Enzymes Embedded in Plastics

Based on a conversation I have had, this is a considerable breakthrough with broad application.

Enzymes successfully embedded in plastics

Research News / June 01, 2021  Fraunhofer Institute

In general, plastics are processed at way over a hundred degrees Celsius. Enzymes, by contrast, cannot usually withstand these high temperatures. Researchers at the Fraunhofer Institute for Applied Polymer Research IAP have managed to reconcile these contradictions: They are able to embed enzymes in plastics without the enzymes losing their activity in the process. The potentials this creates are enormous.

Materials that clean themselves, have anti-mold surfaces or are even self-degrading are just a few examples of what will be possible if we manage to embed active enzymes into plastics. But for the enzyme-specific properties to be transferred to the materials, the enzymes must not suffer damage as they are embedded in the plastic. Scientists at Fraunhofer IAP have developed a solution to the problem as part of the “Biofunctionalization/Biologization of Polymer Materials BioPol” project. Since summer 2018, the project has been running in cooperation with BTU Cottbus-Senftenberg. The Ministry of Science, Research and Culture of the State of Brandenburg is funding the project.

“It was clear from the outset that we were not looking to produce biofunctionalized plastics on a laboratory scale. We wanted to take a giant step to show that technical production is possible,” says Dr. Ruben R. Rosencrantz, Head of the “Biofunctionalized Materials and (Glyco)Biotechnology” department at Fraunhofer IAP, summarizing the ambitious project goals. At around the midpoint in the project, major breakthroughs are already emerging: Enzymes have been successfully embedded, both in terms of the enzymes themselves and the processing technique.  ..." 

Neural Nets Used to Rethink Material Design

Somewhat different approach, training from equations.  Closer to the efforts we used during the early uses of neural nets.  Combining learning methods and existing algorithms.

Neural Nets Used to Rethink Material Design

Rice University News, April 30, 2021

A technique developed by researchers at Rice University and Lawrence Livermore National Laboratory uses machine learning to predict the evolution of microstructures in materials. The researchers demonstrated that neural networks can train themselves to predict a structure's growth in a particular environment. The researchers trained their neural networks using data from the traditional equation-based approach to predict microstructure changes and tested them on four microstructure types: plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The neural networks were 718 times faster for grain growth when powered by graphic processors compared to the prior algorithm, and 87 times faster when run on a standard central processor. Rice's Ming Tang said the new method can "make predictions even when we do not know everything about the material properties in a system," and will be useful in designing more efficient batteries.

AI Agents for Lab Work

 Can see this useful for other kinds of 'labs',  A lab is a business process with specific goals, and scientific constraints, like those we worked with.  Better, faster cheaper, compliantly.  Carefully trained.  

AI Agent Helps Identify Material Properties Faster,  By Ruhr-University Bochum (Germany), April 27, 2021

Researchers at Brookhaven National Laboratory, the University of Liverpool in the U.K., and the Ruhr-University Bochum in Germany demonstrated that artificial intelligence (AI) can speed up X-ray diffraction data (XRD) analysis and improve accuracy in searches for new materials.

The researchers developed an AI agent, Crystallography Companion Agent (XCA), that collaborates with scientists when it comes to decision-making,  XCA can perform autonomous phase identifications from XRD data while it is measured and works with both organic and inorganic material systems.

The algorithm was trained using a large-scale simulation of physically correct X-ray diffraction data.

Ruhr's Lars Banko said the decision-making process "is simulated by an ensemble of neural networks, similar to a vote among experts. This is accomplished without manual, human-labelled data and is robust to many sources of experimental complexity."

Ruhr's Alfred Ludwig called the research "an important step in accelerating the discovery of new materials."

From Ruhr-University Bochum (Germany)

Phonons and Quantum Monte Carlo

More looks at material science.

A Successful Phonon Calculation Within the Quantum Monte Carlo Framework  By Japan Advanced Institute of Science and Technology  April 6, 2021

Phonon dispersion of diamond calculated at the variational Monte Carlo level by TurboRVB Image.

An international team of scientists has enhanced quantum Monte Carlo (QMC) computation speeds based on error reduction, and conducted proof-of-concept calculations for a periodic solid.

They found this method can lower statistical error in atomic-force evaluation by two orders of magnitude, and accelerate computation 10,000-fold.

The researchers used the technique to calculate the atomic vibrations of diamond on the Japan Advanced Institute of Science and Technology (JAIST)'s Cray-XC40 T computer, and the results were consistent with experimental values.

JAIST's Kousuke Nakano said, "The drastic reduction in computational time will greatly expand the range of QMC calculations and enable highly accurate prediction of atomic properties of materials that have been difficult to handle."

From Japan Advanced Institute of Science and Technology

The Value of Advanced Material Science

A long time follower, because of some of my astrophysics connections made me understand the potential of doing things more efficiently.    And you start that with considering what its made of. 

 Material Science, the Unsung Here  By Peter H. Diamandis  

Few people recognize the vast implications of materials science.

To build today’s smartphone in the 1980s, it would cost about $110 million, require nearly 200 kilowatts of energy, and the device would be 14 meters tall, according to Applied Materials CTO Omkaram Nalamasu.

That's the power of materials advances. Materials science has democratized smartphones, bringing the technology to the pockets of over 3.5 billion people.

But far beyond devices and circuitry, materials science stands at the center of innumerable breakthroughs across energy, future cities, transit, and medicine.

As the name suggests, materials science is the branch devoted to the discovery and development of new materials. It’s an outgrowth of both physics and chemistry, using the periodic table as its grocery store and the laws of physics as its cookbook.

And today, we are in the middle of a materials science revolution. In this blog, we’ll unpack the most important materials advancements happening now.

Let’s dive in… 

THE MATERIALS GENOME INITIATIVE

In June 2011 at Carnegie Mellon University, President Obama announced the Materials Genome Initiative, a nationwide effort to use open source methods and AI to double the pace of innovation in materials science ....

By using AI to map the hundreds of millions of different possible combinations of elements—hydrogen, boron, lithium, carbon, etc.—the initiative created an enormous database that allows scientists to play a kind of improv jazz with the periodic table.

This new map of the physical world lets scientists combine elements faster than ever before and is helping them create all sorts of novel elements.

And an array of new fabrication tools are further amplifying this process, allowing us to work at altogether new scales and sizes, including the atomic scale, where we’re now building materials one atom at a time.  ... "

Material AI Invention and Discovery

New directions in new material discovery.  As it mentions, AI-driven molecular driven platform that is said to invent new molecular structure

IBM launches AI platform to discover new materials

Kyle Wiggers  @Kyle_L_Wiggers   in Venturebeat

IBM today announced the launch of the Molecule Generation Experience (MolGX), a cloud-based, AI-driven molecular design platform that automatically invents new molecular structures. MolGX, a part of IBM’s overarching strategy that aims to accelerate the discovery of new materials by 10 to 100 times, uncovers materials from the property targets of a given product.

The chemical sciences have made strides in the discovery of novel and useful materials over the past decades. For example, in the area of polymers, the recent development of thermoplastics has had an influence on applications ranging from new paints to clothing fibers. But while the discovery of new materials is the driving force in the expansion and improvement of industrial products, the vastness of chemical space likely exceeds the ability of human experts to explore even a fraction of it.  .. "