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

We will be exhibiting at the "P-MEC Japan 2025 Pharmaceutical Ingredients and Equipment Exhibition" held at Tokyo Big Sight. During the event, we will showcase various products. We invite all relevant parties to come and visit us. 【Suppliers】 ・LAUDA

Seiko Co., Ltd. will be exhibiting at the "11th Monozukuri World Nagoya" held at Port Messe Nagoya. This exhibition features approximately 630 companies showcasing advanced technologies that contribute to shortening development and manufacturing periods, promoting DX and IT, reducing costs, and enhancing energy efficiency and automation in factories, including IT and DX products, parts, equipment, devices, and measurement products. We plan to display the actual cabinet for equipment management, "SMART BEE," so please be sure to visit us. 【Model Numbers】 ■ Vending Machine Type Tool Management Cabinet Smart Bee X, Smart Bee S, Cute Bee, Smart Bee Pro ■ Locker Shelf Type Tool and Consumables Management Cabinet T Series: T57, T100, T108 TW Series (with electronic scale): TW45, TW68, Hyper S, Hive

We will be exhibiting at "Japan DX Week Spring 2026," which will be held at Tokyo Big Sight from April 8 (Wednesday) to April 10 (Friday). At the Marble booth, we plan to showcase how AI can improve work efficiency and lead to business automation in on-site operations. Additionally, we will provide an opportunity to experience a demonstration utilizing the latest technology, AR. Furthermore, for our manufacturing industry customers, we will present proposals under the theme "Utilizing Drawings to Drive DX," promoting the digitalization of design and quality areas, and introducing packaged products and custom services aimed at enhancing productivity in the manufacturing sector.

Core systems are the foundational support for corporate management, and their importance is increasingly heightened in the DX era, where data becomes a source of competitive advantage. To respond swiftly and flexibly to the rapidly changing business environment, a transformation in the nature of systems is required. We will introduce a method to realize a highly flexible and scalable business system that promotes operational efficiency and data utilization through the combination of kintone and ASTERIA Warp, using examples and demonstrations.

The widespread adoption of generative AI is rapidly evolving from traditional "conversational AI" to "autonomous execution AI (AI agents)." The AI agents that have recently garnered attention are not limited to merely generating responses; they can incorporate an autonomous processing flow of "planning → task decomposition → execution → evaluation," serving as new partners that can substitute and expand users' business operations. In this seminar, we will clearly introduce the following topics, incorporating examples of the AI solution "AIdea Suite AGENT +plus" and the data integration tool "ASTERIA Warp": - Why "AI agents" are becoming essential for corporate competitiveness now - The mechanisms of AI agents and specific use cases - Methods for establishing business efficiency, creating new value, and gaining a competitive edge - Specific steps for implementing AI agents - Data integration and its methods, which are essential for utilizing AI *This is a re-broadcast of the seminar held on October 30, 2025 (Thursday).