シュレーディンガー List of Products and Services

Organic electronics materials are required to have good optoelectronic properties and chemical stability as individual molecules, as well as desirable morphology and thermodynamic properties in the aggregated phase. The Materials Science Suite provides atomic-scale simulations applicable to these systems based on quantum chemistry, molecular dynamics, and machine learning, supporting efficient material development through the insights and theoretical interpretations obtained. *For more details, please refer to the PDF document or feel free to contact us.*

"By gaining access to Schrödinger's tools and unprecedented computational power, Panasonic Industry Co., Ltd.'s approach to innovation has changed." This article is based on an interview with Mr. Nobuyuki Matsuzawa, Principal Engineer at the Process Device Innovation Center of Panasonic Industry Co., Ltd. Please take a look. *For more details, please refer to the PDF document or feel free to contact us.*

Researchers at Panasonic are working on the novel development of organic semiconductor materials with high-efficiency characteristics. Panasonic is conducting joint research with Schrödinger, utilizing the high processing capabilities for DFT calculations, building machine learning/deep learning models, and enumerating chemical substances, leveraging the computational power and expertise provided by Schrödinger to achieve new designs of molecular materials. This catalog is a collection of case studies on "Novel Design of Hole-Conducting Molecular Materials for Organic Electronics," which Schrödinger has collaborated on with Panasonic. We invite you to read it. *For more details, please refer to the PDF document or feel free to contact us.*

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.

Molecular modeling and simulation tools have been proven effective for materials discovery and are increasingly being adopted in industrial research and development. Digital simulation significantly reduces the time required in research and development workflows compared to traditional experimental approaches, but challenges remain. Schrödinger has made it easier to address these challenges. Recently, Schrödinger developed an active learning workflow that leverages the synergy between physics-based simulations and machine learning for predicting optoelectronic properties. Recent research by Schrödinger, published in Frontiers in Chemistry and presented at SID-Display Week 2022, demonstrates an active learning paradigm for the discovery of OLED materials. *For more details, please refer to the PDF document or feel free to contact us.*

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.

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

Schrödinger provides a software platform for innovation in the development of diverse materials, including polymer materials, organic electronics, catalysis and reactivity, thin film processes, energy recovery and storage, pharmaceutical formulations, consumer goods, metals, alloys, and ceramics. By exploring vast compound spaces and predicting molecular properties with high precision, it supports the rapid design of new materials and enhances cost efficiency. This document provides an overview of the platform for materials development. *For more details, please feel free to contact us.*

Schrödinger's advanced physics-based modeling and AI/machine learning software supports the development of a wide range of materials, including polymer materials, batteries, semiconductors, organic electronics, catalysts, pharmaceutical formulations, cosmetics, metals, alloys, and ceramics.

We offer a wide range of modeling services to accelerate and support your projects at each stage of the drug discovery process. - You can leverage Schrödinger's latest technology on a large scale. - You can utilize the expertise of our computational chemistry specialist team. - This includes all licenses, computational environments, and service hours. ■ Target Functionalization Services We unlock the potential of programs to enable structure-based rigorous drug design. ■ Hit Discovery Services Using the latest virtual screening technologies, we quickly identify a more diverse range of hit compounds. ■ De Novo Design Services We rapidly generate and prioritize novel design ideas that meet project-specific conditions. ■ Structure-Based ADMET Services Through structure-based design, we more efficiently reduce risks related to ADMET. ■ Crystal Structure Prediction Services By identifying the most stable polymorph at room temperature, we reduce risks associated with solid form selection.

We support drug discovery programs by leveraging Schrödinger's unique technology and expertise. By utilizing our experts along with the latest large-scale virtual screening and rigorous scoring techniques, we lead hit discovery to success. ■ Maximize the use of Schrödinger's hit discovery capabilities There is no need for initial investments in licenses or hardware. All computational resources, licenses, and working time necessary for cutting-edge hit discovery are included in the service. ■ Acquire a greater number of high-quality hit compounds with our unique scoring technology ■ Maximize novelty and diversity through screening billions of compounds ■ Enable FEP+ execution from structural information at any level Whether starting from template-based homology models, AlphaFold models, incomplete cryo-EM structures, or experimental X-ray structures, we provide the scientific insights and computational resources needed to address your unique projects. *For more details, please refer to the catalog.

Save time and resources—leave the preparation of target protein-ligand systems for structure-based drug discovery to us. Whether starting from X-ray structures, cryo-EM structures, AlphaFold predicted structures, or homology models, Schrödinger's scientists will utilize their unique technologies and expertise to optimize and validate protein-ligand complexes for highly accurate structure-based design. ■ Create designable structures that are on-target or off-target ■ Complement your unique insights on targets with our computational chemistry Schrödinger provides extensive expertise gained from addressing challenging targets in structure-based drug discovery using FEP+. ■ We can accommodate starting from template-based homology models, AlphaFold models, incomplete cryo-EM structures, or experimental X-ray structures. *All computational resources, licenses, and working hours necessary for comprehensive evaluation of target proteins and the construction of accurate structural models are included in the service. *For more details, please refer to the catalog.

- Maximize the use of Schrödinger technology: The service includes all necessary computational resources, licenses, and working hours to conduct comprehensive simulations tailored to research and development needs. - Provide flexible and customizable solutions for project success: Your software, hardware, and computational resources are not required. During and after the project, you will receive knowledge transfer and training from our experts. We design and provide tools and solutions according to your objectives. - Complement your expertise with Schrödinger's specialized capabilities: After jointly defining the scope of deliverables, experts in materials applications and digital simulations will carry out the project.

Machine learning force fields, also known as "machine learning interatomic potentials," have emerged as an important tool for achieving cost-effective atomic-level simulations of diverse chemical systems, often achieving accuracy comparable to density functional theory (DFT) at significantly lower computational costs. Recent advancements in message-passing networks have overcome the challenge faced by traditional MLFFs of being limited in the types of elements they can accommodate. Furthermore, the introduction of atomic charges and electrostatic interactions using charge equilibration methods allows for precise reproduction of multiple charge states, ionic systems, and electronic response characteristics, achieving even higher accuracy by explicitly considering long-range interactions. Our MLFF architecture, "MPNICE," incorporates explicit electrostatics for accurate charge representation. We also provide a set of pre-trained models trained on materials covering the entire periodic table (89 elements). MPNICE emphasizes high throughput performance, enabling long-duration and large-scale atomic-level simulations that were difficult to achieve with traditional methods while maintaining high accuracy.

We will be exhibiting at nano tech 2026 (International Nanotechnology Exhibition and Conference). With the advancement of molecular simulation technology, it has become possible to handle nano-level phenomena through computer simulations. Additionally, the combination of simulations and machine learning is expanding the range of possibilities. Schrödinger will support the enhancement of analytical capabilities, increased efficiency, and reduced development time in your material development using the latest molecular simulation and machine learning technologies, as well as techniques that integrate both. At our exhibition booth, specialized technicians will explain advanced technologies and answer questions from visitors. *During the event, we will hold a seminar at our booth. Theme: "Seeing Inside Materials at the Nano Level - New Material Development through Simulation and Machine Learning" We will introduce examples that enable the prediction of physical property values of various materials on a computer. Furthermore, you will have the opportunity to experience each software firsthand. We look forward to your visit.

We will be exhibiting at nano tech 2026 (International Nanotechnology Exhibition and Conference). With the advancement of molecular simulation technology, it has become possible to handle nano-level phenomena through computer simulations. Additionally, the combination of simulation and machine learning is expanding the range of possibilities. Schrödinger supports the enhancement of analytical power, increased efficiency, and shortened development periods in your material development using the latest molecular simulation and machine learning technologies, as well as their integration. At our exhibition booth, specialized technicians will explain advanced technologies and answer questions from visitors. *During the event, we will hold a seminar at our booth. Theme: "Seeing Inside Materials at the Nano Level - New Material Development through Simulation and Machine Learning" We will introduce examples of predicting physical property values of various materials on a computer. Furthermore, you will have the opportunity to experience each software firsthand. We look forward to your visit.