Saved in:
Bibliographic Details
Main Author: DE DOMINICIS, BRUNO
Format: Recurso digital
Language:English
Published: Zenodo 2026
Subjects:
Online Access:https://doi.org/10.5281/zenodo.20042320
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866901445830246400
author DE DOMINICIS, BRUNO
author_facet DE DOMINICIS, BRUNO
contents <p>Experimental data for atomic masses (AME2020), ionization energies (NIST), covalent bond energies, semiconductor band gaps, DNA base pairs, protein interactions, fundamental biochemical reactions, as well as the training of a ternary neural network, admit a compact arithmetic representation of the form:<br>  $$E = \Lambda \cdot 4^{m} \cdot \sum_{k=0}^{14} \varepsilon_k \beta_k$$<br>  where $\Lambda$ is a scale constant (7.726 MeV for masses, 5.950 eV for chemistry and biology), $m$ an integer, $\varepsilon_k \in \{-1,0,1\}$, and $\beta_k$ are 15 universal constants of empirical origin. For 5811 ionization energies, the mean relative error is 0.00065% and 78.5% of the points are predicted with an error better than 0.001%. The mean relative error for 295 nuclear masses is 0.0287%. The $\beta_k$ numerically coincide with 15 of the 48 non‑degenerate roots of the exceptional lattice $\Lambda_{72}$, suggesting a deep geometric link that remains to be elucidated. </p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_20042320
institution Zenodo
language eng
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Arithmetic unification of masses and energies in physics, chemistry, biology and artificial intelligence via the exceptional lattice $\Lambda_{72}$
DE DOMINICIS, BRUNO
nuclear masses
ionization energies
covalent bonds
DNA
proteins
ATP
lattice $\Lambda_{72}$
arithmetic quantization
ternary code
artificial intelligence
<p>Experimental data for atomic masses (AME2020), ionization energies (NIST), covalent bond energies, semiconductor band gaps, DNA base pairs, protein interactions, fundamental biochemical reactions, as well as the training of a ternary neural network, admit a compact arithmetic representation of the form:<br>  $$E = \Lambda \cdot 4^{m} \cdot \sum_{k=0}^{14} \varepsilon_k \beta_k$$<br>  where $\Lambda$ is a scale constant (7.726 MeV for masses, 5.950 eV for chemistry and biology), $m$ an integer, $\varepsilon_k \in \{-1,0,1\}$, and $\beta_k$ are 15 universal constants of empirical origin. For 5811 ionization energies, the mean relative error is 0.00065% and 78.5% of the points are predicted with an error better than 0.001%. The mean relative error for 295 nuclear masses is 0.0287%. The $\beta_k$ numerically coincide with 15 of the 48 non‑degenerate roots of the exceptional lattice $\Lambda_{72}$, suggesting a deep geometric link that remains to be elucidated. </p>
title Arithmetic unification of masses and energies in physics, chemistry, biology and artificial intelligence via the exceptional lattice $\Lambda_{72}$
topic nuclear masses
ionization energies
covalent bonds
DNA
proteins
ATP
lattice $\Lambda_{72}$
arithmetic quantization
ternary code
artificial intelligence
url https://doi.org/10.5281/zenodo.20042320