Computational Modeling
Simulated data: the pathfinder of what's next
The most important question in the experimental lab is "what's next?", especially with limited time, resources, and budget. Computational data completes the missing piece of the puzzle needed to answer that question: extending beyond limited experimental datasets with quantum accuracy while providing sizable datasets to develop confident machine learning models.
We provide computational modeling workflows that integrate with our laboratory management platform to provide a seamless transition from experiment to simulation. Our computational modeling products run in real time, initializing models and calculating initial properties as soon as the experimental team designs a molecule, sequences a protein, deposits a material, or receives a sample. More advanced simulations can be queued in the background. Our AI products help expert and novice alike identify the most important simulations to run within your budget.
Tiered Quantum Simulations & Probabilities
From molecular dynamics to coupled cluster, our platform provides a tiered approach to molecular and atomistic simulations. Our teams love for errors and uncertainty is integrated deep into our models, helping avoid false negatives. Our probability maps allowing your team to charting a path forward in computational space and identify what experiments to focus precious lab time on.
Problem specific
When dealing with models, we have always found a few quick chats can save an enormous amount of time and frustration. For expert teams, we ensure our APIs and connectors are compatible and working with your current modeling software. For teams eager to get started, we pair your budget and computational resources with the best methods and models to get you started. Most modelling products we incorporate are open source, and we are happy to help you get started with your own methods. In a few settings we have worked with scientific teams to code and implement first of its kind software for specific problems.