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Key Features
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ST4SD Core
Getting Started
The elaunch.py command line tool
Writing experiments
Adding an interface to experiments
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Getting Application Images
Best Practices
DSL 2.0 Specification
ST4SD Cloud
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Installation
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ST4SD Registry
Getting Started
Using the Registry
The stp command line tool
The Build Canvas
Creating parameterised packages
ST4SD Services
Getting Started
Python Client API
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Memoization
OpenShift CLI
Using Graph Relationships
Using Runtime Policies
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Use cases
Scientific Studies
A selection of scientific studies enabled by ST4SD
J. Phys. Chem. B
Model for the Simulation of the CnEm Nonionic Surfactant Family Derived from Recent Experimental Results
JCTC
The Role of Chemical Heterogeneity in Surfactant Adsorption at Solid−Liquid Interfaces
SIAM News
Computational Topology in Geometric Design: Manifolds to Molecules
JCTC
An Efficient Algorithm for Topological Characterisation of Worm-Like and Branched Micelle Structures from Simulations
Polymer International
What Can Digitization Do For Formulated Product Innovation and Development?
J. Phys. Chem. B
Toward a Standard Protocol for Micelle Simulation
J. Phys. Chem. B
The Challenge to Reconcile Experimental Micellar Properties of the CnEm Nonionic Surfactant Family
In preparation
Phase separation in ternary alcohol/water/hydrocarbin systems: Comparison of micro and meso scale methods
J. Chem. Inf. Model
Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields
IBM J. Of Res. and Dev.
Bayesian optimization for accelerated drug discovery
Pre-Print
Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19
J. Science Advances
Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps
ST4SD Technologies
Papers on technologies developed in ST4SD
IEEE Services 2022
Fast, Transparent, and High-Fidelity Memoization Cache-Keys for Computational Workflows
ApPLIED 2022
Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery Tasks
J. Physical Chemistry Chemical Physics
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Pre-Print
Efficient and Scalable Batch Bayesian Optimization Using K-Means
npj Comp. Materials
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Case-Studies
IBM Researchers develop easy-to-use virtual experiments for Unilever chemists
Virtual formulation of polymeric systems
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