Schrödinger Online Courses
Learn Molecular Modeling for Materials Science Applications
Schrödinger Online Courses
Level up your skillset with hands-on, online computational chemistry courses using industry-leading technology
Computational molecular modeling tools have proven effective in materials science research and development. Chemists, physicists and engineers working in materials science will increasingly encounter molecular modeling throughout their careers, making it critical to have a foundational understanding of the cutting edge tools and methods. These courses are ideal for those who wish to develop professionally and expand their CV by earning certification and a badge.
These computational chemistry courses offer an effective and efficient approach to learn practical computational chemistry for materials science:
- Work hands-on with Schrödinger's industry-leading Materials Science Maestro software
- Jump start your research program by learning methods that can be directly applied to ongoing projects
- Learn topics ranging from density functional theory (DFT) to molecular dynamics to machine learning for materials design
- Perform a completely independent case study to demonstrate mastery of the course content
- Access Schrödinger Education Team experts for Q&A throughout the course
- Work on the course materials on your own schedule whenever convenient for you
Register for One Course Register for Seven Course Bundle
Molecular Modeling for Materials Science Applications is a compilation of seven asynchronous courses that present a pathway format, allowing you to learn Schrödinger's Material Science tools for any one or all of the following courses:
Explore Course Syllabi
Click on the course icons above for more information!



“Clear instructions with a well-designed course interface allowed me to run some of my own first molecular dynamics simulations. The information from the course felt much more secure than the information from YouTube because I knew it was developed by real experts in the field.”
-Graduate Student



“The course let me talk confidently about molecular modeling and what it can do. For me, this was a nice experience which left me with many ideas for applying molecular modeling in the research area of our department, not only for me but also for my colleagues.”
-Graduate Student



“As always, the course is very well designed. Pharmaceutical formulation is quite outside my comfort zone in terms of the theory and modeling but this course provided me with general knowledge of evaluating what modeling can facilitate the drug development work in the real world. Really great design and education process. Great experience. This function also seems to be quite unique in the field based on my limited experience which gives Schrodinger a huge advantage.”
-Senior Director, Therapeutic Protein Design



Course Dates
Session Dates |
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October 4th, 2023 - November 15th, 2023 |
October 18th, 2023 - November 29th, 2023 |
November 1st, 2023 - December 13th, 2023 |
The courses are offered during specific session ranges to provide access to web-based compute resources and so that the Schrödinger Edcation Team can actively support participants. However, during a session, participants can work through the course materials completely asynchronously and self-paced.
Course Materials
Schrödinger’s online course does not require any books nor access to specific journals (though many references are included for those who do have journal access). Access to relevant Schrödinger software is included for the duration of the course. Use of a 3-button mouse with a scroll wheel will allow participants to easily follow along with the course videos. Additionally, an external monitor is helpful if one is available.
Course Requirements
A working knowledge of general chemistry and access to a computer with a high-speed internet connection (8 Mbps or better). You can check your internet speed here.
Course Content
In Schrödinger’s series of online courses, Molecular Modeling for Materials Science Applications, we will learn workflows in Materials Science Maestro (the Schrödinger graphical user interface for materials modeling) to apply to research and development:
- Lessons are hands-on and applied, aimed at teaching practical use of the tools
- There is some introduction to basic theory and further references for additional learning
- Courses are designed to teach computational methodologies and industry applications synergistically to optimize the learning process
Click on the course icons above for more information!
Cost
The seven courses are available to purchase individually or bundled together. The bundled cost includes access to all seven courses during the session.
Student | Non-Student | |
---|---|---|
One Course | $380 | $515 |
Seven Course Bundle** | $785 | $1300 |
**Course bundle is only available during the single six week session
If you would like to pay by Purchase Order, please email online-learning-materials@schrodinger.com. If you would like to pay by credit card, please proceed with the online registration
A limited number of course scholarships are available each session based on a demonstration of financial need. See Scholarship Program
FAQ
• Will material still be available after a course ends? [Answer]
• Do I need access to the software to be able to do the course? Do I have to purchase the software separately? [Answer]
• Do I need to know how to use MS Maestro to be able to take this course? [Answer]
• Does this course teach you how to use the software or about theory? [Answer]
• If I register for the course bundle, will I have time to complete all seven courses during the session? [Answer]
• How can I submit payment for the courses? What if my institution will cover my cost? [Answer]
• Are there scheduled, live lectures? [Answer]
Get Started!
1 Before registering, you will need a Schrödinger Web Account. You can request one here if you don't already have one.
2 Click below to choose between a single course or the seven course bundle (up to 60% off for access to all seven courses when you register for the course bundle)
Register for One Course Register for Seven Course Bundle
Need help? For additional information about the course, please email online-learning-materials@schrodinger.com.
Surface Chemistry
Molecular quantum mechanics, periodic quantum mechanics, and machine learning approaches for studying atomic layer processing and heterogeneous catalysis.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Introduction to Modeling for Surface Chemistry
Module 2
Molecular & Periodic Quantum Mechanics

Introduction to Quantum Mechanics (mQM & pQM)
- Functionals, Basis Sets and Geometry Optimizations
- QM Multistage Workflows
- Energies of Reactions
- Building and Manipulating Crystals
- Properties of Bulk Crystals

Module 3
Molecular & Periodic Quantum Mechanics
- Modeling Surfaces
- Activation Energies for Reactivity in Solids and on Surfaces
- R-Group Enumeration
- Organometallic Complexes
- Beta Elminimation Reactions
- Bond and Ligand Dissociation

Module 4
Machine Learning

Introduction to Machine Learning (ML)
- Machine Learning for Materials Science
- Periodic Descriptors for Inorganic Solids

Module 5
Guided Case Study

Palladium Precursor Design
Heterogeneous Carbon Dioxide Reduction
Module 6
Independent Case Study

Adsorption of Formaldehyde onto Palladium

Homogeneous Catalysis and Reactivity
Molecular quantum mechanics and machine learning approaches for studying reactivity and mechanism at the molecular level.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Modeling for Homogeneous Catalysis and Reactivity
Module 2
Molecular Quantum Mechanics

Introduction to Molecular Quantum Mechanics (mQM)
- Functionals, Basis Sets and Geometry Optimizations
- R-Group Enumeration
- QM Multistage Workflows
- Rigid & Relaxed Coordinate Scacns
- Energies of Reactions
- Organometallic Complexes

Module 3
Molecular Quantum Mechanics
- Bond and Ligand Disassociation Energy
- Beta Elimination Reactions
- Locating Transition States: Part One
- Locating Transition States: Part Two
- Reaction Workflow for Polyethylene Insertion

Module 4
Machine Learning

Introduction to Machine Learning (ML)
- Machine Learning for Materials Science
- Cheminformatics Machine Learning for Homogeneous Catalysis

Module 5
Guided Case Study

Fundamental Organometallic Reactivity
Module 6
Independent Case Study

Predicting Regioselectivity of Hydroboration

Consumer Packaged Goods
All-atom molecular dynamics, coarse-grained, and machine learning approaches for studying materials integral to the formulation of CPG.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Modeling for Consumer Packged Goods
Module 2
Molecular Dynamics

Introduction to Molecular Dynamics (MD)
- Disordered System Building and MD Multistage Workflows
- Building, Equilibriating and Analyzing Polymers
- Building a Carbohydrate Polymer
- Building Polymer-Polymer Interfaces
- Crosslinking Polymers

Module 3
Molecular Dynamics & Coarse-Grained Simulation
- Cluster Analysis
- Surfactant Tilt and Electrostatic Potential
- Viscosity
- Starch Moisture Uptake and Plasticization

Introduction to Coarse-Graining
- Building a Coarse-Grained Surfactant Model

Module 4
Machine Learning

Introduction to Machine Learning (ML)
- Machine Learning for Materials Science
- Polymer Descriptors for Machine Learning
- Machine Learning for Sweetness

Module 5
Guided Case Study

Coarse-Grained Modeling of SLES
Module 6
Independent Case Study

Self-Aggregation of DDM and DPC Molecules

Organic Electronics
Molecular quantum mechanics, all-atom molecular dynamics, and machine learning approaches for studying challenges in OLED design and discovery.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Modeling for Organic Electronics
Module 2
Molecular Quantum Mechanics

Introduction to Molecular Quantum Mechanics (mQM)
- Functionals, Basis Sets and Geometry Optimizations
- R-Group Enumeration
- QM Multistage Workflows
- Optoelectronics
- Organometallic Complexes
- Bond and Ligand Dissociation Energy
- Band Shape
- Excited State Analysis

Module 3
All-Atom Molecular Dynamics

Introduction to Molecular Dynamics (MD)
- Disordered System Building and MD Multistage Workflows
- Molecular Deposition
- Kinetic Monte Carlo Charge Mobility
- Molecular Dielectric Properties

Module 4
Machine Learning

Introduction to Machine Learning (ML)
- Machine Learning for Materials Science
- Optoelectronics Active Learning

Module 5
Guided Case Study

Modeling Intermolecular Interactions in the Emissive Layer
Module 6
Independent Case Study

Evaluating Hole Transport Materials

Pharmaceutical Formulations
Molecular and periodic quantum mechanics, all-atom molecular dynamics, and coarse-grained approaches for studying active pharmaceutical ingredients and their formulations.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Modeling for Pharmaceutical Formulations
Module 2
All-Atom Molecular Dynamics

Introduction to Molecular Dynamics (MD)
- Disordered System Building and MD Multistage Workflows
- Molecular Dynamics Simulations for API (active pharmaceutical ingredient) Miscibility
- Glass Transition Temperature for APIs
- Hygroscopicity
- Crystal Morphology

Module 3
Coarse-Grained Simulation

Introduction to Coarse-Graining (CG)
- Ibuprofen Cyclodextrin Inclusion Complexes with the Martini Coarse-Grained Force Field
- Ibuprofen Copovidone Drug Excipient Model with Dissipative Particle Dynamics (DPD)

Module 4
Molecular & Periodic Quantum Mechanics

Introduction to Quantum Mechanics (mQM & pQM)
- Functionals, Basis Sets and Geometry Optimizations
- QM Multistage Workflows
- Bond and Ligand Dissociation Energy
- pKa
- Building and Manipulating Crystals
- Properties of Bulk Molecular Crystals

Module 5
Guided Case Study

Nanoemulsions with Automated DPD Parameterization
Module 6
Independent Case Study

API Property Prediction

Polymeric Materials
All-atom molecular dynamics and machine learning approaches for studying polymeric materials and their properties under various conditions.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Modeling for Polymeric Materials
Module 2
Molecular Dynamics

Introduction to Molecular Dynamics (MD)
- Disordered System Building and MD Multistage Workflows
- Building, Equilibrating and Analyzing Polymers
- Building Polymer-Polymer Interfaces
- Crosslinking Polymers

Module 3
Molecular Dynamics
- Polymer Property Prediction
- Penetrant Loading
- Diffusion
- Polymer Electrolyte Analysis
- Dielectric Properties

Module 4
Machine Learning

Introduction to Machine Learning (ML)
- Machine Learning for Materials Science
- Polymer Descriptors for Machine Learning

Module 5
Guided Case Study

Epoxy Formulations
Module 6
Independent Case Study

Polymer-Mediated Graphene Dispersion

Battery Materials
Molecular and periodic quantum mechanics, all-atom molecular dynamics, and machine learning approaches for studying battery materials and their properties under various conditions.
Broken up into six modules, this course is entirely self-paced during the course session. It will take approximately 25 hours for someone new to computational modeling to complete (actual time may vary depending on experience). The modules can be done on your own schedule. If you have any questions don't hesitate to email us at online-learning-materials@schrodinger.com.
Module 1
Introduction to Materials Modeling

Introduction to Materials Modeling & This Online Course

Introduction to Materials Science (MS) Maestro

Introduction to Modeling for Batteries
Module 2
Molecular & Periodic Quantum Mechanics

Introduction to Molecular and Periodic Quantum Mechanics (mQM & pQM)
- Quantum Mechanical Workflows and Properties: Part 1
- Quantum Mechanical Workflows and Properties: Part 2
- Building Bulk Crystals and Calculating Properties
- Calculating Intercalation and Voltage Curves
- Lithium Ion Migration Barrier (NEB)

Module 3
All-Atom Molecular Dynamics

Introduction to Molecular Dynamics (MD)
- Disordered System Building and MD Multistage Workflows
- Building, Equilibrating and Analyzing Polymers
- Diffusion
- Polymer Electrolyte Analysis
- Liquid Electrolyte Properties: Part 1
- Liquid Electrolyte Properties: Part 2

Module 4
Machine Learning

Introduction to Machine Learning (ML)
- Machine Learning for Materials Science
- Machine Learning for Ionic Conductivity

Module 5
Guided Case Study

EC Decomposition on a Li (001) Surface
Module 6
Independent Case Study

Modifying Battery Electrolyte Components
