- Explore the mechanical, magnetic, and dielectric properties of organic and inorganic materials, their surfaces, and interfaces
- Establish relationships between structure, composition, dimensionality, and key materials parameters
- Elucidate diffusion, segregation, and intercalation reaction mechanisms
- Reveal the structure of grain boundaries and dislocations
- Evaluate factors affecting thermodynamic stability
- Uncover effects of doping and point defects in semiconductors
- Understand phase diagrams and mechanisms for phase transformations
Intuitive computational workflows designed by experts in inorganic materials
Easy-to-use system builders for all inorganic material types
Powerful workflows for molecular simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources
Online certification course: Level-up your skill set in materials surface modeling
Learn how to apply industry-leading computational software to predict key properties of reaction in solids and on surfaces for bulk crystals and inorganic solids with automated workflows and machine learning models.
- Self-paced learning content
- Hands-on access to Schrödinger software
- Guided and independent case studies
Learn more about the key computational technologies available to progress your research projects.
Quantum ESPRESSO GUI
Integrated graphical user interface for nanoscale quantum mechanical simulations
Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties
Automated, scalable solution for the training and application of predictive machine learning models
Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.
Benchmarking Machine Learning Descriptors for Crystals, Aditya Sonpal
Afzal. A et al. Machine Learning in Materials Informatics: Methods and Applications, Chapter 6pp 111-126
Structural investigation, quantum chemical calculation, energy framework analysis and MIC studies of silver and cobalt complexes of 4-amino-N-(4, 6-dimethyl-2 pyrimidinyl) benzenesulfonamide in presence of secondary ligand
Socha B.H. et al. Inorganic Chemistry Communications Volume 154, August 2023, 110936
Software and services to meet your organizational needs
Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.
Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.
Support & Training
Access expert support, educational materials, and training resources designed for both novice and experienced users.