Modelos, Parametrización,
and Software: Epistemic
Opacity in Computational
Chemistry
Frédéric Wieber
Université de Lorraine, Archives
Henri Poincaré—PReST
Alexandre Hocquet
Université de Lorraine, Archives
Henri Poincaré—PReST
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Computational chemistry grew in a new era of “desktop modeling,” which
coincided with a growing demand for modeling software, especially from the
pharmaceutical industry. Parameterization of models in computational chem-
istry is an arduous enterprise, and we argue that this activity leads, en esto
specific context, to tensions among scientists regarding the epistemic opacity
transparency of parameterized methods and the software implementing them.
We relate one flame war from the Computational Chemistry mailing List in
order to assess in detail the relationships between modeling methods, param-
eterization, software and the various forms of their enclosure or disclosure. Nuestro
claim is that parameterization issues are an important and often neglected
source of epistemic opacity and that this opacity is entangled in methods
and software alike. Models and software must be addressed together to under-
stand the epistemological tensions at stake.
Introducción
1.
“Many who do semiempirical calculations accept they are voodoo quantum
mechanics and one has to go to the right witchdoctor” (Evleth 1993a). Este
extract from a post in a scientific mailing list, the “Computational Chemistry
List,” illustrates the issues of epistemic opacity in computational modeling
methods in chemistry, their relations to computers and the development and
use of software in a scientific milieu.
Perspectives on Science 2020, volumen. 28, No. 5
©2020 by The Massachusetts Institute of Technology
https://doi.org/10.1162/posc_a_00352
610
Perspectives on Science
611
Computational chemistry1 is in some respects the heir of quantum chem-
istry in the age of growing computing power available to computational
ciencia, but its lineage also traces back to other scientific and instrumental
fields in chemistry (physical organic chemistry, protein chemistry, spectros-
copies). As a scientific field, it has already been discussed by numerous
autores, from the perspective of the history and philosophy of quantum
chemistry (Gavroglu and Simões 2012; Parque 2003, 2009), its more recent
transformation into computational quantum chemistry (Lenhard 2014; Pescador
2016a, 2016b), and from the perspective of the development of molecular
mechanics force fields in protein chemistry (Wieber 2012).
Computational chemistry emerged in a certain epoch and a certain context.
It began as a relative minor client of the supercomputing resources of the
1960s and 1970s but rapidly grew to become a dominant actor in the scien-
tific computing field (Bolcer and Hermann 1994). In the 1980s and 90s, cuando
the Personal Computer and the workstation made computation more widely
available in the laboratory, a new era of “desktop modeling” (Johnson and
Lenhard 2011) coincided with a growing demand for modeling software,
especially from the pharmaceutical industry (Richon 2008). The molecular
modeling software became a huge potential market for hardware manufac-
turers to sell graphics terminals and computing power to Big Pharma, eso
was eager to dive into the techno-scientific promise of “rational drug design”
(Hocquet and Wieber 2017). Además, in the 1980s, during the Reagan
años, the universities (in the US at least) launched “technology transfer” pro-
gramos. Spin-off companies were encouraged. Software produced in the univer-
sity turned out to be viewed as potential revenue for academia (Berman 2012).
Molecular modeling software became a central artifact in computational
chemistry. The issue of how to transfer it from the developer to the potential
users arose and raised tensions in the community. Computational chemistry
software was local in the beginning. The only people involved with a scientific
program developed for implementing a computational chemistry method
were the group of scientists who coded it and the few colleagues who were
scientifically collaborating with the developers. It progressively became
oriented towards a “market.” Software suites were envisioned, conceived,
and designed, aggregating various methods of various research groups, y
they had to be distributed, apoyado, and maintained. The relations with
the pharmaceutical industry, with its culture of secrecy, exacerbated these
tensions. This particular context allows us to emphasize that epistemological
1. Even if it can be argued that other scientific fields like planned synthesis (see for
example Hepler-Smith 2018a) or database searching belong to “computational chemistry,"
we limit ourselves for the purpose of this study to the restrictive meaning of computational
modeling of molecules, because it describes a community of practice, one that is sharing an
interest on specific scientific methods, but also specific software.
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612
Modelos, Parametrización, and Software
issues associated with scientific modeling in computational chemistry are not
only about methods but also about software.
The relations between computing and scientific activity have been the sub-
ject of numerous studies. Themes such as the “computerization of science”
(Agar 2006), the mutual shaping of computing and biology (Chow-White
and García-Sancho 2011), or the emergence of computerized evidence-based
medicine (Noviembre 2011) explore their interplay. Todavía, the history of software
within computational scientific communities has attracted less attention. En
his study of software used for simulating fluid dynamics, Spencer (2015) tiene
nevertheless shown how analyzing the evolution of a program within a
research group can lead to better comprehension of the evolving workability
of computational models and the associated transformations of the practices
and contexts of actions of computational scientists.
We advocate, along with Spencer, that scientific software deserves more
attention in the epistemological discussions of models in computational sci-
ence, and we argue that computational chemistry and its particular context
is a pivotal case study. As Lenhard has pointed out, one of the dimensions of
the transformation of quantum to computational quantum chemistry is that “the
field of computational quantum chemistry became organized in a market-
like fashion” (Lenhard 2014, pag. 90). The adoption of this new type of
organization was being driven by the commercialization of software, cual
has enlarged the community of its users. In his works on the various approx-
imations and idealizations used by computational quantum chemists to study
pericyclic reactions, Fischer has shown that computational chemists are in “a
position of partial epistemic opacity with respect to the computational pro-
cesses that produce numerical results” (Fisher 2016a, pag. 320), and that what
he calls computational diagnostics is a way to unpack computational models
“in order to probe the impact of approximations and idealizations on the
results” (Fisher 2016b, pag. 253). He also adds, in a footnote: “another factor
[of epistemic opacity] is ownership and access to proprietary software. […]
Perhaps the algorithms will always be partially epistemic opaque for reasons
in addition to the automation of the computational processes” (Fisher 2016b,
páginas. 253–4, n10).2 We follow Lenhard and Fischer in pointing out the interest
2.
Since its introduction by Humphreys (2004, 2009), the notion of epistemic opacity
has been debated in the epistemology of computer simulations (see for example Durán and
Formanek 2018, or Jebeile 2018). As Jebeile (2018) sums up this notion: “computer sim-
ulations are epistemically opaque (Humphreys 2004); mainly they run too fast for one to
follow the computational processes in detail and, even if it was possible to slow down the
simulation, the simulation would still be too long to be cognitively grasped by a human
mind” (pag. 214). This kind of epistemic opacity is what Fisher (2016b) refers to when he
considers, in the footnote we quoted above, “the automation of the computational processes”
as a factor of epistemic opacity. In this article, we are more interested in the second factor of
epistemic opacity he emphasizes on, that is “ownership and access to proprietary software.”
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Perspectives on Science
613
of studying software in computational chemistry, and we aim at engaging
into discussing the issue of epistemic opacity in the context of software.
Computational scientific models usually need the translation of a mathe-
matical formalism into a computationally tractable language but their numer-
ical equations also usually need parameters that are designed and defined to
depict the model target and then tested, benchmarked, calibrated, and coded
to make the model produce sound results. These tasks, bundled into the
wording “parameterization,” are, in computational chemistry, an arduous en-
terprise as we will show in a historical account further into this text, and we
argue that this activity leads to tensions among scientists regarding the lack of
epistemic transparency of parameterized methods and the software imple-
menting them. We have analyzed previously the multiple tensions among
the scientists in this community who develop, licencia, distribute, and use soft-
ware in intertwined academic and industrial contexts (Hocquet and Wieber
2017). In the present paper, we emphasize the epistemological issues at stake,
specifically regarding epistemic opacity within the pivotal issue of parameter-
ización. We first offer a brief account of the two different epistemic cultures,
namely quantum chemistry and molecular mechanics, from which computa-
tional chemistry descends. These two cultures share unifying parameterization
concerns and the scientific methods associated with them are merged in soft-
mercancía, leading to the advent of the common “technical knowledge community”
of computational chemistry (Johnson 2009). We then discuss a lively flame
war episode of the CCL to emphasize what issues are at stake for a diversity
of involved actors, in order to assess, in detail, the relationships between
modeling methods, parameterization, software and the various forms of their
enclosure or disclosure. Our claim is that parameterization issues are a source
of epistemic opacity and that this opacity is entangled in methods and soft-
ware alike. Models and software must be addressed together to understand
the epistemological tensions at stake.
2. Historical Perspective on Methods and Parameterization
Models in computational chemistry have roots in two distinct fields in the
history of chemistry. The mathematical modeling of molecules is an idea
which came up to chemists long before computers were available. Quantum
chemistry is a scientific field that emerged in the late 1920s. Teórico
physicists left quantum chemists with a very practical problem: to imagine
teorías (and models) to describe the molecules in a way that could be
calculable and useful to the chemists (Gavroglu and Simões 2012). Ellos
were the heirs of a reductionist world view of micro-physics, and the naming
of the most popular quantum chemical approximation (“ab initio”) reflects
the idea that the scientific soundness of an approximation method is based on
the universality of the constructed models which should not have to deal
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614
Modelos, Parametrización, and Software
with the tinkering of parameters (Parque 2009). En la práctica, calculating was
done with pencil and paper, desk calculators, and subsequently, durante el
1950s, with the use of excess computer time on the first supercomputers
(Bolcer and Hermann 1994), with little reward in terms of how big the
molecules that could be actually calculated were (Parque 2003).
Parallel to this, in the 1950s and 1960s, and because the computing facil-
ities were attracting the interest of many scientific fields, a different way of pro-
ducing computational models of molecular structure arose, based on far simpler
theoretical grounds, on a classical conception of molecules in chemistry, y
developed entirely on the pragmatic idea of tackling the modeling of what
is actually computable. Organic chemists and biophysicists showed interest
in a theory based on a Newtonian classical mechanics view of molecules which
appeared in the 1950s for the conformational analysis of strained organic mol-
ecules (Westheimer 1956). The very writing of skeletal molecular formulas,
and their 3D incarnation, the ball-and-stick model (Francoeur 2001), es el
core of a chemist’s epistemic culture, one that relies on the chemical concepts
of atoms and bonds, in contrast to the microphysical concepts of nuclei and
electrons. This simplified Newtonian view was not dissimilar to what chemists
knew as a molecule from the nineteenth century molecular models, and to how
spectroscopists theorized the molecular vibrations (Wilson et al. 1955).
The benefit from this mathematically simplistic theory was the prospect to
compute the structure of molecules ranging from the smallest to the most
frequently encountered in organic, biological and pharmaceutical chemistry,
by the computational standards of that time (Wieber 2012). It was ad-hoc
modelado, based on tinkering parameters to fit experimental results, yet a
tractable one, because of its straightforward computability. This tractability
was also to the detriment of the universality of the model: this ad-hoc model-
ing proved successful for a limited (but meaningful) number of molecular
familias (like cycloalkanes, peptides, sugars, …) and the necessary parameter-
ization to achieve results was limiting, because consistent parameterization
was the lengthiest and the hardest task of the modeling activity. It could only
be achieved for limited molecular families. Each scientific team developed
and parameterized their own method (a so-called “empirical force field”).
Each team relied on different (and often competitive) protocols, Residencia en
diferente (and sometimes incompatible) spectroscopic or thermodynamical
resultados, to actually define their parameters, to iteratively refine them, y para
apply their parameterization to specific molecular domains, like for example,
small organic chemistry, polymer chemistry, or protein chemistry.
Strategies of parameterization were supposed to first define “atom types.”
Tetrahedral and trigonal carbon, Por ejemplo, could not use the same linear,
angular and torsional parameters. Allegedly, alkene trigonal carbon and
ketone trigonal carbon neither. Defining a set of “atom types” was thus a
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Perspectives on Science
615
fragile balance between oversimplification (resulting in poor accuracy of the
produced results) and overspecialization (resulting in an exponentially
increasing amount of parameters to define, compute, calibrate, fit, bench-
mark…). Specialization into a molecular family was the only way to mitigate
la tarea. Parameters were also to be validated according to some reference.
Results from X-ray diffraction were used to compute crystallographic
geometries, which would compare with molecular mechanics geometry
optimizations. Another source of geometric parameters could be quantum
calculations on benchmark molecules. This lack of consistency regarding
parameters validation implies that parameters were not interchangeable
from one empirical force field to the other. Todavía, one important point is that
missing parameters were a common caveat. When parameters were missing,
the only workaround to actually make the computation run and not halt,
was sometimes the use of ad-hoc “guessed” parameters. De este modo, the mere
tagging of atom types (ketone trigonal carbon, alkene trigonal carbon…)
in a molecule was pivotal to the soundness of the results. It was also the
biggest source of an inflation of parameters: the simple addition of one
new atom in a molecule could result in an overwhelming avalanche of new
parameters to be defined and tested. Finalmente, this avalanche could be the cause
of missing parameters, and thus calculations coming to a grinding halt, cual
in turn led to the design of coding tricks to avoid them.
The demarcation between quantum chemistry and molecular mechanics
became blurred throughout the evolution of their respective fields, especially
as the promises of the computer arose. The famous Boulder discourse of 1959
by the renowned quantum chemist Charles Coulson at the Conference on
Molecular Quantum Mechanics (Parque 2003) was a turning point when Coulson
acknowledged a schism between two irreconcilable groups within quantum
chemistry. En particular, during the 1960s, the so-called “semiempirical”
methods emerged. Their name was itself a pun on the compromise they
represented. They were based on quantum calculations and thus formed a part
of quantum chemistry, but they shared the concern for feasibility with molecular
mecánica (aka “empirical” methods, as molecular mechanics were sometimes
called). In order to be actually tractable, the quantum methods should be sim-
plified, and above all, parameterized to achieve computability (the most lengthy
calculations of the model should be replaced by empirical parameters). Similarmente
to molecular mechanics, different and sometimes competitive semiempirical
methods, based on different parameterizations, appeared in the 1970s.
Semiempirical methods were akin to ab-initio methods because they shared
the same theoretical formulations, but parameterization in semiempirical
methods shared some concerns with parameterization in molecular mechanics.
Different sources of parameters were used, not only regarding geometries, pero
also thermodynamical or energetic quantities, and the same lack of consistency
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Modelos, Parametrización, and Software
regarding parameters definitions and validation protocols led to distinct and
competitive methods, just like competitive force fields had arisen in molecular
mecánica. Similarly to “atom types” in molecular mechanics, the parameter-
ization in semiempirical methods led to the “missing parameter” caveat, y
molecules that included some exotic elements (besides the ever-present carbon,
hydrogen, nitrogen and oxygen) could not be calculated, or were calculated
with “guessed” parameters.
The plurality of methods (Fisher 2016b) in theoretical and computational
chemistry, based on different but communicating epistemic cultures, gradually
turned to meet each other with the advent of the computer (and the computer
software) as a unifying tool. Quantum chemistry and molecular mechanics were
far from ignoring each other, and as a matter of fact, the birth of the compu-
tational chemistry discipline, as viewed by its founders, encompassed both
fields in a wide spectrum of modeling activities ranging from “pure” ab initio
quantum modeling to so-called “empirical” molecular mechanics. Pragmatic
quantum chemists, using semiempirical methods, were positioned somewhere
in the middle, as Richard Counts (then editorialist of the new “Journal of
Computer-Aided Molecular Design”) defines it (Counts 1987).
Another quantum-based modeling method arose during the 1990s, the so-
called density functional theory (DFT). Like semi-empirical methods, él
hybridizes quantum formalism and parametrization. It is based on solving
the Schrödinger equation using as a variable the overall electronic density of
the chemical object instead of the electronic wavefunctions, as it is the case in
strict “ab initio” methods. Electronic density requires the definition of a so-
called “exchange and correlation function” whose mathematical expression
must be parameterized (de nuevo, unlike ab initio methods) to fit to experimental
or higher-level theoretical results. The parameters are however independent of
the chemical object considered, unlike the parameters of the empirical and
semi-empirical methods. DFT has arguably been the most popular quantum-
based computational chemistry method in the twenty-first century; Ha sido
used by Lenhard (2014) as the archetypal method of what he calls “computa-
tional quantum chemistry.”
Using this plurality of modeling strategies is typical in computational
chemistry. Además, these strategies permeate one another. Por ejemplo,
results from ab initio calculations can be used to define parameters in a
molecular mechanics force field. Within this wide spectrum of modeling strat-
egies, parameters tinkering3 is an important and daily practice. We now focus
3.
“Parameters tinkering,” a widely used phrasing among computational chemists,
consists in the intervention on parameters based on pragmatic ad hoc strategies to improve
a model or even merely make the program work. Examples include the modification of a
parameter value as a rule of thumb, the replacement of an unknown parameter with a sim-
ilar one, the programming of a routine to deal with the lack of some parameters, etc…
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Perspectives on Science
617
on semiempirical and molecular mechanics modeling strategies in the next
section as parameters tinkering is a core characteristic of the way they work.
We discuss how actors debate parameters, modeling methods, and software in
a flame war which occurred in 1993 on the scientific mailing list devoted to
computational chemistry, the CCL.
3. A CCL Flame War
The CCL was created in 1991 to gather members of the fledgling community.
Topics on the CCL include questions and answers about technical details,
personal opinions about hardware or software, commercial software announce-
mentos, and also scientific matters (Wieber et al. 2018). Because it was public,
accessible and open to all, the CCL soon became an informal arena in which
all the actors involved with computational chemistry could interact: de
graduate students to senior researchers, developers and users from academia
or the industry, software vendors, people from supercomputing centers,
hardware vendors, etc… The CCL was the arena where all the people linked
to molecular modeling software one way or another could debate. From a
historical point of view, the lively episodes called “flame wars” are the most
interesting in what they reveal of controversies. Controversial debates force
the actors to leave their formal and polite stance. The uniqueness of the CCL
as a corpus to account for the tensions induced by software in the community
has been described in another publication (Wieber et al. 2018).
We focus here on one of these lively episodes, a flame war from 1993, a
explore a diversity of opinions regarding models, software and the complex is-
sue of parameterization. The first message is an announcement. Andy Holder,
then Assistant Professor of Computational/Organic Chemistry at the Univer-
sity of Missouri-Kansas City and CEO of a scientific software company named
Semichem, Inc.4, announced the publication of a paper providing results for a
new quantum chemistry semiempirical method named “SAM1.” Holder posts:
“This [the paper] is primarily a listing of results for the new method for a vast
array of systems. […] A more complete paper describing the model will be
forthcoming” (Holder 1993a). This last sentence will launch the debate.
Twenty-nine posts from eighteen subscribers will follow for ten days. graham
Hurst (then an employee of the software company Hypercube, Inc.5) posts the
second message of the thread: “this [Holder’s] post disturbs me…” (Hurst
1993a). Hurst considers that “it will be impossible to independently reproduce
these results” because the model leading to the results has not already been
4.
Semichem, Cª. was a company founded to commercialize the SAM1 method (y
other similar semiempirical methods) embedded in the AMPAC software package.
5. Hypercube, Cª. is a software company commercializing the multipurpose Hyperchem
package. Hyperchem implements many semiempirical and molecular mechanics methods.
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Modelos, Parametrización, and Software
published. “If the method has not yet been published, then the results should
not have been accepted for publication since they cannot be verified,” he adds.
Three directions of discussion are opened up in response to Hurst’s post.
Primero, the issue of how possible it is to verify the validity of the results is
discussed as the possibility to reprogram the computational method by one-
self. Is the information necessary to reprogram the method available? Marca
Thompson, then research scientist at the Pacific Northwest Laboratory and
developer of a freely licensed molecular modeling program called Argus,
explains the difficulties he came across while trying to reprogram the
MNDO method with only the publications at hand:6 typos, inconsistencies
in quantities units (due to the importation of geometrical or energetical
parameters from experimental results in units used by experimentalists like
angstroms for distances or kcal/mol for energies), and non-explicit importa-
tion of parameters from other semiempirical methods. He concludes his post
by asking “Would anyone else out there who has implemented the MNDO-
family of methods care to comment on their experiences?" (Thompson
1993a). Other posters comment about the long lasting of parameters errors
in the published literature (Rzepa 1993) or the anomaly of sulfur containing
molecules claimed in a submitted paper to have been calculated with a pro-
gram version that officially does not include any sulfur parameter for the
AM1 method. While checking how this is possible, Evleth, a researcher
at the CNRS French institution in Paris, says he learned that the sulfur pa-
rameters had been implicitly imported from MNDO, another method,
without any testing at all (Evleth 1993b). Authors of said paper had then
to acknowledge retrospectively that both methods were using “mixed
AM1-MNDO parameters” for some chemical elements. The issue of difficult
attempts at reprogramming methods illustrates the messiness of parameter-
ización. Evleth concludes in another message, the same day, that “many who
do semiempirical calculations accept they are voodoo quantum mechanics
and one has to go to the right witchdoctor” (Evleth 1993a).
Similarmente, on molecular mechanics, Hurst (1993b) then adds his experience
of coding various force fields in the Hyperchem package, and his having a hard
time obtaining official lists of parameters to refer to. Said parameters are some-
times claimed to be listed in a reference publication (or doctoral thesis) eso
happens to contain only partial and incomplete specifications.
A more general discussion then opens up regarding the issue of the param-
eters that are used in semiempirical and molecular mechanics methods. Estos
6. MNDO, AM1, and SAM1 are all semiempirical methods of the same “family,” in
the sense that they are developed in the same research group. Holder is the only poster that
belongs to this research group. Thompson attempts to implement MNDO in his “Argus”
free software package.
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parámetros, central in the different methods used, are not always made publicly
disponible. They are sometimes hidden away in the source code of the program,
which is not always made public.7 Hurst cites (perhaps apocryphally) Allinger,
the father of the MM2 molecular mechanics method: “the only complete spec-
ification [of the force field] is the program itself” (Hurst 1993b). It exemplifies
the difficulty to get a hand on parameters, the fact that parameters are not
always available publicly in publications, and moreover that the parameters
available in programs are sometimes enclosed in a non-open code. As one of
the participants of the thread writes: “[…] we should like to know your opinion
on the actual trend in commercializing computational packages without source
codes. Does this trend encourage the development of science? And also: hasta
what limit a computational package can be considered as a product of a single
research group?" (Adamo 1993).
In the second direction of the discussion, the tension between the world of
academic research and the world of scientific software corporations is under-
lined. In response to Hurst’s first message, Holder concedes that it is not always
easy to clearly distinguish scientific from entrepreneurial activities. The scien-
tists’ implication in scientific software corporations, along with the costs nec-
essary to develop software, clashes with the values the actors associate with
ciencia. As Holder puts it: “So, while Dr. Hurst’s point is well-taken and fully
subscribed to by me both in my capacity as a university researcher and president
of Semichem, there is no intention to “hide” anything. I understand the sen-
sitivity of this issue and I am committed to the pursuit of science in an open
atmosphere. […] The development of SAM1 is my primary research activity
[…], but Semichem is also spending money to develop this method and will
be giving it to the scientific community freely. We withhold only our code.
[…] It should be noted, sin embargo, that some interests are not scientific, but competitive”
(emphases added) (Holder 1993b).
In the third direction of the discussion, the problem of publication ethics is
discussed. The importance of the peer review process in scientific publishing is
underlined and some contributors ask if reviewers do a good job when accept-
ing for publication results which have been obtained by a computational
method not fully (and openly) descrito. The question leads more generally
to contrast proprietary methods and open scientific literature. As Mark
Thompson writes down: “I feel very strongly that when a new method is
developed and implemented that it must pass the peer review process to gain
7. Computational programs may be distributed in the form of ready to use “execut-
ables” or «binaries» that lack the possibility to scrutinize what is actually written in the
código. The “source code” offers this possibility, but then must be compiled into an execut-
able by the user. An “open” source code can mean that it is readable (but not necessarily
intelligible), but a precise definition of what “open” source code means (modifiable? reus-
capaz?) is embedded into the software licensing and may vary.
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Modelos, Parametrización, and Software
legitimacy in the scientific community, regardless of whether most other
scientists care to re-implement that method or not. Proprietary methods are
fine, as long as it is openly known that they are proprietary. Results of propri-
etary methods do not belong in the open scientific literature” (Thompson
1993b). Por supuesto, these three directions of discussion are interrelated.
The sixth message of the thread, written by Douglas Smith (then Assistant
Professor of Chemistry at the University of Toledo) is particularly revealing. En
this long post, Smith responds point-by-point, using interleaved posting, a
Thompson’s whole message. The tensions produced by software within the
community are interestingly expressed by contrasting how scientists believe
they should act with what they actually do. Thompson has written that “good
science is that of reproducibility and independent verification” (Thompson
1993b). Smith points out that it is “universally true and accepted” but “rarely
followed” (Herrero 1993). Smith uses as an example the issue of parameters used
in molecular mechanics, which are regularly modified and adjusted for a
particular study without being published in the paper relating to that partic-
ular study. More generally, the very nature of such a method (and of semiem-
pirical methods) leads to a multiplication of the parameters used without a clear
display of which parameters are used when producing such or such results. En
actuality, computational chemists act in a way that differs from what they say
they should do. Thompson has also written: “If the results of a new method are
published without sufficiently describing the method to fulfill the above
criteria [reproducibility and independent verification], then I personally could
not take the results seriously” (Thompson 1993b). Here again, Smith considers
that if this position points to “a real problem,” it is “utopian and most likely not
practical,” because of “the proprietary nature of commercial software” (Herrero
1993), and because some people use this type of software as a “black box.” He
then adds: “Besides, who ever said we had to reveal all our secrets and make
them readily available and accessible? When software copyrights and patents
really provide adequate protection, maybe I will agree with that attitude”
(Herrero 1993). Finalmente, if “results of proprietary methods do not belong in
the open scientific literature,” as Thompson has written, “where do they
belong?” Smith replies. According to him, the situation is complicated: “what
about the difference between someone in industry who paid for the source code
for MacroModel8 as compared to the academic, such as myself, who only gets
binaries? Are my results to be less acceptable because I don’t have the absolute
method available? Or are the industrial results less acceptable because they can
be the results of tweaking the code?" (Herrero 1993).
8. MacroModel is a software package implementing several molecular mechanics
methods, including Allinger’s MM2 force field, and adding in-house parameters.
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Perspectives on Science
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It is worth noting that posters switch easily from semiempirical methods to
molecular mechanics and back when they talk about their concerns regarding
publicaciones, methods, parameterization and software. These two domains
share the same concerns and are bound in similar and sometimes even the same
software, even if the theoretical formulations they use are very different.
In Smith’s post, the discrepancy between the values the actors associate with
science and their actual practices associated with computational methods and
software is clearly highlighted. The issue of the norms of sound science is, en
práctica, difficult for them to address.
Además, computational chemists ask the question of how the difficult
and tedious work of programming can be recognized. Can this recognition
be obtained by publishing programs or by adequately protecting them
(“When software copyrights and patents really provide adequate protection
[…]” Smith writes)? The complexity of the issue of software copyrights
and patents is then stressed in many subsequent posts of the thread.
Some posters try to separate the issue of copyright and patenting, as a soft-
ware issue, from the issue of transparency of methods and publication, as a sci-
ence issue. Hurst (1993b) makes the strong claim that “it is important to
distinguish ‘science’ from ‘code’” (his emphasis). The former should “include
everything a researcher needs to know to reproduce numbers” and the latter
“need not be fully or publicly disclosed.” But this dichotomous view is criti-
cized. Balducci (1993), a research associate and systems manager at the univer-
sity of Texas in Austin, opposes that a lot of work in the code, such as defining
molecular geometry (and especially ring structures), belongs to science: “in
several cases it would be impossible to even describe (much less to reproduce)
the ‘science’ of a method without a clear definition of the structure of the ‘coded’
solution.”9 Mercier, then at Cornell school of medicine, regrets that method
coding is often “hardwiring tables of parameters into the code” (Mercier
1993) and makes them difficult to comprehend. He refers to modular program-
ming, such as in Mathematica software, as a mean to separate parameters from
an enclosed code in order to give the community the possibility to enhance the
parameterization of a hard-coded method.
Finalmente, Fernandes, then an undergraduate student at the University of
Waterloo, insists that even an open (in the sense of readable) source code is
not sufficient to understand the method and its parameterization. Lack of code
annotation and obscure versioning do not help: “what guarantee do we have
that G92 (or any of Biosym’s or AutoDesk’s products) actually do what they
are supposed to? Even having the source code just doesn’t help… who wants to
9. Defining computationally a chemical structure with atoms and bonds is actually
one of the other scientific domains belonging to computational chemistry in a broader sense
we mentioned in the first footnote (Hepler-Smith 2018b).
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Modelos, Parametrización, and Software
root through 350,000 lines of someone else’s code for any reason?" (Fernandes
1993). The thread finally dies of attrition after a general sense of uncertainty
about what the future holds regarding the relationships between intellectual
property notions and the tensions they expressed beforehand.
Parameters as Opacity in Methods and Software
4.
The initial problem of the flame war is an epistemological problem entangled
with a problem of publication ethics: as the details of the model used to produce
the published results have not been published, the results cannot be indepen-
dently reproduced and verified and are then not considered publishable.
Beyond this problem, the tensions revealed by the conversation thread are
the symptomatic expression of opacity regarding parameters, methods and
software.
Andy Holder’s first post speaks about a future paper that will “describe the
[SAM1] model” and Hurst’s infuriated answer states that “the SAM1 method
still does not have an official reference” (his emphasis). Two subsequent posters
speak of disclosure of “semiempirical parameterization” (Evleth 1993b) y
“disclosure of parameters” (St-Amant 1993). As a matter of fact, if the titles
in the subject headers of the first posts are about “SAM1 reference,” there is
a shift in titles towards “full disclosure of methods” after two days of posting,
and actors even insist on the issue of “disclosure of programs” in subsequent
messages. The issue is about what exactly has to be disclosed—models,
methods, parámetros, programas, software—and how to ensure transparency.
The epistemological nature of the models in computational chemistry
implies that epistemic transparency is very difficult to reach in practice. El
very nature of the models, for example in semiempirical methods like SAM1 or
in molecular mechanics approaches like MM2, requires the time-consuming
work of parameterization. Parameterization poses a problem of reproducibility
y transparencia. Tinkered parameters that are designed to make the model
actually work are subject to their own epistemic problems.
Numerous research groups build numerous molecular mechanics force fields,
and the essential work of parameterization in this construction is sometimes
developed in a competitive atmosphere. Force field success is measured in terms
of parameters efficiency (to produce sound results out of well-defined parameters
for the calculation of properties of a molecule), consistencia (to produce reproduc-
ible results from consistently defined parameters across a variety of molecules)
but also workability (to avoid calculation failures because of the absence of
parameters that lead to a program halt). In force field construction, some param-
eters are missing, some generic parameters are designed to replace missing
parameters to avoid program halt, and some parameters are lacking a sufficient
description in order to know if a parameter is proper or simply fills a hole. El
situation of force field multiplicity is even made more complex by the fact
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Perspectives on Science
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that competitive software packages may implement a certain force field differ-
ently, especially in the treatment of missing parameters. Además, otro
research groups may adapt a force field to their particular calculation needs by
adding layers of “in-house” parameters.
Semiempirical methods show similar issues due to a similar complex situa-
ción. Holder (1993b) says that it is impossible to reproduce the MNDO method
“from scratch,” because publication is incomplete. Todavía, Thompson tried to
actually reprogram MNDO “from scratch,” without the source code, con solo
the mere paper in hand. Errores, units discrepancies, and mixed parameters
(importation of parameters from one method to another to make the program
run) were the difficulties encountered by Thompson and others.
Given this diversity of parameterization and parameters descriptions,
methods and their parameters are not consistently disclosed, and this is a source
of epistemic opacity. Actors ask whether they could be, for example in a
publication’s supplementary material. In the end, the question actors are
asking is whether a fully parameterized method could be properly described
and then sufficiently disclosed in a publication.
En la práctica, this ideal of transparency is hard to reach because parameters are
intertwined with the coding of the method. As several actors mention, el
parameters are often “hardwired” into the code. In one poster’s (quoted) palabras,
“the only full description of the method is the program itself.” The entangle-
ment of parameters and code is adding a new layer of opacity in the issue of
reproducibility.
A serious issue regarding parameters embedded in code is the fact that the
code may not be open, in the sense of not readable. If the code of the program is
not open, then executable binaries are what software users get. Parameters,
which are in that case hidden in an enclosed program, thus cannot be checked.
So why do developers “withhold the code” in Holder’s words (Holder
1993b)? Because software is more than just code, it is also a commodity, y
the scientific activity of computational chemistry shares common concerns with
the industrial sector of software sales. In a world where software is also a busi-
ness, issues of intellectual property, or software distribution in general, entwine
with the traditional concerns of the scientific world. The difficulty to finance
continuous development clashes with scientific ethos concerns. This situation
gives rise, Por ejemplo, to the problem of the lack of scientific recognition for
software development. Many actors then judge that it is important to gain
recognition for software development, and in this regard, to protect and enclose
the code is vital, even if methods should be reproducible. Modular program-
ming is cited as a means to give the community the possibility of disclosing
parameters while leaving the code enclosed.
En la misma vena, Hurst (1993b) claims that “science and code must be
separated,” but this attempt to separate methods and software is viewed by
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Modelos, Parametrización, and Software
some posters as vain. He is reminded that the entanglement of both is inex-
tricable. Por ejemplo, Balducci (1993) advocates that beyond mathematical
equations formalizing a model, the computational definition of molecular
structure itself to effectively compute the calculations (molecular geometry,
and especially ring structure) is intermediate between science and code, y
belongs to both.
Is code openness, as a proposal to solve this opacity issue, una solución? El
idea that any scientific code should be open is not straightforward. Some posters
regard the commercialization of computational chemistry packages as having
many advantages. Primero, instead of spending time and energy in building code,
the use of already existing code can be viewed as the possibility to dedicate
oneself to actual chemistry (publishable) cálculos, instead of (unrewarded)
programming. Segundo, financing the computational effort of developing soft-
ware through software sales is seen in some quarters as the only way to have the
possibility that robust, versatile, powerful, and efficient software packages even
existir. And third, from an industrial user point of view, buying commercial soft-
ware grants liability from a corporate software vendor in case of something
going wrong. Some posters then view software packages as a metaphoric
scientific instrument: commercialization is seen as building trust in the
scientific community.
There is however no consensus on this matter. Posters cite many examples of
the need of an open source code for sound scientific practices, and one of them is
precisely the need for parameters verifiability. Tensions proceed from the
confrontation of two viewpoints: one concerned with epistemic transparency
as scientific ethos, and the other concerned with stability thanks to software
as a commodity. Academic publishing, which constitutes the traditional form
of academic reward, is central in the actors’ ideal concept of the openness of
ciencia. Todavía, there is a tension between two stances. One is that modeling soft-
mercancía, as a scientific tool, should be considered a public tool, and as such, uno
that belongs to the scientific community, including its potentiality to be
disclosed, enhanced, and maintained. The other is that, software, as a tool
developed by a small team, in a commercial context, should be licensed, strict
licensing policies helping to keep software stable, which guarantees the
production of sound scientific results.
Todavía, even if the code is made public, the opacity also lies in the complexity of
the programs: checking programs out (beyond merely testing results) is very
difficult according to Fernandes (1993). It is not easy to assess whether the
program behaves as intended by the code developers: lack of code annotations
and versioning issues are again sources of opacity, even in a readable code.
Finalmente, the opacity also lies in the software package licensing policy that
impedes checking of parameters. Herrero (1993) evokes the paradoxical issue
of industrial or academic users of the same software package. The former get
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Perspectives on Science
625
access to the source code and thus have the possibility to tinker force field
parameters “in house” and then publish computational results that can be
criticized for unsoundness (are the parameters used thoroughly tested?) y
lack of traceability (are the modified parameters published?). The latter only
get access to binaries and their calculations may be criticized for using a method
whose parameters they don’t know exactly. A este respecto, packaging and
licensing policies add yet another layer of epistemic opacity.
The flame wars as we have depicted one of them are thus the symptoms of
tensions about a lack of epistemic transparency regarding methods and soft-
mercancía. Primero, the opacity of methods’ parameters lie in their diversity, y esto
implies issues regarding publications. Methods are opaque if parameters are
unavailable. Segundo, parameters are intertwined with code, which adds a layer
of opacity, be it open or not. Tercero, programs are themselves opaque because of
their complexity and lack of traceability. The final layer of opacity lies in the
policies of software as packages. We have shown that this epistemic situation
and the tensions implied by parameterization at any level, are constitutive of
computational chemistry. It also implies that the concepts of verification (does
the model perform in a consistent way mathematically and computationally?)
and validation (does the model do a good job at depicting its target?) are actually
confluent, as Winsberg hints (Winsberg 2018). The entanglement between
parameters and software can lead to this confluence.
Conclusión
5.
The issue of parameterization as a source of epistemic opacity in computational
chemistry is a telling example that models and software must be addressed
together in computational science. Interrelations between both imply that
transparency and validity of computational methods are complex, and that they
are a source of tensions for scientists, in the economic, political and techno-
logical context we mentioned.
It is interesting and necessary to discuss the structure, properties and epis-
temological status of models, as it is common in the philosophy of science.
We argue that it is furthermore necessary to understand models in relation
with software which embody them, which give them their productivity. En
doblar, understanding software (in computational sciences) needs to take into
account the models they express, which is “the representations of the world”
scientists translate in a way the computer can “understand.” These represen-
tations depend on the communities of scientists involved and the histories
of the ways they represent the portion of the world they are interested in
(Mahoney 2008). In Mahoney’s words, estos modelos, and their translations
into software, are “operative representations”, which are central to our study,
and parameterization is pivotal to this entanglement.
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626
Modelos, Parametrización, and Software
The complexity of the parameterization is central in the modeling activity.
This has to be understood in the context of the calculability problems quantum
approaches and molecular mechanics approaches have faced. En el caso de
molecular mechanics methods, the choice of a particular representation of
asunto, which is consistent with a classical conception of molecules, also leads
to a necessary complex work of parameterization. The choices of sets of param-
eters, made locally by such or such research group for such or such group of
molecules, lead to models whose epistemic transparency is questioned by the
actors themselves. What is interesting for our argument is that this lack of trans-
parency of models has consequences on the status of software: the issue of the
openness of the source code is for example made more salient knowing the
importance of parameterization in modeling. What is also interesting is that
there are similar concerns with semiempirical methods, even though the latter
are quantum methods. A este respecto, molecular mechanics and semiempirical
quantum methods share a fundamental epistemic issue, especially since they are
bound in similar ways to software, and sometimes even within the same
package, even though they are attached to different (and even incompatible)
teorías. The phrasing “Voodoo quantum mechanics,” as used by one of the
actors of the flame war we have narrated, ironically highlights the issues of opacity
in methods, parameterization, and software that literally possess computational
chemists, who have then to rely on “the right [but often evasive] witchdoctor.”
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