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

[Lecture Topics] 1. Trends in Policies for Decarbonization of Housing and Buildings Tetsuya Hirayama, Deputy Director, Housing Bureau, Ministry of Land, Infrastructure, Transport and Tourism (in charge of architectural planning) 2. Construction Circular Economy - Expanding Horizontal Recycling for Achieving Sustainable Construction Takahiro Moritomi, Analyst, Social Infrastructure Team, Next Generation Infrastructure and Services Office, Industry Research Department, Mizuho Bank, Ltd. 3. Beyond "Scrap and Build" Aiming for the Realization of "Circular Design Build®" Shoichi Fukui, Senior Chief Expert, Sustainability Promotion Department, Management Planning Office, Takenaka Corporation

[Instructor] Attorney Kurato Suzuki, Irokawa Law Office [Key Lecture Content] Due to the amendments to the Comprehensive Promotion Law for Labor Policies, starting from October 1, 2026, employers will be required to take necessary measures in employment management regarding customer harassment (Kasu Hara) and sexual harassment against job seekers (Job Hunting Sexual Harassment). In line with this, companies will need to prepare for practical implementation before the enforcement date, based on the guidelines published by the Ministry of Health, Labour and Welfare. This includes developing internal regulations, establishing consultation and response systems, raising awareness within the company, and determining judgment and response methods on-site. This seminar will organize the setting of job hunting rules related to job hunting sexual harassment, internal awareness, and key points for regulatory development. It will also explain the distinction between legitimate complaints and customer harassment, judgment, documentation, and response on-site, the establishment of a company-wide system, regulatory development considering dispute risks, and points that are easily overlooked. The Human Resources, Labor, Legal, and Compliance departments will systematically organize the response matters that need to be confirmed in preparation for the enforcement on October 1, 2026, from the perspectives of guideline compliance and on-site operations. [Lecture Items] 1. Job Hunting Sexual Harassment 2. Customer Harassment 3. Q&A

[Speaker] Mr. Itsuki Wakayama, Project General Manager, Technology Promotion Unit, Low Carbon Solutions Division, INPEX Corporation [Key Lecture Content] The greatest advantage of synthetic methane production through CO2 methanation technology is that it enables decarbonization without significant additions or modifications to all existing natural gas and city gas infrastructure. At INPEX, after two NEDO commissioned projects, we have been conducting the development of practical application technology with a capacity of 400 Nm3-CO2/h, one of the largest in the world, as a subsidized project by NEDO since the fiscal year 2021. By the fiscal year 2025, we aim to achieve the injection of synthetic methane produced during the demonstration operation of the test facility. This presentation will provide detailed information on the results of the NEDO project for the fiscal year 2025, including the current status of the latest test facilities. [Presentation Items] 1. INPEX's efforts towards net zero by 2050 2. Policy trends related to CO2 methanation 3. Challenges of CO2 methanation 4. Results of the NEDO practical application technology development project with a capacity of 400 Nm3-CO2/h for the fiscal year 2025 5. Future commercialization prospects and challenges 6. Q&A / Business card exchange

[Speaker] Mr. Satoru Shimizu, President and CEO of Okunote Inc. [Key Lecture Content] Generative AI is not a "magic wand," but a "mirror" that reflects the questions of the user. In this lecture, based on my book "The 'Super' Planning Techniques Practiced by Competition Disruptors: Seven Creative Steps to Lead Winning Strategies with the 'Art of Questioning'," I will explain how to overcome the challenges of proposals being rejected, not moving forward within the company, and not being executed on-site through interactions with AI. I will introduce the entire process of planning, linking it with AI utilization, from question design to hypothesis destruction, structuring materials from the decision-maker's perspective, forming internal consensus, managing execution, and turning reflections into assets. In the latter part of the workshop (exclusive to on-site participants), you will actually formulate questions and refine your dialogue with AI, taking home usable "art of questioning" techniques for tomorrow. [Lecture Items] 1. Orientation: AI is a "mirror," not a "magic wand." 2. Enhancing the precision of planning through "question design" and "hypothesis destruction." 3. Structuring materials from the decision-maker's perspective to create "acceptable" proposals. 4. Forming consensus that transcends internal barriers. 5. Mechanisms that prevent execution from stopping and turning experiences into assets through reflection. 6. Habitualizing AI as a "partner" and the roles humans will take on in the AI era.

【Exhibition Overview】 A collection of products related to data centers, which are at the core of AI infrastructure, gathered in one place.