Case Studies
- Jul 11, 2025
Advancing sustainable food processing through integrated experimental and molecular simulation approaches
Scientists from Schrödinger and UMass carried out comprehensive studies experimentally and computationally to investigate the key properties and extrusion performance of zein-formulated meat alternatives.
Documentation
- Documentation
Machine Learning Force Fields
Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.
- Documentation
MS Transport
Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity and diffusions of atoms and molecules.
Events
- Aug 17th-21st, 2025
ACS Fall 2025
Schrödinger is excited to be participating in the ACS Fall 2025 conference taking place on August 17th – 21st in Washington, D.C.
- Aug 19th-22nd, 2025
IMID 2025
Schrödinger is excited to be participating in the 25th International Meeting on Information Display conference taking place on August 19th – 22nd in Busan, Korea.
- Aug 25th-29th, 2025
5th Summer School on Cheminformatics 2025
Schrödinger is excited to be participating in the 5th Summer School on Cheminformatics 2025 conference taking place on August 25th – 29th in Hamburg, Germany.
Training Videos
Getting Going with Materials Science Maestro Video Series
A free video series introducing the basics of using Materials Science Maestro.
Schrödinger’s Materials Science Builder Series: Disordered System Builder
The video demonstrates how to use the Disordered System Builder within Schrödinger’s Materials Science Suite to prepare systems for molecular dynamics simulations.
Schrödinger’s Materials Science Builder Series: 2D Sketcher
This video demonstrates how to use the 2D Sketcher within Schrödinger’s Materials Science Maestro for building and editing molecules, covering its toolbar, drawing area, and various palettes.
Publications
- Publication
- May 9, 2025
Efficient long-range machine learning force fields for liquid and materials properties
Weber JL, et al. arXiv, 2025, Preprint- Publication
- Mar 17, 2025
Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures
Chew, et al. npj Computational Matererials, 2025, 11, 72- Publication
- Mar 5, 2025
A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study
Zhou, et al. Nature Communications, 2025, 16, 2210Quick Reference Sheets
- Quick Reference Sheet
Coarse Grained Mapping
Get an overview of the Coarse Grained Mapping panel for mapping all-atom structures to coarse grained models.
- Quick Reference Sheet
Visualize Restraints
Get an overview of the Visualize Restraints panel for displaying restraints in a cms structure.
- Quick Reference Sheet
ML Model Manager
Get an overview of the ML Model Manager for organizing and retraining outdated ML models.
Tutorials
- Tutorial
Introduction to Materials Science Maestro Tutorial
An introduction to Materials Science Maestro, covering basic navigation, an intro to building models and several of the key functionalities of the graphical user interface.
- Tutorial
Disordered System Building and Molecular Dynamics Multistage Workflows
Learn to use the Disordered System Builder and Molecular Dynamics Multistage Workflow panels to build and equilibrate model systems.
- Tutorial
Introduction to Geometry Optimizations, Functionals and Basis Sets
Perform geometry optimizations on simple organic molecules and learn basics regarding functionals and basis sets.
Webinars
- Sep 2nd-16th, 2025
Innovations in Digital Chemistry: Computational Approaches for Drug & Materials Discovery
This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.
- Sep 18, 2025
Advancing machine learning force fields for materials science applications 最新機能 MPNICEのご紹介
シュレーディンガーが開発した最先端のMLFFアーキテクチャ「MPNICE(Message Passing Network with Iterative Charge Equilibration)」をご紹介します。
- Aug 6, 2025
Advancing machine learning force fields for materials science applications
In this webinar, we will introduce Schrödinger’s state-of-the-art MLFF architecture, called Message Passing Network with Iterative Charge Equilibration (MPNICE), which incorporates explicit electrostatics for accurate charge representations.
White Papers
Latest insights from Extrapolations blog
Training & Resources
Online certification courses
Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.
Free learning resources
Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.