- Tutorial
Calculating Reaction Energetics for Molecular Systems
Perform geometry optimizations and frequency calculations to predict the thermodynamics of a sample Diels-Alder reaction.
- Tutorial
Meta Workflow
Learn to use the Meta Workflow Builder to string together multiple workflows for automating user-specific projects.
- Tutorial
Machine Learning Property Prediction
Learn to use pre-built machine learning models to predict polymer properties and volatility for organic and organometallic molecules.
- Tutorial
Cheminformatics Machine Learning for Homogeneous Catalysis
Learn to develop and use a machine learning model to predict reaction rate constants for iridium catalysts.
- Tutorial
Machine Learning for Sweetness
Learn to use the DeepAutoQSAR panel to predict whether a molecule is sweet by machine learning methods.
- Tutorial
Machine Learning for Ionic Conductivity
Generate descriptors for ionic liquids which can be used to build machine learning models.
- Tutorial
Molecular Dynamics Descriptors for Machine Learning
Generate descriptors using molecular dynamics simulation, which can be used to build machine learning models.
- Tutorial
Polymer Descriptors for Machine Learning
Generate descriptors for polymers which can be used to build machine learning models.
- Tutorial
Organometallic Complexes
Learn tools for building and enumerating organometallic complexes, as well as for performing and analying geometry optimizations for organometallic complexes.
- Tutorial
Optoelectronics Active Learning
Learn to predict optoelectronic properties using active learning models for a series of iridium complexes.
- Tutorial
Calculating Voltage Curves of Spinel Intercalation Compounds
Learn to generate intercalation voltage curves.
- Tutorial
Activation Energies for Reactivity in Solids and on Surfaces
Learn to model the transition state of a reaction of a small molecule on a surface via the nudged elastic band method.
Case Studies
Case Study
Materials Science
- 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.
Case Study
Life Science
Materials Science
Case Study
Materials Science
Documentation
- Documentation
Quantum ESPRESSO Interface
A comprehensive graphical user interface for calculation set-up, job control and results analysis.
- Documentation
QSite
A multi-scale simulation tool that utilizes the QM/MM method, which combines the principles of quantum mechanics and molecular mechanics.
- Documentation
OPLS4 and OPLS5 Force Field
A force field that is a model of the potential energy of a chemical system – a set of functions and parameters used to model the potential energy of the system, and thereby to calculate the forces on each particle.
Events
Webinar
Life Science
- Jul 30, 2025
In silico cryptic binding site detection and prioritization
In this webinar, we will introduce a novel computational workflow that integrates mixed solvent molecular dynamics (MxMD) with SiteMap to reveal and identify cryptic binding sites.
Webinar
Materials Science
- 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.
Event
Materials Science
- Aug 7th-9th, 2025
Display Innovation China 2025
Schrödinger is excited to be participating in the Display Innovation China (DIC) conference taking place on August 7th – 9th in Shanghai, China.
Training Videos
Video
Materials Science
Getting Going with Materials Science Maestro Video Series
A free video series introducing the basics of using Materials Science Maestro.
Video
Materials Science
Polymers: Schrödinger’s Materials Science Builder Series
This video showcases a workflow for polymer modeling, including building, simulating, and analyzing polymer models, and exporting to PyMOL for movie creation.
Video
Materials Science
Building Small Molecules in MS Maestro
The sixth video in the Getting Going with Materials Science (MS) Maestro Video Series: 2D Sketcher, 3D Builder, and Force Field Minimization.
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
Microkinetics Deposition Analysis
Get an overview of the Microkinetics Deposition Analysis panel analyzing microkinetic modeling calculation results for deposition processes.
- Quick Reference Sheet
CREST
Get an overview of the CREST panel covering conformational search for small molecule using CREST.
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
Webinar
Materials Science
- 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.
Webinar
Materials Science
- Jun 19, 2025
Schrödinger 디지털 플랫폼 솔루션을 응용한 디스플레이 소재/소자 및 배터리 소재 기술의 혁신
최근 계산과학과 신소재기술의 발전에 힘입어, 컴퓨터를 활용한 디지털 재료 설계 솔루션을 보다 쉽고 빠르게 연구개발에 적용할 수 있는 기회가 마련되었습니다.
Webinar
Materials Science
- Jun 17, 2025
Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications
In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.
White Papers
White Paper
Materials Science
White Paper
Materials Science
White Paper
Materials Science
Latest insights from Extrapolations blog
Blog
How L’Oreal Uses Digital Simulation to Explore Sustainable Product Ingredients
Most of us could name at least one cosmetic or personal care product that’s essential to looking and feeling our best. Whether it’s a specific kind of soap, a brand of mascara, the right shade of lipstick or the shampoo that works best for our hair, these products have become a critical part of our daily routine.
Uncovering Better Materials for the Spacecraft of Tomorrow
When it comes to solving problems for his job, the work done by Dr. Levi Moore actually is rocket science. As a research chemist with the United States Air Force Research Laboratory, he’s tasked with helping to develop new materials that make it easier and safer to launch rockets into space.
Sustainable Food Packaging Designed at the Atomic Level
Each year, an estimated 330 million metric tonnes of plastic are produced globally. A significant portion of that plastic becomes waste, clogging landfills, rivers, and the ocean for centuries, damaging ecosystems and negatively impacting human health.
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.
Other Resources