and steadily lead to results.

[Speaker] Yoichiro Okuyama, Senior Researcher, Research Department, Hamagin Research Institute, Inc. [Key Lecture Content] In China, the operation of autonomous taxis began in 2021, and now fully unmanned robo-taxis are running in cities like Beijing and Shenzhen. Experiencing a ride reveals the evolution of technology through natural acceleration, smooth steering, and seamless deceleration. Currently, there are several thousand vehicles in operation, but projections suggest that by 2030, this number could exceed one million, accounting for 70% of the global total. What are the underlying factors driving the social implementation of robo-taxis in China? By incorporating local test ride experiences, we will analyze the revenue situation of robo-taxi operating companies and the outlook for market expansion based on data, exploring their future potential. [Lecture Items] 1. Personal experiences of test riding robo-taxis in China 2. Trends in the market size of robo-taxis in China and globally 3. Background of the advancing social implementation of robo-taxis in China 4. Public acceptance and actual usage 5. Performance and challenges of major robo-taxi companies 6. Will robo-taxis become widespread in Japan as well? 7. Q&A / Business card exchange

Daitō Shikō Co., Ltd. will exhibit at the "102nd Tokyo International Gift Show Autumn 2026," which will be held at Tokyo Big Sight from September 2 (Wednesday). At the exhibition venue, we plan to showcase new products including the A7 binder, popular foil-stamped goods, stationery, clear files, and more. If you are looking for SP tools, promotional items, or novelties, please be sure to visit our booth.

Our company will participate in the 1st High-Performance Materials Exhibition (within High-Performance Materials Week) as a member of the MGC Group. We will be showcasing various functional polycarbonate resins that can serve as alternatives to metals and ceramics, so please be sure to visit our booth.

In the health and medical fields, which are directly connected to people's lives, it is essential to carefully interpret the results of data analysis and utilize them in decision-making. This seminar will first introduce basic analytical methods using JMP 19 and explain the contexts in which each method can be applied. Next, we will discuss the thought process for making causal interpretations, such as "Did a certain treatment improve the condition?" and how to estimate causal effects using JMP Pro 19. - Comparison of results before and after treatment using paired t-tests - Group comparisons using t-tests - Group comparisons considering pre-treatment values through analysis of covariance - Distinguishing between correlation and causation - Estimation of causal treatment effects This seminar will introduce these basic analytical methods with demonstrations. Those without statistical expertise and those unfamiliar with JMP are also very welcome. ▼ Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/09-11-health-medical-data