Quantum Computing Breakthrough: Finite Temperature Solvers Transform Material Science

The Magnificent 7 - the seven largest U.S. tech stocks - owe a large part of their advantage to recognizing early that data has inherent value. As the new gold, the most successful companies are the ones selling pickaxes in this unprecedented rush to process, understand, and monetize data.

So where do companies actually find rich veins of digital gold?

For OpenAI the answer was the internet. All of the internet. The result was a series of groundbreaking models that effectively represented the intricacies of language learned from blogs, books, Wikipedia, Reddit, Facebook etc. In this paradigm, every bit of text formed a meaningful example.

Discussions now revolve around questions of where we go from here? We expect leaps in capability in short lengths of time. Now that large language models contain the entirety of the world’s collective text - how do we improve these models?

Increasingly, the answer is simulation.

Simulated data becomes attractive when real-world data is scarce, expensive, or impossible to generate. Predicting protein structures used to be painstaking and manual. Google’s AlphaFold - trained on public protein sequence and structure data - learned the relationship between amino acids and the highly folded and complex shapes of proteins. Now it’s a reliable generator of proteins, expanding the known library by orders of magnitude, speeding up experimental design and discovering new pharmaceuticals that save lives.

Similarly, large quantitative models (LQMs) represent complex systems using massive numbers of variables and data points. Like how ChatGPT was a large model for text generation - LQMs generate potential solutions to problems in drug and material discovery. In simple terms, if ChatGPT is an arts student, LQMs are STEM majors. Unlike ChatGPT, LQMs require large quantities of high quality data to build performant models.

That’s where Compute Everything comes in.

We’re a data factory producing high-accuracy simulations of molecular systems that power LQMs for materials discovery, to validate materials these models produce, or support manual, detailed research by scientists in diverse fields.

Just like a parts manufacturer, we’ve built specialized tooling - proprietary algorithms, computation kernels, and workflows that power our production line. Our simulations are higher quality and lower cost. And unlike others, our engine accounts for temperature, a critical detail that lets us model systems under real-world biological and mechanical conditions.

We provide an easy to use API to organize and track experiments and return the most accurate data on your system of interest. With our Double Blind method, we never know the materials you’re investigating, maintaining trade secrets and upholding privacy.

If you're building a material that can change the world - we're here to help you move faster.