The Quantum Boost platform gives formulators the tools to improve their experimental strategy, from setting project objectives to figuring out the best experiments to conduct. With advanced analytics and suggestions from AI, the platform helps you move quickly and accurately toward your target formulations. This tutorial will show you the main features of Quantum Boost and how to get the most out of the platform to speed up your projects.
Quantum Boost uses advanced Artificial Intelligence algorithms, particularly Bayesian Optimization, to navigate your complex factor space efficiently. Unlike the usual Design of Experiments (DoE) software, which can fail when dealing with problems that are high-dimensional, non-linear, or non-convex, Quantum Boost works well even in tough situations. By integrating your existing experimental data into its optimization model, the platform helps you reach your project objectives swiftly and accurately. For a closer look at how Bayesian Optimization compares to DoE, you can read our Bayesian optimization vs. DoE article.
When defining a project on Quantum Boost, you are setting up a roadmap for your experimentation. It is important to provide the system with as much accurate information as possible to leverage its AI capabilities fully. Here is a step-by-step guide on how to define a project:
Goals in Quantum Boost are the outcomes you are aiming to achieve. These could be attributes like "Viscosity," "Surface Tension," or "Resistivity."
You can add as many goals as you want, and if you manage to reach your targets, Quantum Boost will continue to optimize to make those goals even better.
Factors are the variables that you can control or monitor during your experiments. There are three types:
Factors have some considerations based on each type:
The beauty of Quantum Boost lies in its flexibility. All these factors can be modified during the lifespan of your project without losing valuable data.
One standout feature of Quantum Boost is its adaptability. You can change goals and factors mid-project without compromising the integrity of the existing data. This is incredibly useful in real-world scenarios where supply chain issues might force you to swap out ingredients or adjust your goals.
(Note: when dropping an ingredient used in past experiments, you would just set its value to a constant zero.)
By thoughtfully defining your project with accurate goals and factors, you are laying a strong foundation for successful experimentation. Remember, the more precise your initial setup, the more effectively Quantum Boost will steer you toward your objectives.
Constraints are rules or limitations you set to guide the algorithm's suggestions further. They ensure that the experiments align with real-world conditions or specific goals you are trying to achieve. Quantum Boost allows you to set two types of constraints: Sum Constraints and Group Constraints.
These constraints deal with the collective effect or total sum of a particular group of factors in your formulation. For example, you might want to ensure that:
These constraints ensure your formulation meets the practical limitations and pre-defined project specifications. You can define as many sum constraints as necessary to narrow your experimentation to the most useful and feasible options.
While sum constraints focus on the collective quantities or costs, group constraints limit the selection from a particular category of ingredients. Examples include:
Group constraints are particularly useful when you have mutually exclusive options or want to limit complexity by specifying the maximum number of components used from a specific category.
Together, these constraint functionalities in Quantum Boost empower you to conduct scientifically rigorous, realistic experiments aligned with your business or research objectives.
The core of Quantum Boost's algorithm relies on your past experiments. By analyzing these experiments, we build a model of your factor space to suggest the most impactful next steps toward your objectives. Here is how to add experiments to fuel this model:
You can manually input new experiments from scratch, filling in details about factors and responses.
If your organization has completed relevant experiments in other projects, you can import these directly into your current project.
After experiments are added, input your factors and their corresponding responses. The platform provides visual cues:
By carefully inputting your experiment details, you contribute to the precision and effectiveness of Quantum Boost's model, streamlining your journey toward meeting your project objectives.
Generating suggestions is at the core of what makes Quantum Boost so efficient. The platform uses its advanced algorithms to pinpoint the most promising next experiments for you to perform based on your project definitions and previous experiments. The aim is to lead you to your desired formulation with the fewest experiments possible.
Here are some key aspects to consider when generating suggestions:
By keeping these considerations in mind when generating suggestions, you are positioning your project for both accelerated progress and optimized resource utilization.
The analytics section in Quantum Boost is designed to provide user-friendly insights tailored for formulation scientists. Unlike traditional DoE software, which often requires a statistical background to interpret, our analytics are straightforward and intuitive. Below are the key types of analytics provided:
This graph offers a visual way to identify correlations among factors, responses, and constraints. Key indicators on the graph include:
This plot informs you about how various factors impact your responses. A factor that doesn't appear for a given response is likely not statistically significant. Reading the information popup associated with this graph is advised for a more nuanced understanding.
The Model Reasoning Plot is designed to help you better understand the underlying logic that guides the algorithm's suggestions. The graph represents what the algorithm thinks about the relationship between the selected factor and response, complete with a band of uncertainty.
For an in-depth understanding of each plot, click on the information icon associated with each graph. This will provide detailed explanations and use-case scenarios to help you maximise these analytical tools.
Quantum Boost presents a robust platform for any formulator aiming to expedite the process of material and chemical development. Understanding and utilizing its multifaceted features allows you to stay ahead of the curve in your research and development endeavours.
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