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The researchers simulated the molecules H4, molecular nitrogen, and solid diamond. These involved as many as 120 orbitals, the patterns of electron density formed in atoms or molecules by one or more electrons. These are the largest chemistry simulations performed to date with the help of quantum computers.

A classical computer actually handles most of this fermionic quantum Monte Carlo simulation. The quantum computer steps in during the last, most computationally complex step—calculating the differences between the estimates of the ground state made by the quantum computer and the classical computer.

The prior record for chemical simulations with quantum computing employed 12 qubits and a kind of hybrid algorithm known as a variational quantum eigensolver (VQE). However, VQEs possess a number of limitations compared with this new hybrid approach. For example, when one wants a very precise answer from a VQE, even a small amount of noise in the quantum circuitry “can cause enough of an error in our estimate of the energy or other properties that’s too large,” says study coauthor William Huggins, a quantum physicist at Google Quantum AI in Mountain View, Calif.

Janice Chen, Ph.D., one of Olympic gold medalist Nathan Chen’s siblings, is on a mission to build a $100 billion biotech company.

In 2018, she co-founded Mammoth Biosciences with Trevor Martin, Lucas Harrington and Jennifer Doudna 0, who won the Nobel Prize in Chemistry two years later for her pioneering work in CRISPR gene editing. Doudna also served as Chen’s mentor while she pursued her doctorate degree in molecular and cell biology at the University of California at Berkeley.

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Quantum computers are getting bigger, but there are still few practical ways to take advantage of their extra computing power. To get over this hurdle, researchers are designing algorithms to ease the transition from classical to quantum computers. In a new study in Nature, researchers unveil an algorithm that reduces the statistical errors, or noise, produced by quantum bits, or qubits, in crunching chemistry equations.

Developed by Columbia chemistry professor David Reichman and postdoc Joonho Lee with researchers at Google Quantum AI, the algorithm uses up to 16 qubits on Sycamore, Google’s 53-qubit computer, to calculate ground state energy, the lowest energy state of a molecule. “These are the largest quantum chemistry calculations that have ever been done on a real quantum device,” Reichman said.

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Artificial intelligence advances how scientists explore materials. Researchers from Ames Laboratory and Texas A&M University trained a machine-learning (ML) model to assess the stability of rare-earth compounds. This work was supported by Laboratory Directed Research and Development Program (LDRD) program at Ames Laboratory. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.

Ames Lab has been a leader in rare-earths research since the middle of the 20th century. Rare earth elements have a wide range of uses including clean energy technologies, energy storage, and permanent magnets. Discovery of new rare-earth compounds is part of a larger effort by scientists to expand access to these materials.

The present approach is based on machine learning (ML), a form of artificial intelligence (AI), which is driven by computer algorithms that improve through data usage and experience. Researchers used the upgraded Ames Laboratory Rare Earth database (RIC 2.0) and high-throughput density-functional theory (DFT) to build the foundation for their ML model.

Engineers at the University of Cincinnati have developed a promising electrochemical system to convert emissions from chemical and power plants into useful products while addressing climate change.

UC College of Engineering and Applied Science assistant professor Jingjie Wu and his students used a two-step cascade reaction to convert carbon dioxide to carbon monoxide and then into ethylene, a chemical used in everything from food packaging to tires.

“The world is in a transition to a low-carbon economy. Carbon dioxide is primarily emitted from energy and chemical industries. We convert carbon dioxide into ethylene to reduce the carbon footprint.” Wu said. “The research idea is inspired by the basic principle of the plug flow reactor. We borrowed the reactor design principle in our segmented electrodes design for the two-stage conversion.”

For me, the concern was just how easy it was to do. A lot of the things we used are out there for free. You can go and download a toxicity dataset from anywhere. If you have somebody who knows how to code in Python and has some machine learning capabilities, then in probably a good weekend of work, they could build something like this generative model driven by toxic datasets. So that was the thing that got us really thinking about putting this paper out there; it was such a low barrier of entry for this type of misuse.

AI could be just as effective in developing biochemical weapons as it is in identifying helpful new drugs, researchers warn.

Ever since its discovery in 2004, graphene has received attention owing to its extraordinary properties, among them its extremely high carrier mobility. However, the high carrier mobility has only been observed using techniques that require complex and expensive fabrication methods. Now, researchers at Chalmers report on a surprisingly high charge-carrier mobility of graphene using much cheaper and simpler methods.

“This finding shows that graphene transferred to cheap and flexible substrates can still have an uncompromisingly high mobility, and it paves the way for a new era of graphene nano-electronics,” says Munis Khan, researcher at Chalmers University of Technology.

Graphene is the one-atom-thick layer of carbon atoms, known as the world’s thinnest material. The material has become a popular choice in semiconductor, automotive and optoelectronic industry due to its excellent electrical, chemical, and material properties. One such property is its extremely high carrier mobility.

A research team from KTH Royal Institute of Technology and Max Planck Institute of Colloids and Interfaces reports to have found the key to controlled fabrication of cerium oxide mesocrystals. The research is a step forward in tuning nanomaterials that can serve a wide range of uses—including solar cells, fuel catalysts and even medicine.

Mesocrystals are nanoparticles with identical size, shape and crystallographic orientation, and they can be used as building blocks to create artificial nanostructures with customized optical, magnetic or electronic properties. In nature, these three-dimensional structures are found in coral, sea urchins and calcite desert rose, for example. Artificially-produced cerium oxide (CeO₂) mesocrystals—or nanoceria—are well-known as catalysts, with antioxidant properties that could be useful in pharmaceutical development.

“To be able to fabricate CeO₂ mesocrystals in a controlled way, one needs to understand the formation mechanism of these materials,” says Inna Soroka, a researcher in applied physical chemistry at KTH. She says the team used radiation chemistry to reveal for the first time the ceria mesocrystal formation mechanism.

Re-engineering clinical trials around participants — katie baca-motes, co-founder, scripps research digital trials center, scripps research.

Katie Baca-Motes, MBA, (https://www.scripps.edu/science-and-medicine/translational-i…aca-motes/) is Senior Director, Strategic Initiatives at the Scripps Research Translational Institute, and Co-Founder of the Scripps Research Digital Trials Center (https://digitaltrials.scripps.edu/).

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