A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of relying on slow simulations that take weeks of supercomputer time, the system uses tensor network mathematics and machine-learning models to solve the problem directly. The approach can compute key thermodynamic properties hundreds of times faster while preserving accuracy. Researchers say this could accelerate discoveries in materials science, physics, and chemistry.
From the article:
“Each equation [for calculating π ] seemed unrelated to the others. But in late 2025, a team of seven AI researchers at the Technion–Israel Institute of Technology found a previously unknown mathematical structure underlying hundreds of pi formulas, including those of Archimedes, Euler and Ramanujan. “It’s not every day that you get to cite Archimedes,” says Ph.D. student Michael Shalyt, part of the team. The structure, called a conservative matrix field, or CMF, acts as a kind of mathematical common ancestor, showing how formulas that look nothing alike turn out to be different expressions of the same underlying object.”
A mixture of AI and algorithms uncovered a hidden structure spanning 2,000 years of equations for pi.
By Lyndie Chiou edited by Clara Moskowitz.
In 2013, physicist Alex Wissner-Gross published a single equation for intelligence in [ITALIC] Physical Review Letters [/ITALIC]: # F = T∇Sτ
The force of an intelligent system equals its temperature — computational capacity, raw horsepower — multiplied by the gradient of its future option-space. Intelligence is not a mysterious property of carbon-based brains.
It is a physical force: the tendency of any sufficiently energetic system to maximize the number of future states accessible to it.
The equation was elegant. Correct. And incomplete.
It describes the force. It does not describe the geometry of the space through which that force navigates.
A gradient without a metric is a direction without distance — it tells the system where to push but not what distortion it will encounter on the way there.
We spent three years building the geometry. We tested it across 69 billion simulations. What we found changes everything. ## The Missing Geometry — From Force to Navigation.
Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power.
Today, artificial intelligence and science find themselves at a similar inflection point. The current AI revolution has been fueled by decades of research in the mathematical and physical sciences (MPS), which provided the challenging problems, datasets, and insights that made modern AI possible. The 2024 Nobel Prizes in physics and chemistry, recognizing foundational AI methods rooted in physics and AI applications for protein design, made this connection impossible to miss.
In 2025, MIT hosted a Workshop on the Future of AI+MPS, funded by the National Science Foundation with support from the MIT School of Science and the MIT departments of Physics, Chemistry, and Mathematics. The workshop brought together leading AI and science researchers to chart how the MPS domains can best capitalize on — and contribute to — the future of AI. Now a white paper, with recommendations for funding agencies, institutions, and researchers, has been published in Machine Learning: Science and Technology. In this interview, Jesse Thaler, MIT professor of physics and chair of the workshop, describes key themes and how MIT is positioning itself to lead in AI and science.
Excellent review in which Solé et al. explore how physical/mathematical constraints may determine what subset of biological systems could theoretically evolve in the universe. Lots of fascinating ideas applying concepts like Turing machines, cellular automata, McCulloch-Pitts networks, energy minimization, and phase transitions to multiscale biological and evolutionary phenomena!
I found the description of how parasites almost inevitably emerge and drive increased biodiversity in computational models of evolution particularly fascinating. Interestingly, I recall this idea was featured in the Hyperion Cantos novels during an explanation of the history of artificial intelligence in their fictional universe!
Abstract. It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, multicellularity and development, the threshold nature of computations in cognitive systems and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.
At Argonne National Laboratory, researchers are trading in old-school approximations for raw supercomputing power, proving that the secret to a safer carbon-free future lies in mastering the math of chaos.
Researchers are advancing nuclear safety by using high-performance computing to model turbulent flow — the chaotic movement of fluids and gases that governs heat transfer and gas mixing within a reactor.
The COVID-19 pandemic brought messenger RNA (mRNA) vaccines to the forefront of global health care. After their clinical trial stages, the first COVID-19 mRNA vaccine was administered on 8 December 2020 and mathematical models suggest that mRNA vaccines prevented at least 14.4 million deaths from COVID-19 in the first year alone.
Their extraordinary effectiveness in having softened the blow of the disease has led to the development of mRNA vaccines to also combat other infectious pathogens.
Clinical trials for influenza virus, Respiratory Syncytial Virus (RSV), HIV, Zika, Epstein-Barr virus, and tuberculosis bacteria are all on the way. Importantly, however, COVID-19 research has revealed shortcomings of mRNA vaccines that highlight the need for different approaches.
