Tootfinder

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales, Kin Long Kelvin Lee, Nitya Nand Gosvami, Sayan Ranu, Santiago Miret, N M Anoop Krishnan
https://arxiv.org/abs/2508.05762

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evaluation has been limited to computational benchmarks that may not reflect real-world performance. Here, we present UniFFBench, a comprehensive framework for evaluating UMLFFs against experimental measurements of ~1,500 carefully curated mineral structures spanning diverse chemical environments, bonding types, structural …

Some one invented a new element just to make up for a secret word that they can’t make up with element already in the Periodic Table.

@… Oooh, I’m gonna show this to my kid. They love the periodic table! Thank you for the accidental recommendation. 😃

Absence of two-orbital superconductivity in cuprate family: A DFT DMFT perspective
Jian-Hong She, Jing-Xuan Wang, Rong-Qiang He, Zhong-Yi Lu
https://arxiv.org/abs/2509.08823 htt…

Absence of two-orbital superconductivity in cuprate family: A DFT+DMFT perspective
The recent discovery of high-temperature superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_7$ has spurred intense interest in exploring analogous mechanisms in other transition metal oxides. This raises a pivotal question: can cuprates, as neighbors to nickelates in the periodic table, host similar two-orbital superconductivity? Here, we systematically investigate the electronic structure of a series of designed Ruddlesden-Popper cuprates. Our calculations reveal that the parent compoun…

Learning from the electronic structure of molecules across the periodic table
Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao, Juno Nam, Daniel S. Levine
https://arxiv.org/abs/2510.00224

Learning from the electronic structure of molecules across the periodic table
Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training set size. Meanwhile, the even greater quantities of accompanying data in the Hamiltonian matrix H behind these datasets has so far gone unused for this purpose. Here, we provide a recipe for integrating the orbital interaction data within H towards training pipelines for atomic-level properties. We first introduce HEL…

The Periodic Table Of Finite Simple Groups

Benchmarking Universal Interatomic Potentials on Zeolite Structures
Shusuke Ito, Koki Muraoka, Akira Nakayama
https://arxiv.org/abs/2509.07417 https://arxi…

Benchmarking Universal Interatomic Potentials on Zeolite Structures
Interatomic potentials (IPs) with wide elemental coverage and high accuracy are powerful tools for high-throughput materials discovery. While the past few years witnessed the development of multiple new universal IPs that cover wide ranges of the periodic table, their applicability to target chemical systems should be carefully investigated. We benchmark several universal IPs using equilibrium zeolite structures as testbeds. We select a diverse set of universal IPs encompassing two major catego…

Quantized Giant Gravitons as the Periodic Table of Supersymmetric States: D3, M2 and M5
Evan Deddo, Sabarenath Jayaprakash, James T. Liu, Leopoldo A. Pando Zayas
https://arxiv.org/abs/2509.18252

What Do Bouncing Balls Tell Us About the Universe? A Journey into Billiard Systems
Weiqi Chu, Matthew Dobson
https://arxiv.org/abs/2508.18519 https://arxiv…

What Do Bouncing Balls Tell Us About the Universe? A Journey into Billiard Systems
Have you ever played or watched a game of pool? If so, you have already seen a billiard system in action. In mathematics and physics, a billiard system describes a ball that moves in straight lines and bounces off walls. Despite these simple rules, billiard systems can produce remarkably rich behaviors: some table shapes generate regular, periodic patterns, while others give rise to complete chaos. Scientists also study what happens when we shrink the ball down to the size of an electron to a w…

Next highest weight and other lower $SU(3)$ irreducible representations with proxy-$SU(4)$ symmetry for nuclei with $32 \le \mbox{Z,N} \le 46$
V. K. B. Kota
https://arxiv.org/abs/2510.00800

Next highest weight and other lower $SU(3)$ irreducible representations with proxy-$SU(4)$ symmetry for nuclei with $32 \le \mbox{Z,N} \le 46$
In the applications of proxy-SU(3) model in the context of determining $(β,γ)$ values for nuclei across the periodic table, for understanding the preponderance of triaxial shapes in nuclei with $Z \ge 30$, it is seen that one needs not only the highest weight (hw) or leading $SU(3)$ irreducible representation (irrep) $(λ_H, μ_H)$ but also the lower $SU(3)$ irreps $(λ,μ)$ such that $2λ+ μ=2λ_H + μ_H-3r$ with $r=0,1$ and $2$ [Bonatsos et al., Symmetry {\bf 16}, 1625 (2024)]. These give …

A High-Throughput Search for Stable and Magnetically Robust Fe$_3$XY$_2$ Monolayers
Soheil Ershadrad, Biplab Sanyal
https://arxiv.org/abs/2509.02199 https://

A High-Throughput Search for Stable and Magnetically Robust Fe$_3$XY$_2$ Monolayers
We present first principles exploration of 529 Fe$_3$XY$_2$ compounds, where $X$ and $Y$ elements are selected from the $p$-block of the periodic table. Out of the entire set, 31 compounds satisfy all criteria for energetic, dynamic, mechanical, and thermal stability. Our analysis reveals several key trends: halide-containing systems exhibit the highest average magnetic moments and the highest magnetic transition temperatures, highlighting their potential for room-temperature spintronic applica…

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
Ardavan Mehdizadeh, Peter Schindler
https://arxiv.org/abs/2508.21663

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials properties across the periodic table. Despite their remarkable success in predicting bulk properties, no systematic evaluation has assessed how well these universal MLIPs (uMLIPs) can predict cleavage energies, a critical property governing fracture, catalys…