array(14 items) uid => 950 (integer) title => 'Are the chemical families still there? Formal structure of similarity of ele
ments and its thermochemical domain' (111 chars) abstract => 'The periodic table organizes chemical elements into families based on their
similarity, now understood through Quantum Mechanics. However, these familie
s were inferred from limited compounds in the nineteenth century. Since then
, the number of compounds has exponentially grown, leading to the discovery
of new types unknown to pioneers. This situation prompts the question of whe
ther these families can still be discerned from the amassed data, or if thei
r recognition is confined to specific thermochemical domains. To address thi
s inquiry, we conducted a comprehensive exploration by comparing formulae (1
771–2015) as a proxy for chemical similarity. Our findings reveal that sto
ichiometry not only captures a significant portion of the trends observed wi
thin families but also unveils other intriguing features of the formal struc
ture of chemical similarity. These patterns approach equivalence classes ind
ependent of thermochemical context and demonstrate high resilience. Temporal
analysis demonstrates that, since approximately 1980, similarity is diminis
hing due to an increasing production of unique formulae for nearly all eleme
nts. Nevertheless, chemical families endure over time and they stand out as
the most robust similarity patterns. Our analysis offers compelling evidence
that any study will reach the same conclusions, provided there is a suffici
ent diversity in input compound data.' (1405 chars) authors => array(5 items) 0 => array(3 items) last_name => 'Llanos Ballestas' (16 chars) first_name => 'Eugenio' (7 chars) sorting => 1 (integer) 1 => array(3 items) last_name => 'Leal' (4 chars) first_name => 'Wilmer' (6 chars) sorting => 2 (integer) 2 => array(3 items) last_name => 'Bernal' (6 chars) first_name => 'Andrés' (7 chars) sorting => 3 (integer) 3 => array(3 items) last_name => 'Jost' (4 chars) first_name => 'Jürgen' (7 chars) sorting => 4 (integer) 4 => array(3 items) last_name => 'Stadler' (7 chars) first_name => 'Peter Florian' (13 chars) sorting => 5 (integer) type => '0' (1 chars) keywords => '' (0 chars) year => 2024 (integer) affiliation => 0 (integer) link_paper => '' (0 chars) link_supplements => '' (0 chars) file_published => 0 (integer) journal => 'Proc. Roy. Soc. A' (17 chars) doi => '10.1098/rspa.2024.0165' (22 chars) preprint => '-1' (2 chars)
Are the chemical families still there? Formal structure of similarity of elements and its thermochemical domain
2024: Eugenio Llanos Ballestas; Wilmer Leal; Andrés Bernal; Jürgen Jost; Peter Florian StadlerIn: Proc. Roy. Soc. A DOI: 10.1098/rspa.2024.0165
The periodic table organizes chemical elements into families based on their similarity, now understood through Quantum Mechanics. However, these families were inferred from limited compounds in the nineteenth century. Since then, the number of compounds has exponentially grown, leading to the discovery of new types unknown to pioneers. This situation prompts the question of whether these families can still be discerned from the amassed data, or if their recognition is confined to specific thermochemical domains. To address this inquiry, we conducted a comprehensive exploration by comparing formulae (1771–2015) as a proxy for chemical similarity. Our findings reveal that stoichiometry not only captures a significant portion of the trends observed within families but also unveils other intriguing features of the formal structure of chemical similarity. These patterns approach equivalence classes independent of thermochemical context and demonstrate high resilience. Temporal analysis demonstrates that, since approximately 1980, similarity is diminishing due to an increasing production of unique formulae for nearly all elements. Nevertheless, chemical families endure over time and they stand out as the most robust similarity patterns. Our analysis offers compelling evidence that any study will reach the same conclusions, provided there is a sufficient diversity in input compound data.