Isolating the first spark of life on Earth is a matter of biology, geology, and chemistry—but it’s also an amazing math problem. At least, that’s how Varun Varanasi viewed it when he was a Yale undergraduate. The question, in a nutshell, is this: How did the primordial soup of interacting molecules on the Earth’s surface billions of years ago transform itself from complete chaos to an organized system of self-sustaining, reproducing chemicals? Did this occur gradually over millions of years, or was it abrupt?
An international team of scientists, including researchers from Loughborough University, has developed a method to dramatically speed up the discovery and design of advanced materials. The study, published in Physical Review Letters, shows how the new approach can map complex phase diagrams in as little as a day—rather than weeks or months—and pinpoint where important structures, including crystals and quasicrystals, are likely to form.
The method will enable scientists to “scout ahead” and identify where promising structures are likely to form and the conditions needed to create them, rather than using a trial-and-error approach. It could help accelerate the development of advanced materials and technologies that harness the unique properties of quasicrystal structures.
“Our approach is a day’s work for an expert—it’s much faster,” said Professor Andrew Archer, an expert in applied mathematics and theoretical physics at Loughborough University and one of the paper’s authors.
What if creativity wasn’t magic—but math? In this video, we explore the mathematics of creativity through psychology, philosophy, and science. From Dean Keith Simonton’s law of large numbers, Margaret Boden’s theory of combinational creativity, Zipf’s Law, Malcolm Gladwell’s 10,000-hour curve, and even cellular automata—we break down how imagination follows hidden equations.
Whether you’re a student, teacher, scientist, engineer, or philosopher, this video will change how you think about art, science, and human innovation.
Chapters: 00:00 – Intro: Is Creativity Random? 00:34 – The Law of Large Numbers 01:42 – Zipf’s Law of Ideas 02:33 – Combinational Creativity (Boden) 03:15 – Time & Growth (Gladwell) 03:58 – Edge of Chaos (Complexity Theory) 04:48 – The Formula for Creativity.
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“It is impossible to be a mathematician without being a poet in soul.”-Sofia Kovalevskaya
We don’t often think of math as something that’s “dangerous” or “forbidden”; after all, what could be so dangerous about numbers? Russian-born Sofia Kovalevskaya was told at numerous points during her life that she had to stop studying math, that girls weren’t good enough, they weren’t allowed to go to school, or teach classes, edit magazines or win awards. Sofia Kovalevskaya never gave in to the couldn’t’s or wouldn’t’s. She fought time and again for her right to continue learning and teaching, eventually becoming one of the most celebrated mathematicians of her century and the first woman professor of a northern European University. Today, we celebrate Sofia and all the young mathematicians who overcome great odds!
When Sofia Kovalevskaya was a little girl in the early 1850’s, her room wasn’t wallpapered with flowers or meadowscapes, it was covered in pages and pages of math lecture not es. She would stare at the pages filled with differential and integral analysis, and while she didn’t understand exactly what she saw, Sofia saw beauty in the calculations.
Crystals, bacterial colonies, flame fronts: the growth of surfaces was first described in the 1980s by the Kardar–Parisi–Zhang equation. Since then, it has been regarded as a fundamental model in physics, with implications for mathematics, biology, and computer science.
Now—40 years later—a Würzburg-based research team from the Cluster of Excellence ctd.qmat has achieved the first experimental demonstration of KPZ behavior on 2D surfaces in space and time.
This was made possible by sophisticated materials engineering and a bold experimental approach: researchers injected polaritons—hybrid particles composed of light and matter—into the material. The results have been published in Science.
Can a wall get stronger the more it breaks, and greener the more it stands? Swiss scientists say buildings are about to start breathing and devouring carbon, and the concrete status quo will not like the math.
From a Zurich lab comes a building skin that inhales carbon, knits its own cracks and grows sturdier with time. Researchers at ETH Zurich embedded photosynthetic cyanobacteria in a 3D printed hydrogel, creating a living material that draws down CO₂ and strengthens over time, its chlorophyll tinting it green. Across 400 days of testing, a prototype matched the yearly uptake of a 20-year-old pine, pulling in up to 18 kilograms of CO₂, while each gram of the base material fixes about 26 milligrams. Detailed in Nature Communications on April 6, 2026 and co-authored by Mark Tibbitt, the work points to facades that do carbon duty as part of everyday architecture.
Some breakthroughs feel both surprising and oddly familiar, like rediscovering a tool nature kept in plain sight. Swiss scientists have blended biology with architecture to shape a new kind of material that lives with its surroundings. It repairs small cracks, it sips CO2 from the air, and it quietly strengthens with time. The promise is simple, and bold: buildings that help clean the sky.
David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to detect sudden changes in fluid behavior, improving speed and the cost of identifying these instabilities and overcoming one of the major obstacles faced when using machine learning to simulate physical systems. The findings are published in the Journal of Computational Physics.
Computational simulations of mathematical models of fluid flow are essential for everyday applications ranging from predicting the weather to the assessment of nuclear reactor safety. The advent of this simulation capability over the past 50 years has revolutionized the development of fuel-efficient airplanes, and sail configurations on racing yachts can now be optimized in real time, providing the marginal gains needed to win races in the America’s Cup.
Optimized aerodynamics means that modern day cyclists can ride faster, golf balls fly further and Olympic swimmers consistently set world records. Computational fluid dynamics also enables the modeling of the flow of blood in the human heart, making the provision of patient-specific surgery possible.
For years, Rutgers physicist David Shih solved Rubik’s Cubes with his children, twisting the colorful squares until the scrambled puzzle returned to order. He didn’t expect the toy to connect to his research, but recently he realized the logic behind the puzzle was exactly what he needed to solve a problem involving particle physics.
That idea led to a new artificial intelligence (AI) method that can simplify some of the extremely complex equations used in particle physics. Shih described the method in a study posted to the arXiv preprint server, a widely used site where scientists share new research.
“In reaching our solutions, we found that an analogy between mathematical simplification and solving Rubik’s Cubes was key,” said Shih, a professor in the Department of Physics and Astronomy at the Rutgers School of Arts and Sciences. “Both can be viewed as scrambling and unscrambling problems.”
Can living neurons replace AI? A new study shows that biological neural networks (BNNs) can be trained to perform reservoir computing. Researcher used rat neurons to generate complex time-series signals and chaotic trajectories like the Lorenz attractor.
Many drivers will know the feeling: you pull ahead of the slower car you’ve been stuck behind and cruise the open road ahead at your own, faster speed. By the time you reach the next stop light, you’re sure that you’ve left the slower car far behind you—but to your surprise, you see that same car cruise up right behind you in the mirror. Horror buffs might even recall scenes from “Friday the 13th,” where masked villain Jason Voorhees always catches up to his sprinting victims—despite himself walking at a leisurely pace.
In a new study published in Royal Society Open Science, Conor Boland at Dublin City University shows that this unsettlingly common phenomenon can be explained with simple mathematics. His model reveals precisely when and why a slower vehicle catches up after being overtaken, offering fresh insights into how individual vehicles interact with traffic signals.
