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

The particle visualization technology developed under our brand "ViEST" has a very high level of detection sensitivity, capable of visualizing the suspended or adhered states of micro and nano-sized particles in real time. We are engaged in the sale of visualization systems and contract services for evaluation (investigating particles and airflow in production processes, inside and outside manufacturing equipment, evaluating the performance of cleaning products, proposing yield improvement measures, etc.) both domestically and internationally. In manufacturing environments, invisible fine foreign substances, dust generation, particle adhesion due to static electricity, and airflow disturbances can cause quality defects and reduced yield. Our company clarifies these contamination factors through visualization technology and provides consistent support from identifying the source of the problem to considering and confirming improvement measures. Do you have any of the following concerns? - Unable to identify the cause of foreign matter contamination - Want to check airflow and cleanliness in clean rooms - Want to understand dust generation from manufacturing equipment and transport systems - Want to reduce particle adhesion to films and substrates - Want to improve contamination that leads to reduced yield

We will introduce the modeling features that utilize drawings through "2D×3D integration," which allows for easy creation of 3D models from 2D drawings, as well as an efficient integration function that projects 3D assembly data onto assembly and part drawings in bulk, automatically generating 2D drawings. Additionally, you will be able to see through a live demo how seamlessly utilizing 2D and 3D can lead to improved design efficiency, reduced errors, and increased overall productivity in business operations. This content is recommended for those considering the introduction of CAD or feeling challenges in their current design work. We hope you will join us.

[Key Lecture Content] 1. Outlook on Renewable Energy Policy Centered on Solar Cells Ministry of Economy, Trade and Industry Agency for Natural Resources and Energy Energy Conservation and New Energy Department New Energy Division, Deputy Director Akihiro Kanayama 1. Trends in Renewable Energy Policy 2. Towards the Introduction of Perovskite Solar Cells 3. Regarding the "Law on Promoting the Recycling of Solar Cell Waste, etc." Established in May of This Year 4. Q&A / Business Card Exchange 2. Potential of the Solar Panel Disposal Business from the Perspective of Solar Power Generation Equipment ~Verifying What Can Be Done Now at a Practical Level~ Environmental Energy Recycling Center, Executive Director Yosuke Anada 1. Market Trends of Solar Power Generation Equipment and Movements Regarding Disposal 2. What is Happening Now with Solar Power Generation Equipment? How Does Disposal Occur? 3. Businesses Emerging and Activating Due to Mandatory Recycling 4. What Should Be Considered and Done Now 5. Q&A / Business Card Exchange

[Instructor] Koichi Sato, Representative Partner / Certified Social Insurance and Labor Consultant, HR Plus Social Insurance and Labor Consultant Corporation [Key Lecture Content] Recently, approximately 40% of the labor force consists of non-regular employees, and the declining birthrate is progressing due to insufficient income. In response, the government has been making successive legal revisions, including the indefinite conversion rule and the so-called "equal pay for equal work." Additionally, against the backdrop of a recent labor shortage, there has been an increase in cases of employing contractors and freelancers, which has raised the risk of disguised contracting due to inappropriate practices. This course will touch on the contents of the "Equal Pay for Equal Work Guidelines," which are scheduled to be revised in October this year, and will also organize the latest "current situation" for each employment category of non-regular employees.

In recent years, have you noticed an increase in corrective recommendations regarding "free storage of molds"? Cases where molds are stored for long periods without orders or where usage records cannot be confirmed are increasingly viewed as problematic from the perspective of the Subcontract Act (Act on the Proper Transaction of Small and Medium-Sized Enterprises). In fact, many companies may feel uneasy about management situations such as "not knowing where they are" or "not knowing when they were last used." Therefore, we will hold a webinar to explain practical methods for managing loaned molds as a response to these corrective recommendations. ✅ Recommended for: - Those who feel uneasy about the management status of loaned molds - Those who want to strengthen operations in preparation for legal compliance (Subcontract Act) - Those who have challenges in establishing mold management rules with business partners - Those who want to visualize the location, usage history, and evidence management of molds In this webinar, we will not only introduce management methods but also clearly explain how to advance mold management at a practical operational level. Now is the perfect opportunity to prepare for risks while you still think "it's okay." If you have any interest, please join us.