Creating complex molecules usually requires years of experience and countless decisions, but a new AI system is changing that. Synthegy lets chemists guide synthesis and reaction planning using simple language, while powerful algorithms generate and evaluate possible solutions. The AI doesn’t just compute—it reasons, scoring pathways and explaining which ones make the most sense.
Finding and developing new molecules is one of the great research endeavors of modern chemistry. From the development of new drugs to the creation of more sustainable materials, everything depends on finding new combinations of atoms with useful properties. Now, a research team from the Universitat Rovira i Virgili (URV) has developed an artificial intelligence tool capable of generating millions of new molecules which, although still unknown to science, comply with the laws of chemistry and could therefore be realistic possibilities. The research results have been published in the journal Nature Machine Intelligence.
The system, called CoCoGraph, works in a similar way to generative artificial intelligence tools for text or images, such as ChatGPT or Dall-E. “These models create new content that looks very much like the real thing. Our algorithm does the same, but with molecules,” explains Roger Guimerà, an ICREA Research Professor in the Department of Chemical Engineering at the URV.
Unlike other AI tools, however, the model does not yet respond to specific instructions. For the moment it simply carries out the more basic task of generating plausible molecules, that is, structures that comply with the rules of chemistry.
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Kurt Gödel discovered a solution to General Relativity that allows time travel without any exotic physics, revealing that the theory doesn’t actually guarantee a consistent chain of cause and effect. His “Gödel universe” shows that under certain conditions, the structure of spacetime itself can loop back on itself—blurring the line between past and future and exposing a deep limitation in our understanding of reality.
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Large language models (LLMs) could help human scientists identify interesting research topics that have not previously been explored, say scientists at Germany’s Karlsruhe Institute of Technology (KIT). By analysing abstracts in materials science publications and mapping connections between different concepts, the model was able to generate predictions for future areas of interest that the KIT team says are more precise than those produced by traditional, rule-based algorithms.
The number of research articles published each year is increasing so quickly that it is impossible for scientists to keep up with everything, observes team leader Pascal Friederich, who heads a KIT research group on artificial intelligence for materials sciences. While experienced scientists know how to find connections between research areas within their field, identifying links between these and other, unfamiliar topics is a different story.
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In his essay “The Unreasonable Effectiveness of Mathematics”, the physicist Eugine Wigner said that “the enormous usefulness of mathematics in the natural sciences is something bordering on the mysterious”. This statement was inspired by the observation that so many aspects of the physical world seem to be describable and predictable by mathematical equations to incredible precision especially as quantum phenomena. But quantum phenomena have no subjective qualities and have questionable physicality. They seem to be completely describable by only numbers, and their behavior precisely defined by equations. In a sense, the quantum world is made of math. So does that mean the universe is made of math too? If you believe the Mathematical Universe Hypothesis then yes. And so are you.
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If you used every particle in the observable universe to do a full quantum simulation, how big would that simulation be? At best a large molecule. That’s how insanely information dense the quantum wavefunction really is. And yet we routinely simulate systems with thousands, even millions of particles. How? By cheating. Using the ultimate compression algorithm: Density Functional Theory (DFT). Let’s learn how to cheat the universe.
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Penn Engineers have developed a new way to use AI to solve inverse partial differential equations (PDEs), a particularly challenging class of mathematical problems with broad implications for understanding the natural world.
The advance, which the researchers call “Mollifier Layers,” could benefit fields as varied as genetics and weather forecasting, because inverse PDEs help scientists work backward from observable patterns to infer the hidden dynamics that produced them.
“Solving an inverse problem is like looking at ripples in a pond and working backward to figure out where the pebble fell,” says Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Materials Science and Engineering (MSE) and senior author of a study published in Transactions on Machine Learning Research (TMLR), which will be presented at the Conference on Neural Information Processing Systems (NeurIPS 2026). “You can see the effects clearly, but the real challenge is inferring the hidden cause.”
The concept of spacetime, first described in Einstein’s theory of general relativity, has since been widely studied by many physicists worldwide. Spacetime is described mathematically as a four-dimensional (4D) continuum in which physical events occur, which merges three-dimensional (3D) space, with one-dimensional (1D) time.
This 4D continuum is known to continuously evolve following complex and intricate patterns that are governed by Einstein’s field equations; mathematical equations that describe how matter and energy shape spacetime. While various past theoretical studies explored the evolution of spacetime, identifying patterns that persist during its evolution has proved challenging so far.
Researchers at Adolfo Ibáñez University in Chile and Columbia University set out to explore the evolution of spacetime using ideas rooted in nonlinear electrodynamics, an area of physics that studies the behavior of electric and magnetic fields in complex materials.