Designing the next generation of materials starts at the molecular level.
Our molecular modeling platform enables the prediction of better properties for better materials.
Pushing the frontier of materials science
Solutions for diverse materials science R&D applications
- Polymeric Materials
- Consumer Packaged Goods
- Catalysis & Reactivity
- Thin Film Processing
- Energy Capture & Storage
- Pharmaceutical Formulations & Delivery
- Metals, Alloys & Ceramics
Organic Electronics
Discover optimal organic electronic materials with good conductivity, mechanical and thermal stability, and suitable for fabrication.
- Accurately predict key optoelectronic properties
- Run high-throughput screening for rapid design and discovery of advanced materials
- Leverage easy-to-use, automated workflows backed by expert scientific support
Polymeric Materials
Understand and predict product performance through simulations of polymers at molecular and atomic scale.
- Accurately predict chemical reactivity, polymer morphology, and key physicochemical properties
- Design new polymers, screen formulations, and optimize manufacturing
- Leverage easy to use, automated workflows for discovery and optimization
Consumer Packaged Goods
Harness molecular simulation to accelerate innovation for consumer goods
- Predict key performance indicators for consumer products
- Build and simulate complex molecular systems that directly relate to product formulas
- Leverage easy-to-use, automated solutions backed by expert scientific support
Catalysis & Reactivity
Accelerate the discovery of the next generation of catalytic and non-catalytic processes.
- Run automated workflows for high-throughput discovery of novel catalysts and reactants
- Elucidate the details of reactivity, selectivity, and specificity
- Leverage a collaborative enterprise platform for novel materials discovery
Thin Film Processing
Optimize atomic level processing for the semiconductor industry and improve device performance.
- Accurately predict surface reactivity to optimize deposition or etch processes
- Validated prediction of precursor volatility by machine learning
- Leverage efficient simulation tools for the design of novel chemicals for materials processing
Energy Capture & Storage
Accelerate the development of cleaner, lighter, safer, and more energy-efficient materials for batteries, fuel cells and photovoltaics.
- Predict key properties for batteries, fuel cells, photovoltaics, and hydrogen storage R&D
- Simulate and analyze critical properties of component materials and interfaces
- Enumerate and explore vast chemical space using streamlined workflows
Pharmaceutical Formulations & Delivery
Optimize your pharmaceutical at the molecular level with smart, strategic drug formulation
- Characterize and optimize drug formulations and delivery
- Understand drug stability and reactivity
- De-risk your solid form selection process by identifying the most stable polymorph at room temperature
Metals, Alloys & Ceramics
Enable the design of high-performance inorganic materials with highly efficient multiscale simulations and cheminformatics machine learning models.
- Accurately predict key properties (structures, morphologies, and compositions) for inorganic materials
- Enhance your materials analysis capabilities
Use Cases
Explore how teams can apply Schrödinger technology to accomplish their work across industries.
BY INDUSTRY
Oil & Gas
Consumer Packaged Goods
Aerospace
Academics
Automotive
Specialty Chemicals
Biotechs & Pharmaceuticals
Semiconductor
BY TEAM FUNCTION
Computational Chemistry
Experimental Chemistry
Materials Engineering
Research IT
The leading computational platform for molecular design and discovery
Grounded in physics-based molecular modeling
Amplified by state-of-the-art machine learning
Democratized across teams through collaborative enterprise informatics
Featured Product
Formulation ML
Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties
Strategic Partner
Schrödinger has partnered with Ansys since 2023 to enable the development and implementation of multiscale simulation, providing engineers with a powerful tool for virtual material design and optimization
Upcoming Event
- Materials Science
- 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.
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Date & Time:
Aug 6th, 2025 | 8:00 AM PDT | 11:00 AM EDT | 4:00 PM BST | 5:00 PM CEST
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Location:
Virtual
Date & Time: 
Location: 
The importance of human know-how in AI execution for R&D
How Schrödinger’s materials science domain experts ensure partner success
Read the articleThe world’s most innovative companies use Schrödinger
