ReactionMatch.com (theory)

CFAR
Generic diagram of a dating site that uses algorithms to match people, which in technical terms would be defined as a continuous flow algorithmic reactor (CFAR) a computerized human chemical reaction facilitator.
In human chemistry, the theory behind the concept of ReactionMatch.com, the chemical thermodynamics based pair matching site, is to match people (human molecules) to obtain successful or energetically feasible reactions (relationships) using the very same methods that are used to match atoms and small molecules in chemical reactions. [1] This theory was originally developed in 1809 by German polymath Johann von Goethe, which he called "elective affinities", otherwise known as the world's first chemical treatise on the origin of love. [2]

In modern terms, the first to develop Goethe's premise of elective affinity reactions occurring between humans and thus determining their mate and friendship preferences, was American chemical engineer Libb Thims, who in 1995 began to mediate on on the issue of chemical thermodynamics based mate selection, which in 2007 led to the publication of the world's first textbook on human chemistry (Human Chemistry), the study of the chemical behaviors of human molecules. [3]

Human chemical reaction engineering (HCRE) is the technical name of this emerging discipline, in which people (human molecules), i.e. reactants (A, B, C, etc.), are funneled through online matching algorithmic reactors to achieve desired products, i.e. stable dihumanide molecules (A=B, C≡D, etc.). [14] The first published, albeit in a humorous-style, outline of this logic was the Gibbs free energy thermodynamics of love theory describe by American computational chemist David Hwang in his 2001 article "The Thermodynamics of Love". [4]

Continuous flow algorithmic reactors
A continuous flow algorithmic reactor (CFAR) is the technical name of an online matching site that uses science-based algorithms to match people, by design, in desired reactions, as shown adjacent. The basic CFAR design, of chemical thermodynamics-based online matching, is modeled on the standard continuous-flow reactor design variations. [14]

In this model, the input flow consists of single human particles or human molecules (A, B, C, ..) entering the matching site as "reactants". The output flow or "products" consists of two streams: (1) outflow of singles (C, D, ...) that didn't match and (2) outflow of paired couples (A=B, E≡F, ...) that did match.
The time, i.e. number of days or months, in which a person is in a CFAR, as a paying subscriber of an online matching site, in the form of an unattached single, is called the “residence time”.

Matching theory overview
The basic idea behind scientific pair matching, from a chemical thermodynamic perspective, which is the science that determines the feasibilities of chemical reactions, is to pair people, considered as “human molecules”, such that successful reactions (relationships) lead to a stabilization in the bond A≡B, over the extent of the reaction, a factor quantified by a decrease in the free energy in the functionality and system of the relationship. This essential nature of pair matching between human molecules is captured cogently by American-Canadian biochemist Julie Forman-Kay in her 1999 article “The Dynamics in the Thermodynamics of Binding”, where she summarizes: [15]

“Whether two molecules will bind is determined by the free energy change of the interaction, composed of both enthalpic and entropic terms.”

This logic has successfully been applied to the chemical matching determinations for gas phase reactions (by Fritz Haber in 1905), for various chemical reactions (by Gilbert Lewis and Merle Randall in 1923), for various biochemical reactions (by Keith Burton in 1957) and for various larger chemical species, of size 6-elements in composition or less, as sizing criterion that can be discerned via molecular evolution tables, and for larger molecular entities such as protein and drugs, in protein thermodynamics and drug-receptor thermodynamics. For larger chemical species, such as human molecules, a 26-element molecule, the same logic applies, however the matching determination (reaction feasibility) calculations are more difficult, but invariably reduce to enthalpy-entropy determinants (among other factors). In step-by-step terms, t
he matching of people (human molecules), in algorithmic terms, equates to the following:

#Key Points to Matching Theory

a.Matching people is the process of setting up “potential” chemical (combination) reactions between individuals.
b.From a physical chemistry point of view, the overall “criterion” to predict the successfulness, spontaneity, or feasibility of any chemical reaction or of the matching any two chemical entities, is based on the criterion of energetics.
c.Technically, this means that at the start of a successful pairing, there exists a great deal of stored chemical potential energy, i.e. the relationship has “great potential”, and thus will continue reacting as it burns its working potential over the “extent” of the reaction.
d.The release of “working energy” (G), out of the bond of the relationship and into linked systems of friends, family, associations, and society, in a virtuous manner, over the course of a successful or spontaneous reaction, is quantified by decrease in the Gibbs free energy of the pairing: this is called thermalization or equilibration.
e.Successful thermalization processes, react past a desireable point, e.g. the golden anniversary (50+ years of happy marriage).
f.The point at which a relationship “stops working”, however, signifies a point in time in which the reaction is at equilibrium wherein the chemical potential is spent (a fact quantified in 1876 by American mathematical engineer Willard Gibbs).
g.The equilibrium point is typically when couples begin to break up, through what is called a dissociation reaction.
h.In extrapolation, the “overall criterion” to the successfully matching of individuals in relationships is an energetic one, meaning that individuals have to be matched energetically (criterion: ΔG < 0 for the paring); the mechanism has to match the laws of thermodynamics.
i.This can be quantified in terms of a per unit (A, B, AB, etc.) 12-factor free energy function, G = f(t), a dynamic function of time t, comprised of physical and neurological mate desireability attributes:

G = f(averageness, age, symmetry, sexuality, i.e. testosterone-to-estrogen ratios, immune system, i.e. MHC-determinants, fitness, complexion, personality, social graces, character, dependability, occupation, possessions, wealth, information, intelligence, education, knowledge, status, prestige, inner nature, values, and ambition, among others).

j.Each G = f(t) function, representing a person's (A) or couple's (AB) quantifiers, must then be translated in terms of the thermodynamic energy quantifiers of enthalpy H (the heat of reaction) and entropy S (the irreversible energy loss of reaction), i.e. G = f(H,S) according to the definition equation: G = H - TS, where T is the absolute temperature of the reaction system.
k.These G = f(t) energetic quantifications of individual persons can be used to determine pair feasibility for combination reactions (scientific matchings).

Matching example
To explain further, by example, suppose that person A was to be hypothetically paired up with three potential mates (B, C, D):

Couple pairing #1:
A + BAB

Couple pairing #2:
A + CAC

Couple pairing #3:
A + DAD

The question then becomes, how would one use the logic of chemical thermodynamics and its ability to predict reactions, according to the spontaneity criterion (ΔG < 0), to help them determine who they should date or marry? In other words, which of the above reactions would be more spontaneous and thus more energetically favored? The answer is that each potential reaction, will have a different value of free energy change, ΔGAB, ΔGAC, and ΔGAD; subsequently, for the three potential matches, the one that has the largest negative value will be the best match.

Graphical analysis
In order to formulate the above human chemical thermodynamics matching scheme into a mate-selection algorithm, a first-step graphical analysis is needed. A first approximation match would be to determine feasible 20-year human chemical reactions, thus besting the current national average for divorce rates, in the US, which currently stands at 43% at 15-years. [5] In other words, according to the US National Center for Health Statistics, forty-three percent of first marriages break within fifteen-years. [6] Scientifically, from the get-go, marriages, i.e. unions (reactions), that end quickly in divorce are “less thermodynamically stable” than as compared to more energetically-favored marriages (reactions) that continue to ignite for 50-years or more.

Using chemical thermodynamics, it is changes in the value of free energy that are used to determine if a reaction is thermodynamically favorable. [7] The change in Gibbs free energy ΔG for a chemical reaction (human chemical reaction or otherwise) is defined by the following expression:

 \Delta G = G_f - G_i \,

in which GF is the instantaneous value of the free energy at the hypothetical final stage of the reaction and GI is the initial value at the collision point of the reaction.
Collision theory
Potential energy surface plot of the free energy versus extent of reaction, showing various hypothetical final states, in various stabilities (A≡B, A≡D, A≡C, begin stable products; A≡X being an unstable product) above and below thermodynamic equilibrium.

Collision theory
The collision point, according to collision theory, is the point in time at which the two particles “collide” or first begin to have an interaction. The typical example being love at first sight, in which a visual field particle stimulus triggers the formation of a marriage bond (human chemical bond). About 20% of people will fall in love at first sight and marry that person. [8]

Subsequently, in the above set of hypothetical reactions, 1, 2, or 3, between the collision point (day-one) and the extent of reaction goal (20-year point), we will have three possible “initial” free energy GI values (GA+B, GA+C, GA+D) and three possible “final” free energy GF values (GAB, GAC, and GAD), which together yield three different calculated values of free energy change ΔG, namely: ΔGAB, ΔGAC, and ΔGAD, which are shown on the adjacent “free energy” vs. “extent” of reaction plot.

Activation energy barrier
In this diagram, the activation energy barrier represents the amount of "activation energy" EA that the potential couple must possess in terms of congruent arrangements of kinetic energy (entropy of activation; enthalpy of activation) at the point of collision, in order to surmount the activation energy barrier. This is an important factor that must be accounted for in any matching algorithm. [9] At the height of the activation energy barrier, the couple would be at a high energy state called the “activated complex” A - - - B, in which both their old bonds (family, friends, and associated networks) and newly forming bonds (attachment structure of the newly forming family, friends, and extended network) are stretched to their maximum. The entity that emerges on the other side of the activation energy barrier is the attached couple or “dihumanide molecule” connected through what is called a “human chemical bond” A≡B.

Spontaneity criterion

In this diagram we have four possible outcomes depicted, namely relationships A≡X, A≡B, A≡D, and A≡C positioned at various levels of Gibbs free energy. The best relationship outcome, namely the one that is the most stable, is determined by the spontaneity criterion.

Hence, in the hypothetical results depicted in the diagram, if a matched pair ended up at life position A≡X, above the relationship equilibrium level, the positive value of ΔG would be the amount of energy (work energy) that the pair would have to “put into” it to keep their relationship going, i.e. they would have to spend a lot of time and energy "working on their relationship" (a draining process), e.g. energy loss in friction, tension, arguments, disagreements, counseling, extended family strains, etc.

Conversely, positions below the relationship equilibrium level, namely A≡B, A≡D, and A≡C, are energetically favored and thus would each be, in theory, spontaneous reactions (relationships). The amount of “free energy”, in this case (ΔG < 0), in the words of American chemist Raymond Chang, is “the energy available to do work”. [11] In other words, it is the amount of the working energy of the relationship that is released out of the functionality of the human chemical bond of the pair, as they thermalize off each other, being used to do virtuous activity in life, in accordance with the progress of evolution.

The question then becomes: which of these three spontaneous reactions is the most favored? The answer to this is that the reaction that yields the most negative free energy change, will be the most spontaneous and thus the most energetically favored pairing. In the reaction situation: ΔGX < ΔGY < ΔGZ < 0, for instance, the reaction defined by the large negative value of "ΔGX" would indicate this to be the most favored reaction. A chemical bonding of this type, will be the most stable in the long term owing to the fulfillment of "quantum neurochemical octet rule bonding satieties" (drive/dream fulfillment) of the pair. [1] In the above example reaction, pairing A≡C is most-favored over the others.

Difficulties on theory
Beyond this basic outline, the greater difficulties involved in creating a computerized free energy compatibility based matching algorithm, is the mathematical translation of input values:

Physical data:
Genetic disease markers (e.g. Down syndrome), MCH-characteristics (immune-system compatibilities), blood types (blood sample processing), pupil-to-iris ratios, body-fat-percentages, measurements (height, weight, hair color, eye color, skin tone, etc.), beauty (physical attractiveness ratings, both unique and consensus), fitness, age, complexion, ethnicity, sexuality, waist-to-hip ratio, symmetry measurements.

Neurological data:
Personality patterns (explorer [dopamine], builder [serotonin], director [testosterone], negotiator [estrogen], etc.), occupation, Wealth, (possessions), intelligence, education, knowledge, status, prestige, drive, inner nature, values, ambition

Environment data:
Birth order, extended family networks, external forces, geographical momentum tendencies.

Among other data input sets, such as the 60-mate trait desireability factors, these total inputs need to be formulated into enthalpy ΔH and entropy ΔS terms in the standard Gibbs free energy function: [12]

 \Delta G = \Delta H - T \Delta S \,

Beyond this, system particle flow (new human molecules into or out of a system), as quantified by changes in chemical potential μ of the system, and external forces, e.g. job loss, divergences in goal paths, the strain of a war, extended family factors, etc., need to be accounted for. In addition, emotional imaging processing would need further development to determine video (and in person) positive-to-negative Gottman stability ratios. [1]

Corroboration
The theory of matching human molecules (people) according to "free energy compatibilities", developed by Thims, not only finds excellent corroboration with the chemical-thermodynamic love-theories of those as Johann von Goethe (1809), William Fairburn (1914), George Carey (1919), Jeremy Adler (1987), David Hwang (2001), Chanel Wood (2007), but also with the recent thermodynamic evolution theories of Russian physical chemistry Georgi Gladyshev (1978), who in his 1997 book Thermodynamic Theory of the Evolution of Living Beings set forth the view, among others, that:

(a) The definition of ΔG is the energetic measure of system structure formation in a given system evolution relaxation window.
(b) Only the initial and final states of a process under study are of interest.
(c) The variations of the Gibbs function of the system, at any stage of evolution, particularly ontogenesis and philogenesis, can be calculated by thermodynamic methods.
(d) A function Gsystem-formation = f(t), can be obtained using thermodynamical data, thus representing the “state” of the system at a fixed moment in time.
(e) Variations of calculations of ΔG characterize stability changes in the system.

In review of the RM chemical thermodynamic based theory of mate selection, using a free energy compatibility methodology, as outlined above, Gladyshev agrees with the propositions, stating that it "is a good practical application of theory." [13]

See also
ReactionMatch.com (projections)
ReactionMatch.com (history)
ReactionMatch.com (operations)

References
1. (a) Thims, Libb. (2007). Human Chemistry (Volume One), (preview), (Google books). Morrisville, NC: LuLu.
(b) Thims, Libb. (2007). Human Chemistry (Volume Two), (ch. 10: “Goethe’s Affinities”, pgs. 371-422, ch. 12: Affinity and Free Energy” [section: “Online Matching”, pgs. 455-64], pgs. 423-68). (preview), (Google books). Morrisville, NC: LuLu.
2. (a) Goethe, Johann von. (1809). Elective Affinities. New York: Penguin Classics.
(b) See: Goethe's human chemistry
(c) Tantillor, Astrida O. (2001). Goethe's Elective Affinities and the Critics. New York: Camden House.
(d) Adler, Jeremy. (1987). “Eine fast magische Anziehungskraft”. Goethe’s “Wahlverwandtschafte” und die Chemie seiner Zeit (“An almost Magical Attraction”). Goethe’s Elective Affinity and the Chemistry of its Time), Munich.
(e) Adler, Jeremy. (1990). "Goethe's use of chemical theory in his Elective Affinities" (ch. 18, pgs. 263-79) in Romanticism and the Sciences - edited by Andrew Cunningham and Nicholas Jardine, New York: Cambridge University Press.
(f) Thims, Libb. (2007). Human Chemistry (Volume Two), (ch. 10: "Goethe's Affinities", pgs. 371-422), (preview), (Google books). Morrisville, NC: LuLu.

3. World's First-ever Textbook on the Chemistry of Love - PR.com (Institute of Human Thermodynamics)
4. Hwang, David. (2001). "The Thermodynamics of Love", Journal of Hybrid Vigor, Issue 1, Emory University.
5. Wilkins, S. (2005). "For Richer or Poorer". MotherJones.com
6. NSHS (2001). "43 Percent of First Marriages Break Up Within 15 Years". Maryland: Hyattsville, US CDC.
7. Gibbs free energy (definition) – The Essential Dictionary of Science (2004), Barnes & Noble.
8. Naumann, Earl. (2001). Love at First Sight – the Stories and Science Behind Instant Attraction, (ch. 2: “The Chemistry of Love”, pgs. 23-42 [35]). Naperville, IL: Casablanca Press.
9. Thims, Libb. (2007). Human Chemistry (Volume Two), (section: “Online Matching”, pg. 458). (preview), (Google books). Morrisville, NC: LuLu.
10. Chang, Raymond. (1998). Chemistry, 6th ed. (ch. 18: “Entropy, Free Energy, and Equilibrium”, pgs. 725-55). New York: McGraw-Hill.
11. ibid (pg. 738).
12. (a) Thims, Libb. (2007). Human Chemistry (Volume One), (section: “Mate desireability factors”, 60-trait table as a function of G(H,S), pgs. 174-178). (preview), (Google books). Morrisville, NC: LuLu.
(b) Todosijević, Bojan, Ljubinković, Snežana, and Arančić, Aleksandra. (2003). “Mate selection criteria: A trait desirability assessment study of sex differences in Serbia”. Evolutionary Psychology, 1: 116-26.
13. Email comments from Gladyshev to Thims on 06/24/08.
14. (a) Fogler, H. Scott. (1992). Elements of Chemical Reaction Engineering, 2nd ed. Englewood Cliffs, N.J.: Prentice Hall P T R.
(b) American chemical engineer H. Scott Fogler, author of the world’s leading textbook on chemical reaction engineering was a teacher of Thims; many parts of the design of algorithmic matching of humans in reactions are modeled on his textbook.
15. Forman-Kay, Julie D. (1999). “The ‘Dynamics’ in the Thermodynamics of Binding.” Nature Structure Biology, 6: 1086-87.

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