AI News, When Artificial Intelligence Meets Genomics artificial intelligence
Artificial intelligence meets materials science
The team developed and demonstrated an autonomous and efficient framework capable of optimally exploring a materials design space (the materials design space is an abstraction of the concrete world.
An autonomous system -- or artificial intelligence (AI) agent -- is defined as any system capable of building an internal representation, or model, of the problem of interest, and that then uses the model to make decisions and take actions independent of human involvement.
'Advanced materials are essential to economic security and human well-being, with applications in industries aimed at addressing challenges in clean energy, national security and human welfare, yet it can take 20 or more years to move a material after initial discovery to the market.'
-- Materials Genome Initiative The team wanted to test the framework exhaustively, so they carried out the demonstration in a closed-loop computational platform, using quantum mechanics to predict properties of MAX-phases, which are promising materials for high-temperature applications, including novel oxidation resistant coatings for jet engine turbine blades.
However, this team is the first to use a Bayesian based technique (meaning they take stock of all that is known about a material/material class and leverage that knowledge to find the best material) and employ it in an autonomous fashion, continuously searching not only for the next best computation/experiment to run but also for the best model to represent the acquired data.
At the very least, this framework provides a very efficient means of building the initial data set since it may be used to guide experiments or calculations by focusing on gathering data in those sections of the materials design space which will result in the most efficient path to achieving the optimal material.
'While other people were focusing on the generation and analysis of huge amounts of data, we realized that the best way forward was to focus on experiment design -- how to explore the vast domain of possible materials and increase our chances of success by choosing materials with a goal, target property, or response in mind,' said Talapatra.
DeepMind Solves One Of The Oldest Challenges Of Biochemistry With AlphaFold
An Antarctic eelpout swims gracefully in cold dark depths without freezing its internal juices.
The 3D structure of AFPs allows them to bind to ice crystals and prevent organisms from freezing by forming a hydrophobic layer that separates liquid from crystallising.
The challenge is that DNA only contains information about the sequence of a protein’s building blocks called amino acid residues, which form long chains.
“One possible sequential process which might lead a protein to land in a particular state, is the growth of the peptide chain on the ribosome, starting with the amino-terminal end and proceeding to the carboxy terminus.
With large amounts of genomic data available, imbibing machine learning for protein sequencing makes it easier which otherwise would have taken longer than the age of the universe.
“Our team focused specifically on the hard problem of modelling target shapes from scratch, without using previously solved proteins as templates.
We achieved a high degree of accuracy when predicting the physical properties of a protein structure, and then used two distinct methods to construct predictions of full protein structures,” noted the DeepMind’s jubilant team after their successful demonstration at CASP.
Be it diagnosing fatal diseases or engineering a bacteria to eat up the plastic, the knowledge of a protein structure enables us to tackle problems which were thought to be impossible and irreversible.
- On Friday, October 18, 2019
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