While technological advancements are great for new discoveries and as more and more scientific institutions continue upgrading their technical infrastructure with new and improved devices, there is a major issue that is coming to light – analysis of the humongous amounts of data collected by these instruments.
To ensure that they are able to make full use of these instruments, there needs to be found a way to decrease the amount of data required for scientific discovery and also address the data acquisition rates that we humans have been increasing over the years and how we are in no way capable of keeping up with that.
Scientists at the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at Berkeley Lab have published a new paper on Gaussian processes for autonomous data acquisition wherein they have revealed how CAMERA introduces innovative autonomous discovery techniques across a broad scientific community.
Stochastic Processes Take the Lead
Over the last few years, autonomous discovery methods have become more sophisticated, with stochastic processes (for instance, Gaussian process regression [GPR]) emerging as the method of choice for steering many classes of experiments. The success of GPR in steering experiments is due to its probabilistic nature, which allows us to make decisions based on the uncertainty of the current model. This is what lies at the heart of gpCAM, a software tool developed by CAMERA.
While CAMERA’s initial research efforts have focused primarily on synchrotron beamline experiments, a growing number of scientists in other disciplines are now seeing the advantages of incorporating autonomous discovery techniques into their experimental project workflows. In April, a workshop on autonomous discovery in science and engineering sponsored by CAMERA and chaired by Marcus Noack, a research scientist in the CAMERA attracted hundreds of scientists from around the world, reflecting the expanding interest in this emerging field.
Multiple Uses Emerging
For example, John Thomas, a post-doctoral research fellow in Berkeley Lab’s Molecular Foundry, is using photo-coupled scanning probe microscopy to understand material properties of thin-film semiconducting systems and has been working with gpCAM to enhance these efforts.
The group recently completed an application that makes use of gpCAM within a Python-to-LabVIEW interface, where, with some user input for initialization, gpCAM drives an atomically sharp probe across a semiconductive two-dimensional material for hyperspectral data collection. Images obtained represent a convolution of both electronic and topographic information, and point spectroscopy extracts local electronic structure.