Leverage advanced Bayesian Optimization to efficiently discover optimal parameters, significantly reducing experimental costs and accelerating your R&D cycles in chemistry, materials science, and beyond.
Launch ExperimentSmart AppExperimentSmart moves beyond traditional trial-and-error, employing intelligent algorithms to provide a data-driven path to optimal experimental outcomes.
Conventional experimentation is often a lengthy, resource-intensive endeavor, heavily reliant on intuition. This can lead to numerous iterations, slow progress, and missed opportunities for rapid innovation.
Our platform utilizes sophisticated Bayesian Optimization, powered by Gaussian Process models, to intelligently explore your experimental space. It learns from each observation to guide your subsequent choices towards optimal conditions with significantly fewer experiments.
Save invaluable time and material resources, accelerate your innovation pipeline, and uncover novel materials, formulations, or process conditions faster. Focus on groundbreaking discovery, not just repetitive testing.
A suite of tools meticulously designed to enhance your experimental workflow and maximize research productivity.
Our core employs our developed robust training method for Gaussian Process models, ensuring enhanced generalization, providing reliable uncertainty quantification, and consistently outperforming traditional ML methods.
Tailor your search strategy by choosing from Expected Improvement (EI), Upper Confidence Bound (UCB), Probability of Improvement (PI), Thompson Sampling (with multiple recommendation strategies), or Minimum Predicted Value (MPV) to align with your specific optimization goal (minimization or maximization).
Begin with an explorative set of initial points generated via Latin Hypercube Sampling (LHS) for comprehensive space coverage, or seamlessly upload your existing experimental data from Excel files to leverage prior knowledge and jumpstart the optimization.
Easily define numeric variables (continuous or discrete with precise minimum, maximum, and step control) and categorical variables using various encoding options (e.g., One-Hot, Ordinal) suitable for diverse scientific datasets.
Track optimization progress with plots of observed values against iteration. Monitor Gaussian Process model performance with R² scores and parity plots. Visualize initial sample distributions and intelligently suggested points within the feature space (2D t-SNE for higher dimensions).
Maintain complete authority over your experimental domain. All suggested parameters rigorously adhere to your initially specified variable ranges and step sizes, ensuring that recommendations are always practical and relevant to your specific experimental constraints.
A straightforward, intuitive process from initial setup to discovering your optimal experimental conditions.
Intuitively set up your experimental factors: define all relevant numeric (continuous/discrete) and categorical variables, specify their operational ranges or allowed values and step sizes, clearly state your optimization objective (e.g., maximize yield, minimize cost), and select your preferred Bayesian algorithm settings.
ExperimentSmart intelligently suggests the next set of parameters identified as most promising by its model. Conduct your physical or simulated experiment using these conditions, then input your observed result (e.g., yield, property value) back into the platform, allowing the model to learn and continuously refine its understanding of your system.
Visualize your optimization journey and experimental progress in real-time. Watch as the algorithm intelligently and efficiently converges towards the optimal conditions. Analyze model performance and confidently identify the parameters that achieve your research objectives faster than ever before.
Stop guessing, start optimizing. Launch ExperimentSmart today and harness the power of AI to accelerate your discovery process and achieve superior results with unparalleled efficiency.
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