Lifeboat Foundation

Recent advancements in the development of machine learning and optimization techniques have opened new and exciting possibilities for identifying suitable molecular designs, compounds, and chemical candidates for different applications. Optimization techniques, some of which are based on machine learning algorithms, are powerful tools that can be used to select optimal solutions for a given problem among a typically large set of possibilities.

Researchers at Colorado State University and the National Renewable Energy Laboratory have been applying state-of-the-art molecular optimization models to different real-world problems that entail identifying new and promising molecular designs. In their most recent study, featured in Nature Machine Intelligence, they specifically applied a newly developed, open-source optimization framework to the task of identifying viable organic radicals for aqueous redox flow batteries, energy devices that convert chemical energy into electricity.

“Our project was funded by an ARPA-E program that was looking to shorten how long it takes to develop new energy materials using machine learning techniques,” Peter C. St. John, one of the researchers who carried out the study, told TechXplore. “Finding new candidates for redox flow batteries was an interesting extension of some of our previous work, including a paper published in Nature Communications and another in Scientific Data, both looking at organic radicals.”

The research community is excited about the potential of DNA to function as long-term archival storage. That’s largely because it’s extremely dense, chemically stable for tens of thousands of years, and comes in a format we’re unlikely to forget how to read. While there has been some interesting progress, efforts have mostly stayed in the research community because of the high costs and extremely slow read and write speeds. These are problems that need to be solved before DNA-based storage can be practical.

So we were surprised to hear that storage giant Seagate had entered into a collaboration with a DNA-based storage company called Catalog. To find out how close the company’s technology is to being useful, we talked to Catalog’s CEO, Hyunjun Park indicated that Catalog’s approach is counterintuitive on two levels: It doesn’t store data the way you’d expect, and it isn’t focusing on archival storage at all.

Chardan hosted its 4th Annual Chardan Genetic Medicines Conference in October 2020, featuring over 80 public and private companies representing in vivo gene therapy, ex vivo gene therapy, gene editing, RNA medicines, and other subsegments of the genetic medicines space. Among our various panels with preeminent thought leaders, we spoke with newly-minted Nobel laureate, President of the Innovative Genomics Institute, and Professor of Molecular and Cell Biology and Chemistry at UC Berkeley, Jennifer Doudna.

PhD about open questions and areas of innovation in the CRISPR gene editing space.

Development of medical treatment against cancer is a major research topic worldwide — but cancer often manages to circumvent the solutions found. Scientists around Tanja Weil and David Ng at the Max Planck Institute for Polymer Research (MPI-P), have now taken a closer look at the cancer’s countermeasures and aim to stop them. By disrupting the cellular components that are responsible for converting oxygen into chemical energy, they have demonstrated initial success in eliminating cells derived from untreatable metastatic cancer.

Treatment of cancer is a long-term process because remnants of living cancer cells often evolve into aggressive forms and become untreatable. Hence, treatment plans often involve multiple drug combinations and/or radiation therapy in order to prevent cancer relapse. To combat the variety of cancer cell types, modern drugs have been developed to target specific biochemical processes that are unique within each cell type.

However, cancer cells are highly adaptive and able to develop mechanisms to avoid the effects of the treatment. “We want to prevent such adaptation by invading the main pillar of cellular life — how cells breathe – that means take up oxygen — and thus produce chemical energy for growth,” says David Ng, group leader at the MPI-P.

If smoke indicates a fire, nitric oxide signals inflammation. The chemical mediator promotes inflammation, but researchers suspect it can do its job too well after anterior cruciate ligament (ACL) ruptures and related injuries and initiate early onset osteoarthritis. Typically, the degenerative disease is only diagnosed after progressive symptoms, but it potentially could be identified much earlier through nitric oxide monitoring, according to Huanyu “Larry” Cheng, James E. Henderson Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State.

Cheng and his student, Shangbin Liu, who earned a master’s degree in engineering science and mechanics at Penn State this year, collaborated with researchers based in China to develop a flexible biosensor capable of continuous and wireless nitric oxide detection in rabbits. They published their approach in the Proceedings of the National Academy of Sciences.

“Real-time assessment of biomarkers associated with inflammation, such as nitric oxide in the joint cavity, could indicate pathological evolution at the initial development of osteoarthritis, providing essential information to optimize therapies following traumatic knee injury,” Cheng said.

U.S. and European physicists have demonstrated a new method for predicting whether metallic compounds are likely to host topological states that arise from strong electron interactions.

Physicists from Rice University, leading the research and collaborating with physicists from Stony Brook University, Austria’s Vienna University of Technology (TU Wien), Los Alamos National Laboratory, Spain’s Donostia International Physics Center and Germany’s Max Planck Institute for Chemical Physics of Solids, unveiled their new design principle in a study published online today in Nature Physics.

The team includes scientists at Rice, TU Wien and Los Alamos who discovered the first strongly correlated topological semimetal in 2017. That system and others the new design principle seeks to identify are broadly sought by the quantum computing industry because topological states have immutable features that cannot be erased or lost to quantum decoherence.

Long-range interactions between electronically excited molecules and molecules at the ground state were found for distances of much more than 100 nm, as indicated by the dependence of the fluorescence lifetime on the concentration of dyes in diluted solutions. In contrast to this experimental result, the fluorescence lifetimes of distant isolated molecules should be independent from the concentration according to basic theory for light emission, such as that reported by Förster and Strickler–Berg. As a consequence, the theory of such emission should be modified for real systems to include electromagnetic interactions with distant resonating structures. Consequences of these findings concern many subjects, such as imaging methods (FLIM) in biochemistry.

Australian researchers have developed and tested a way to electrolyze hydrogen straight out of the air, anywhere on Earth, without requiring any other fresh water source. The Direct Air Electrolyzer (DAE) absorbs and converts atmospheric moisture – even down to a “bone-dry” 4% humidity.

Such a machine could be particularly relevant to a country like Australia, which has ambitions as a clean energy exporter, along with enormous solar energy potential – but also widespread drought conditions and limited access to clean water. Decoupling hydrogen production from the need for a water supply could allow green hydrogen to be produced more or less anywhere you can ship it out from – and since water scarcity and solar potential often go hand in hand, this could prove a boon for much of Africa, Asia, India and the Middle East, too.

Chemical engineers at Melbourne University came up with what they describe as a simple design: an electrolyzer with two flat plates acting as anode and cathode. Sandwiched between the two plates is a porous material – melamine sponge, for example, or sintered glass foam. This medium is soaked in a hygroscopic ionic solution – a chemical that can absorb moisture from the air spontaneously.

The equations of quantum mechanics provide a roadmap to predicting the properties of chemicals starting from basic scientific theories. However, these equations quickly become too expensive in terms of computer time and power when used to predict behavior in large systems. Machine learning offers a promising approach to accelerating such large-scale simulations.

Researchers have shown that machine learning models can mimic the basic structure of the fundamental laws of nature. These laws can be very difficult to simulate directly. The machine learning approach enables predictions that are easy to compute and are accurate in a wide range of chemical systems.

The improved machine learning model can quickly and accurately predict a wide range of properties of molecules (Proceedings of the National Academy of Sciences, “Deep Learning of Dynamically Responsive Chemical Hamiltonians with Semi-Empirical Quantum Mechanics”). These approaches score very well on important benchmarks in computational chemistry and show how deep learning methods can continue to improve by incorporating more data from experiments. The model can also succeed at challenging tasks such as predicting excited state dynamics—how systems behave with elevated energy levels.