Lifeboat Foundation

Scientists from the Dutch Institute for Fundamental Energy Research (DIFFER) have created a database of 31,618 molecules that could potentially be used in future redox-flow batteries. These batteries hold great promise for energy storage. Among other things, the researchers used artificial intelligence and supercomputers to identify the molecules’ properties. Today, they publish their findings in the journal Scientific Data.

In recent years, chemists have designed hundreds of molecules that could potentially be useful in flow batteries for energy storage. It would be wonderful, researchers from DIFFER in Eindhoven (the Netherlands) imagined, if the properties of these molecules were quickly and easily accessible in a database. The problem, however, is that for many molecules the properties are not known. Examples of molecular properties are redox potential and water solubility. Those are important since they are related to the power generation capability and energy density of redox flow batteries.

To find out the still-unknown properties of molecules, the researchers performed four steps. First, they used a desktop computer and smart algorithms to create thousands of virtual variants of two types of molecules. These molecule families, the quinones and aza aromatics, are good at reversibly accepting and donating electrons. That is important for batteries. The researchers fed the computer with backbone structures of 24 quinones and 28 aza-aromatics plus five different chemically relevant side groups. From that, the computer created 31,618 different molecules.

An algorithm that already predicts how proteins fold might also shed light on the physical principles that dictate this folding.

Proteins control every cell-level aspect of life, from immunity to brain activity. They are encoded by long sequences of compounds called amino acids that fold into large, complex 3D structures. Computational algorithms can model the physical amino-acid interactions that drive this folding [1]. But determining the resulting protein structures has remained challenging. In a recent breakthrough, a machine-learning model called AlphaFold [2] predicted the 3D structure of proteins from their amino-acid sequences. Now James Roney and Sergey Ovchinnikov of Harvard University have shown that AlphaFold has learned how to predict protein folding in a way that reflects the underlying physical amino-acid interactions [3]. This finding suggests that machine learning could guide the understanding of physical processes too complex to be accurately modeled from first principles.

Predicting the 3D structure of a specific protein is difficult because of the sheer number of ways in which the amino-acid sequence could fold. AlphaFold can start its computational search for the likely structure from a template (a known structure for similar proteins). Alternatively, and more commonly, AlphaFold can use information about the biological evolution of amino-acid sequences in the same protein family (proteins with similar functions that likely have comparable folds). This information is helpful because consistent correlated evolutionary changes in pairs of amino acids can indicate that these amino acids directly interact, even though they may be far in sequence from each other [4, 5]. Such information can be extracted from the multiple sequence alignments (MSAs) of protein families, determined from, for example, evolutionary variations of sequences across different biological species.

Kathryn Tunyasuvunakool grew up surrounded by scientific activities carried out at home by her mother—who went to university a few years after Tunyasuvunakool was born. One day a pendulum hung from a ceiling in her family’s home, Tunyasuvunakool’s mother standing next to it, timing the swings for a science assignment. Another day, fossil samples littered the dining table, her mother scrutinizing their patterns for a report. This early exposure to science imbued Tunyasuvunakool with the idea that science was fun and that having a career in science was an attainable goal. “From early on I was desperate to go to university and be a scientist,” she says.

Tunyasuvunakool fulfilled that ambition, studying math as an undergraduate, and computational biology as a graduate student. During her PhD work she helped create a model that captured various elements of the development of a soil-inhabiting roundworm called Caenorhabditis elegans, a popular organism for both biologists and physicists to study. She also developed a love for programming, which, she says, lent itself naturally to a jump into software engineering. Today Tunyasuvunakool is part of the team behind DeepMind’s AlphaFold—a protein-structure-prediction tool. Physics Magazine spoke to her to find out more about this software, which recently won two of its makers a Breakthrough Prize, and about why she’s excited for the potential discoveries it could enable.

All interviews are edited for brevity and clarity.

While many studies have investigated the underpinnings of the mammalian motor system (i.e., the collection of neural networks that allow mammals to move in specific ways), some questions remain unanswered. One of these questions relates to the ways in which recurring or stable behaviors are maintained in the brain.

Some theories and research findings suggest that the neural activity underlying stable behaviors is itself very stable. Others, however, hinted at the possibility that the activity of individual motor neurons might change considerably over time, despite the production of similar behavioral patterns.

Researchers at Harvard University have recently tried to move toward the resolution of this long-standing debate, by observing the behavior and neural activity of rodents. Their findings, published in Nature Neuroscience, suggest that the activity of single neurons in brain regions associated with movement and physical behavioral patterns is highly stable over time.

Cornell University researchers have created an interface that allows users to handwrite and sketch within computer code—a challenge to conventional coding, which typically relies on typing.

The pen-based interface, called Notate, lets users of computational, digital notebooks open drawing canvases and handwrite diagrams within lines of traditional, digitized computer code.

Continue reading “New programming tool turns sketches, handwriting into code” »

Benchmarks orient AI. They encapsulate ideals and priorities that describe how the AI community should progress. When properly developed and analyzed, they allow the larger community to understand better and influence the direction of AI technology. The AI technology that has evolved the most in recent years is foundation models, highlighted by the advent of language models. A language model is essentially a box that accepts text and generates text. Despite their simplicity, these models may be customized (e.g., prompted or fine-tuned) to a wide range of downstream scenarios when trained on vast amounts of comprehensive data. However, there still needs to be more knowledge on the enormous surface of model capabilities, limits, and threats. They must benchmark language models holistically due to their fast growth, growing importance, and limited comprehension. But what does it mean to evaluate language models from a global perspective?

Language models are general-purpose text interfaces that may be used in various circumstances. And for each scenario, they may have a long list of requirements: models should be accurate, resilient, fair, and efficient, for example. In truth, the relative relevance of various desires is frequently determined by one’s perspective and ideals and the circumstance itself (e.g., inference efficiency might be of greater importance in mobile applications). They think that holistic assessment includes three components:

Will AI kill art? Not likely, says the artist Alexander Reben, who has been working with AI for years.

UC San Diego nanoengineering professor Shyue Ping Ong described M3GNet as “an AlphaFold for materials”, referring to the breakthrough AI algorithm built by Google’s DeepMind that can predict protein structures.

“Similar to proteins, we need to know the structure of a material to predict its properties,” said Professor Ong.

“We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures.”

This CEO says generative AI is all flash, no substance — and ultimately, will fail to generate major new revenue streams for VCs’ billions.