Develop higher performing, more sustainable polymers, faster

Getting started in polymer compute-driven design: Predicting glass transition temperature in the Materials Science Suite

Learn from a live demo of building polymers and polymer formulations and running a Tg prediction workflow.

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Accelerating product development with computational materials engineering

Learn how Ansys and Schrödinger are transforming product development with Integrated Computational Materials Engineering (ICME) to accelerate material discovery and innovation.

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Accelerating materials discovery with physics-informed AI/ML

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

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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.

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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.

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Computational insights into polymer excipient selection for amorphous solid dispersions

In this webinar, we will highlight how molecular models can aid our ability to screen through standard polymer excipients for target lists to push into lab testing.

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High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

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Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

In this webinar, we demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

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How Physics-based Modeling and Machine Learning Enable Accelerated Development of Battery Materials

In this webinar, we focus on examples to demonstrate the application of automated solutions for accurate prediction of thermodynamic stability and voltage profile of cathode materials, ion diffusion pathways and kinetics in electrode materials, transport properties of liquid electrolytes and modeling the nucleation and growth of solid electrolyte interphase (SEI) layers using Schrödinger’s SEI simulator module.

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AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

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Designing the Next Generation of Polymers with Machine Learning and Physics-Based Models

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Optimization of fluorinated phenyl azides as universal photocrosslinkers for semiconducting polymers

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Advancing material property prediction: using physics-informed machine learning models for viscosity

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Tuning the Mobility of Indacenodithiophene-Based Conjugated Polymers via Coplanar Backbone Engineering

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Physics-based molecular modeling of biosurfactants

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Molecular-scale exploration of mechanical properties and interactions of poly(lactic acid) with cellulose and chitin

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Study of water sorption in Methacryl-based Polyhedral Oligomeric Silsesquioxane (POSS) dental composites using molecular dynamics simulations

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Whole-cell mediated carboxylation of 2-Furoic acid towards the production of renewable platform chemicals and biomaterials

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Understanding the Effect of the Oil-to-Surfactant Ratio on Eugenol Oil-in-Water Nanoemulsions Using Experimental and Molecular Dynamics Investigations

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Linking Redox Characteristics to Dissolved Organic Matter Derived from Different Biowaste Composts: A Theoretical Modeling Approach Based on FT–ICR MS Analysis

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