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

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.

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 in 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 mechanism 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.

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.

We will clearly introduce Schrödinger's integrated platform that supports the development/analysis of semiconductors and related technologies. 【Product Overview】 ■ Prediction and analysis of semiconductor physical properties using quantum mechanical calculations - Electronic properties - Mechanical properties (elastic constant tensor, bulk modulus) - Dielectric properties - Reaction pathway exploration ■ Optimization of semiconductor film deposition processes (CVD, ALD, ALE) - Development of new precursors using quantum mechanical calculations and machine learning ■ Optimization of semiconductor packaging using classical molecular dynamics calculations - Construction of cross-linked structure models for resin encapsulants - Prediction of heat resistance through calculations of glass transition temperature - Prediction of gas barrier properties through calculations of absorption rates and diffusion coefficients of water and gas molecules - Analysis of physical property changes during the absorption of water/gas molecules *For more details, please refer to the PDF document or feel free to contact us.

We would like to introduce Schrödinger's integrated platform that supports the development and analysis of next-generation battery materials. 【Product Features】 ■ Analysis of ion behavior within electrodes through quantum mechanical calculations ■ Analysis of the conduction mechanism of Li+ ions in polymer electrolytes using molecular dynamics simulations ■ Development of electrolytes through molecular simulations and machine learning *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.*

In the Schrödinger materials science reaction workflow, automatic exploration of the conformational space allows for the coverage of often-overlooked conformers. Furthermore, the automation of quantum chemical calculations eliminates the challenging processes that require meticulous maintenance of hundreds of files and properties, as well as specialized training. This simplifies the workflow and enhances reproducibility and predictability. [Case Study] ■ Diels-Alder Reaction *For more details, please refer to the PDF document or feel free to contact us.

As the speed of drug discovery accelerates, the rapid and efficient preformulation and formulation of new drugs has become a crucial element in pharmaceutical development. Advances in atomic-scale modeling and simulation techniques have made it possible to conduct in silico screening of numerous candidate materials and formulations based on complete physics-based models. [Case Studies] - Stability of drugs against chemical degradation - Compatibility of pharmaceutical ingredients - Thermophysical stability based on glass transition temperature - Controlled release: Supramolecular structures in formulation *For more details, please refer to the PDF document or feel free to contact us.

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.

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

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.

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.

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.

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.*

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.

Eonix is a startup focused on the rapid design of next-generation materials for energy storage technologies targeting home appliances, grid storage, and electric vehicles. CEO Don DeRosa, Ph.D., explains how combining high-throughput screening and physics-based modeling can transform the material discovery process for building better batteries.

Machine-learned force fields (MLFF) are designed to improve traditional force fields by incorporating machine learning models to accurately model interactions between atoms and molecules. This technology is based on neural network potential energy surface (NN-PES) architecture, and the model is trained to reproduce the total electronic energy of the system with chemical accuracy. With the combination of OPLS4 for initial structure generation, fast DFT and MD engines, and key MLFF methods, Schrödinger has become a leading partner in MLFF generation. This application note introduces the application of QRNN technology in modeling across three different areas of materials science: liquid electrolytes, polymers, and ionic liquids.

L'Oréal, the world's number one cosmetics company, has gained a deeper understanding of the differences in shear behavior between synthetic polymers and polysaccharide polymers on biomimetic surfaces by utilizing Schrödinger's software. • New insights into the aggregation behavior of shampoo formulations were obtained using simulated hair surfaces. • The influence of polymer topology was demonstrated, linking observed polymer interactions to experimentally observable phenomena. • A framework was established for studying complex formulations in contact with biomimetic surfaces using molecular dynamics simulations. • The design of eco-friendly cosmetic formulations was rationally accelerated.

Schrödinger, Inc. will hold a webinar for materials science on February 19 (Wednesday) titled "Virtual testing of personal care and cosmetics formulations using digital chemistry methods." The development of sustainable products faces many challenges, requiring time, resources, and new raw materials. Predictive modeling is gaining attention to streamline this process. It allows for the identification of promising ingredients and formulations that meet standards, as well as new packaging materials, providing molecular-level understanding through virtual testing using computational methods. Specifically, it enables the analysis of the behavior of individual components, the form of formulations, stability, and interactions with biological surfaces. Additionally, it allows for the exploration of interactions between products and packaging materials, helping to identify factors that significantly affect shelf life. In this seminar, we will demonstrate through case studies how computational chemistry can assist in product development, container design, and analysis during product use. We invite you to join us without hesitation.

Schrödinger provides a powerful and user-friendly integrated software solution for the research and development of consumer goods. Schrödinger's platform is designed for a wide range of users, from beginners to experts in computational chemistry, offering a simple workflow to build, simulate, and analyze real systems using advanced physics-based modeling and machine learning technologies. ■ Accurately predicts key physical properties such as the glass transition temperature (Tg) of amorphous amylose polymers in both wet and dry states. ■ Effectively models water absorption and transport by investigating the impact of moisture content on Tg and the diffusion of water within starch polymers. ■ The OPLS3e force field provides high accuracy for amorphous starch models. ■ Detailed studies of the interactions between water and amylose, along with further research on the effects of components on complex starch formulations.

"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.*

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.*

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.

Schrödinger provides a powerful and user-friendly integrated software solution for the research and development of consumer goods. Schrödinger's platform is designed for a wide range of users, from beginners to experts in computational chemistry, offering a simple workflow to build, simulate, and analyze real systems using advanced physics-based modeling and machine learning technology. Here, we introduce Schrödinger's applications for consumer goods research and development. ■ Food and Beverage ■ Cosmetics and Personal Care ■ Cleaning Agents ■ Packaging Materials ■ Materials Informatics

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.