Composite resin formulation with AI and machine learning in action

This webinar with demos will demonstrate how integrating ML with molecular simulation enables faster, more informed development of next-generation resin formulations.

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Composite resin formulation with AI and machine learning in action recording

This webinar with demos will demonstrate how integrating ML with molecular simulation enables faster, more informed development of next-generation resin formulations.

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Integrating AI and Machine Learning to Accelerate Composite Resin Formulation

Schrödinger is excited to be hosting a webinar in collaboration with Composites World, taking place on May 13th at 11:00AM EDT.

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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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Material Property Simulation for Advanced Packaging

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Designing polymersomes with surface-integrated nanoparticles through hierarchical phase separation

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Synthesis, optical and electrochemical properties of thiophene and thieno [3, 2-b] thiophene linked with structurally modified rhodanine based copolymers

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Stability enhancement of Amphotericin B using 3D printed biomimetic polymeric corneal patch to treat fungal infections

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

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Electrochemical Sensing of Phenylalanine using Polyaniline-Based Molecularly Imprinted Polymers

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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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Fast, accurate, and tunable: Advancing battery materials innovation with Schrödinger’s Machine Learning Force Fields

Join us for live demos showcasing applications of MLFFs for accurate modeling of complex systems including liquid and solid-state electrolytes.

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Fast, accurate, and tunable: Advancing battery materials innovation with Schrödinger’s Machine Learning Force Fields recording

Join us for live demos showcasing applications of MLFFs for accurate modeling of complex systems including liquid and solid-state electrolytes.

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Physics-driven ML to accelerate the design of layered multicomponent electronic devices Japan Recording

Schrödinger has developed a machine learning (ML) framework that enables users to predict key performance metrics of multilayered electronic devices from simple, intuitive descriptions of their architecture and operating conditions.

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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.

• Watch Webinar

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.

• Watch Webinar

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.

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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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Accelerating OLED innovation with multi-scale, multi-physics simulations

Join us to explore how integrated digital workflows drive the design of next-generation, high-performance OLEDs.

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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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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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Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization

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Digital Discovery of Large-Scale Optoelectronic Materials via MPNICE Machine-Learning Force Fields

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Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation

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Band Gap and Reorganization Energy Prediction of Conducting Polymers by the Integration of Machine Learning and Density Functional Theory

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Charge Transport Regulation in Solution-Processed OLEDs by Indenocarbazole–Triazine Bipolar Host Copolymers

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Advancing efficiency in deep-blue OLEDs: Exploring a machine learning–driven multiresonance TADF molecular design

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Exploring Molecules with Low Viscosity: Using Physics-Based Simulations and De Novo Design by Applying Reinforcement Learning

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A machine learning approach for in silico prediction of the photovoltaic properties of perovskite solar cells based on dopant-free hole-transport materials

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Modified t-butyl in tetradentate platinum (II) complexes enables exceptional lifetime for blue-phosphorescent organic light-emitting diodes

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Understanding of complex spin up-conversion processes in charge-transfer-type organic molecules

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