For most of human history, the discovery of new materials has been a crapshoot. But now, UConn researchers have systematized the search with machine learning that can scan millions of theoretical compounds for qualities that would make better solar cells, fibers, and computer chips.
The search for new materials may never be the same.
No one knows why an early metallurgist decided to smelt a hunk of tin into some copper, but the resulting bronze alloy was harder and more durable than any material previously known. Most materials experimentation over the ensuing 7,000 years has been similarly random, guided largely by philosophy and chemical intuition.
But in a world that contains at least 95 stable elements – the basic building blocks of matter – the number of possible combinations is enormous, and experimentation is an awfully inefficient way to find what you’re looking for.
Enter UConn materials scientist Ramamurthy ‘Rampi’ Ramprasad. Instead of randomly mixing chemicals to see what they do, Ramprasad designs them rationally, using machine learning to figure out which atomic configurations make a polymer a good electrical conductor or insulator.
A polymer is a large molecule made of many repeating building blocks. Polymers are very common in both living and man-made materials. Probably the most familiar example is plastics, and the wide variation in plastics – which can be hard, soft, stretchy, brittle, spongy, clear, opaque or translucent – gives an inkling of how diverse polymers in general can be.
Polymers can also have diverse electronic properties. For example, they can be very good insulators – preventing electrons, and thus electric current, from traveling through them – or good conductors, allowing electricity to pass through them freely. And what controls all these properties is mainly how the atoms in the polymer connect to each other. But until recently, no one had systematically related properties to atomic configurations.
So Ramprasad and his colleagues decided to do just that. First, they would analyze known polymers, using laborious but accurate quantum mechanics-based calculations to figure out which arrangements of atoms confer which properties, and quantify those atomic-level relationships via a string of numbers that fingerprint each polymer. Once they had those, they could have a computer search through any number of theoretical polymers to figure out which ones might have which properties. Then anyone looking for a polymer with a certain property could quickly scan the list and decide which theoretical polymers might be worth trying.
Many polymers are made of building blocks containing just a few atoms. For instance, polyurea, a common plastic, has as the basic structure a repeating sequence of nitrogen (N), hydrogen (H) and oxygen (O): NH-O-NH-O. Most polymers look like that, made of carbon (C), H, N and O, with a few other elements thrown in occasionally.
For their project, Ramprasad’s group looked at polymers made of just seven building blocks: CH2, C6H4, CO, O, NH, CS, and C4H2S (the S is sulfur). These are found in common plastics such as polyethylene, polyesters, and polyureas. An enormous variety of polymers could theoretically be constructed using just these building blocks; Ramprasad’s group decided at first to analyze just 283, each composed of a repeated four-block unit.
They started from basic quantum mechanics, and calculated the three-dimensional atomic and electronic structures of each of those 283 four-block polymers. This is not trivial: calculating the position of every electron and atom in a molecule with more than two atoms takes a powerful computer a significant chunk of time, which is why they did it for only 283 molecules.
Learn more: Building a better mouse trap, from the atoms up
The Latest on: Machine learning
via Google News
The Latest on: Machine learning
- Associated factors of white matter hyperintensity volume: a machine-learning approachon January 27, 2021 at 8:23 am
To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We ...
- Pinecone exits stealth with a vector database for machine learningon January 27, 2021 at 7:51 am
Pinecone Systems Inc. is emerging from stealth mode today armed with $10 million in seed funding and a serverless vector database that it says can make machine learning queries much faster and more ...
- Pinecone leaves stealth with $10M, launches first serverless vector database for machine learningon January 27, 2021 at 7:16 am
PineconeSystems Inc., a machine learning (ML) cloud infrastructure company, left stealth today with $10m in seed funding. The investment ...
- Pinecone lands $10M seed for purpose-built machine learning databaseon January 27, 2021 at 7:01 am
Pinecone, a new startup from the folks who helped launch Amazon SageMaker, has built a vector database that generates data in a specialized format to help build machine learning applications faster, ...
- How AI and Machine Learning Will Shape Software Testingon January 27, 2021 at 6:03 am
In this special guest feature, Erik Fogg, Chief Operating Officer at ProdPerfect, covers some of the main benefits of adding AI to the software testing process, and why you should consider adding it ...
- Machine Learning Identifies Patterns of Maternal Autoantibodies Linked to Autismon January 27, 2021 at 5:00 am
The UC Davis research team says its study opens the door for more work on potential pre-conception testing, particularly useful for high-risk women older than 35 or who have already given birth to a ...
- Machine Learning as a Service Market Size 2021 with Regional Opportunities, Share, Trends, New Product Launches, Consumption Demand Forecast to 2024on January 27, 2021 at 2:16 am
Machine Learning as a Service Market Study 2017-2024 by Deployment Type ( Public Cloud, Private Cloud), By End-Use Application (Manufacturing, Retail, Healthcare & Life Sciences, BFSI, Travel & ...
- Pinecone, a serverless vector database for machine learning, leaves stealth with $10M fundingon January 26, 2021 at 11:00 pm
Machine learning applications understand the world through vectors. Pinecone, a specialized cloud database for vectors, has secured significant investment from the people who brought Snowflake to the ...
- 76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgetson January 26, 2021 at 7:29 pm
Enterprises accelerated their adoption of AI and machine learning in 2020, concentrating on those initiatives that deliver revenue growth and cost reduction.
- Threat Detection And Security Are Aided By Integrated Machine Learning / Artificial Intelligenceon January 21, 2021 at 10:41 am
Government buildings, airports, and other buildings at risk from terror attacks, whether from individuals or organizations, need more security that visual tracking of potential threats. In addressing ...
via Bing News