Catalyst.Jl

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Catalyst.jl is an Open Source chemical reaction network and systems biology interface for scientific machine learning (SciML). It offers high-performance, GPU-parallelized, and O(1) solvers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Catalyst.jl?

Catalyst.jl is a symbolic modeling package designed for the analysis and high-performance simulation of chemical reaction networks and related dynamical systems. It supports various simulation types including ODE, steady-state ODE, SDE, stochastic chemical kinetics (jump), and hybrid simulations. Models can be specified using an intuitive domain-specific language (DSL) or constructed programmatically. Built on ModelingToolkitBase.jl and Symbolics.jl, Catalyst leverages symbolic computation for tasks like sparsity exploitation, Jacobian construction, and dependency graph analysis. It integrates seamlessly with the broader Julia and SciML ecosystems for advanced analyses such as sensitivity analysis, parameter estimation, and bifurcation analysis, making it a powerful tool for researchers and developers in systems biology and scientific machine learning.

Best used for

Ideal for developers and data scientists who need to model and simulate complex chemical reaction networks, analyze systems biology, and integrate with scientific machine learning workflows. Especially valuable for researchers requiring high-performance, GPU-parallelized solvers and symbolic computation capabilities for advanced analyses.

Common actions

model reaction networks

simulate biological systems

analyze chemical kinetics

perform parameter estimation

conduct sensitivity analysis

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Capabilities

Key features

Symbolic modeling package
Chemical reaction networks
Systems biology interface
High-performance simulation
GPU-parallelized solvers
Domain-specific language
ODE, SDE, Jump simulations

Target Audience

developerdata scientistprofessorstudent

Integrations

ordinarydiffeqstochasticdiffeqjumpprocessesdiffeqgpuhomotopycontinuationsteadystatediffeqnonlinearsolvebifurcationkit+ 17 more

Pricing & Plans

Open Source

Free

FAQs

What types of simulations can Catalyst.jl perform?

Catalyst.jl supports a wide range of simulations including Ordinary Differential Equation (ODE), steady-state ODE, Stochastic Differential Equation (SDE), stochastic chemical kinetics (jump), and hybrid simulations. It can also couple reactions with differential equations, events, and external noise sources.

How does Catalyst.jl leverage symbolic computation?

Built on ModelingToolkitBase.jl and Symbolics.jl, Catalyst.jl uses symbolic computation for tasks such as sparsity exploitation, Jacobian construction, dependency graph analysis, and parallelism. This allows for efficient and robust model generation and analysis.

What are some key features for model specification in Catalyst.jl?

Catalyst.jl offers an intuitive domain-specific language (DSL) for specifying models using chemical reaction notation. It also supports compositional modeling with features like `@network_component`, `compose`, and `extend`, allowing for hierarchical model construction.

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