Molecular Dynamics for Everyone - Molecular Workbench

Molecular Dynamics for Everyone: A Technical Introduction to the Molecular Workbench Software
Charles Xie
The Advanced Educational Modeling Laboratory
The Concord Consortium
The world moves because molecules move. Studying the motion of molecules is important to the
understanding of many critically important concepts in physics, chemistry and biology. A fundamental goal of scientific research is to learn how things work, which at the microscopic level primarily
means how atoms and molecules move to perform certain functions such as chemical reactions,
molecular recognition and protein synthesis.
The ability to reason about complex phenomena in terms of fundamental facts and theories describing the structures, interactions and dynamics of the atoms and molecules of which all things are
made is called the Molecular Literacy [1]. This bottom-up perspective, in addition to its philosophical
significance, is increasingly important, as nanotechnology and biotechnology are emerging as the
twin engines of the next industrial revolution. Molecular Literacy empowers people to better
understand known phenomena and to anticipate the properties of novel arrangements of matter
under new conditions. Achieving Molecular Literacy at the grassroot level is therefore crucial for
future scientists and engineers as it promotes the imaginative thinking needed to create and develop
new nano-structured and bio-mimetic materials and devices for diverse applications to meet critical
healthcare, energy, and environmental challenges.
Since the invention of molecular graphics, a subject that focuses on visualizing molecules using 3D
computer graphics, chemists have embraced tools capable of rapidly displaying molecular structures
and viewing them from different perspectives. Because of the appealing effect of 3D graphics,
commercial computer-aided design packages often feature molecular visualization tools to promote
the products. Since the advent of the Internet Era, several free tools have been developed for showing
molecules on the Web. These tools are widely used by educators to teach molecules [2].
Most molecular viewers, however, are mainly designed to show static structures. The user can rotate
and translate the entire rigid structure or change the view angle dynamically to create a motion effect,
but the atoms do not move relatively to each other. Viewing static structures helps students learn the
structures, but it is often far more important to learn the functions—after all, we study molecules
because we hope to make use of what they can do. Although one can argue that in many cases there
exists a strong structure-function relationship that can help people derive functions from structures, it
is impractical to expect inexperienced students to be able to reason using the relationship that may be
evident only to experts. Too often have we seen an excited chemist trying in vain to explain to nonexperts what he or she sees in a 3D model of a molecule that is beyond a cool picture of some 3D
structure. It would be most helpful if a molecular mechanism can “speak for itself” in a dynamic
visualization to fill the gap. Some molecular viewers can sequentially display a series of static frames,
which can be different states of the same molecule observed experimentally or computed theoretically, to create an animation of a conformational change. This is a step forward to help students learn
molecular mechanisms, but it only allows them to passively watch what was set to happen. For a tool
to do a better educational service, students should be permitted to “mess up” with the models, try
many what-if’s, and see what happens.
Funded by the National Science Foundation, a team of the Concord Consortium has been developing
a free, open-source program called the Molecular Workbench (MW) (, which
brings a salient, dynamic molecular world to the computer screen and allows students to interact and
experiment with it1 based on real-time molecular dynamics (MD) calculations and visualizations [3].
MD modeling provides a powerful means to foster the Molecular Literacy because it complements
and enhances traditional instructional approaches, including formal mathematics. Cognition can be
viewed as a process of making and manipulating mental models of imaginary objects and events [4].
A scientific model that comprises basic units of coherently structured knowledge in a tested and
integrated framework, if presented appropriately with effective pedagogy, can be enormously useful
in helping students develop correct mental models from which they can make logical inferences such
as explanations, predictions and designs. Molecular modeling, which is an important part of
contemporary scientific research [5, 6], constitutes the theoretical foundation for creating “objective
conceptual models” [7] that can be used by students to explain and investigate many natural
phenomena at the molecular scale and thereby develop concrete mental models about them.
It is important to emphasize the educational significance of computational models based on science
as opposed to movies composed of frames of images and animations based on simple timeline rules.
In a computational model, critical behaviors emerge from algorithms derived from first principles,
which give it the explanatory power that can be used to manifest existing knowledge and the
predictive power that can be used to explore unknown domains. A computational model can
accurately simulate a large number of different phenomena by varying parameters, configurations,
initial conditions and boundary conditions. A movie or an animation, in contrast, can only illustrate a
handful of situations that are recorded or preprogrammed. If a picture is worth 1,000 words and a movie
is worth 1,000 pictures, a model is worth 1,000 movies, or 1000,000 pictures, or 1000,000,000 words. Because
a model provides a much larger intellectual space and more freedom for learners to explore, create
and invent, effective, profound learning is more likely to occur.
This article presents the scientific methods, the educational background and the technical ideas
behind the software. It is assumed that you have some preliminary knowledge about computational
science, educational technology and software engineering. In the first section, we review the basic
procedures of classical MD simulations. In the second section, we discuss the requirements of
educational simulations and the technical work needed to be done to meet these requirements. In the
third section, we introduce our ideas to evaluate student learning based on MW materials. The results
of our educational research are not covered in this technical article. Please see a paper by Pallant and
Tinker [8] if you are interested in them.
How to simulate the motion of atoms and molecules?
Everything moves because of forces, which result from the interactions among atoms and molecules.
We will begin with how theoretical chemists model interatomic and intermolecular interactions.
You can watch a movie about MW at:
There are two levels of modeling for molecular interactions. One is based on quantum mechanics
calculations [9], which is beyond the scope of this article. The other uses empirical forms, which will
be introduced in the following.
Molecular mechanics force fields
Atoms basically interact with each other through van der Waals forces and electrostatic forces. When
they are covalently bonded to others, strong forces hold them together as stable chemical groups. A
widely used mathematical model for the potential energy of a molecular system consists of six types
of interactions:
The first type, VLJ, is the Lennard-Jones potential that has an attractive part representing the van der
Waals energy and a repulsive part representing the Pauli repulsion:
 
  4 ij 
2 i , j ,i  j
 Rij
  ij  
 
 
R  
 
where Rij is the distance between the i-th and j-th atom, εij is called the van der Waals dissociation
energy, and σij is called the collision diameter. The dissociation energy is equal to the amount of
energy needed to pull a pair of atoms in the strongest van der Waals binding state apart. The collision
diameter is approximately the distance at which a pair of atoms bounces off from each other in a
normal, non-reacting condensed state.2 The power of the negative term, which is sometimes also
called the London dispersion force, has a root in the quantum mechanical calculation of the binding
energy of the hydrogen molecule, but the power of the positive term has no apparent theoretical basis
(sometimes, it is set to be 9 to soften the repulsion core for dense systems).
VEL is the electrostatic potential energy according to Coulomb’s Law:
qi q j
2 i , j ,i  j Rij
where qi is the charge of the i-th atom. Compared with the van der Waals potential, the electrostatic
potential is a stronger, more long-range interaction. A pair of charged atoms in vacuum will be able
to “feel” each other from quite a distance away, whereas a pair of neutral atoms will “feel” each
other’s existence only when they are close. For crystals or solutions, the Ewald Sum is often needed to
compute the summation of the weak contributions from numerous remote charges. But if only
qualitative results are needed, this expensive procedure may be skipped to speed up the simulation.
VBS is the bond-stretching energy standing for the elastic interaction between a pair of atoms
connected by a covalent bond, VAB the angle-bending energy standing for the interaction among three
If a pair of atoms can react to form a covalent bond, the length of the bond between them can be
smaller than the collision diameter.
covalently-bonded atoms that form a stable angle, and VPT and VIT the proper and improper torsional
energies standing for the interactions among four covalently-bonded atoms that form a stable proper
and improper dihedral angle (see Fig. 1):
k ml (lm  lm0 ) 2
2 mbonds
k m ( m   m0 ) 2
2 mangles
 V [1  cos(nmm   m )]
2 mtorsionsm
k m ( m   m0 ) 2
2 mtorsions
where lm is the distance between Figure 1: A schematic illustration of the interactions that model cothe two atoms of the m-th bond, lm0 valent bonding: (A) Bond-stretching force; (B) Angle-bending force;
is the equilibrium bond length, kml (C) Proper torsional force; (D) Improper torsional force.
is the bond strength, m is the m-th
angle between the two adjacent bonds that share a common atom, m0 is the equilibrium bond angle,
km is the strength, m is the m-th dihedral angle between the two adjacent angles that share a
common bond, nm is the periodicity factor which determines the number of equilibrium dihedral
angles in a 360º rotation, m is the phase shift, Vm is the amplitude, ξm is the m-th improper dihedral
angle among four atoms that are not bonded successively to one another, ξm0 is the equilibrium
improper dihedral angle, and kmξ is the strength.3
The last four items are called the bonded interactions, which maintain the bond lengths, the bond
angles and the dihedral angles so that chemical groups will remain sterically stable in an MD
simulation4 (in MW we call these constructs radial bonds, angular bonds and torsional bonds). The
first two terms, the Lennard-Jones potential and the electrostatic potential, are called the non-bonded
interactions. In MD simulations, they are more important than the bonded interactions. It is the nonbonded interactions among the atoms of a macromolecule that affect its secondary structure. It is the
non-bonded interactions among the atoms of different molecules that organize them into crystals,
complexes and other assemblies.
It should be pointed out that the decomposition of the potential energy of a molecular system into the
above force field terms is purely empirical. In other words, they are the mathematical models for
describing the chemical forces that stabilize the structures derived from diffraction patterns obtained
3 The bond-stretching potential given by Hooke’s Law does not permit a bond to break. The more
a bond is stretched, the greater is the force to pull the atoms back. As a result, the above force
fields cannot be used to simulate chemical reactions, which involve making and breaking bonds.
We have proposed a method that allows bonds to make and break, and thus makes it possible to
simulate some simple reactions [10].
4 Some molecules, such as benzene, have delocalized bonds that involve more than four atoms.
However, no higher terms of energy decomposition seem to be necessary in this treatment.
using crystallography. The force field approach per se has no power in explaining how the structures
are initially assembled from discrete atoms, which is essentially a question of how to simulate the
origin of life.
Most MD packages for biomolecular simulations are based on the molecular mechanics as described
above, though they may differ in the parameterization (the determination of the bond strengths, the
van der Waals parameters and the partial charges). In MW, to ensure broad applicability, an atom can
be considered as a generic particle with parameters that can be changed freely.
Molecular dynamics simulations
Having defined the interactions among atoms, the position, velocity and acceleration of each atom
are calculated using a numerical method (e.g. the Verlet method or the Runge-Kutta method) to solve
Newton’s equations of motion according to the forces derived from the gradients of the interaction
potentials involving the atom:
   U R , R ,......, R 
mi R
where Ri is the position vector of the i-th atom and mi is its mass. The numeric integration is carried
out stepwisely. The process is repeated at each discrete time step. The trajectory of each individual
object can be tracked by connecting its states to form a time series. The time evolution of the entire
system can be viewed as a fiber bundle of time series in the phase space.
That is all you need to do to get an MD simulation up and running. For advanced topics, such as
boundary conditions, thermostats, pistons, statistical analysis and so on, interested readers can
consult with Ref. 6, or read the online User’s Manual within MW.
The MD method is very useful in scientific research, because it satisfies the following fundamental
physical laws:
 The First Law of Thermodynamics. The Law of Conservation of Energy automatically
emerges in an MD simulation. If there is no energy input/output through external forces or
dissipation through friction, the total energy, which is the summation of the potential energy
and the kinetic energy for all the atoms in the system, remains constant within the tolerance of
numerical errors. This can be used as a criterion to check if a simulation runs properly.
 The Second Law of Thermodynamics. Although the Reversibility Paradox suggests that
classical dynamics might be at odds with the Second Law of Thermodynamics, MD simulations of basic processes such as diffusion, heat transfer and phase transition clearly show that
the entropy of an isolated molecular system always tends to maximize. Despite of the fact that
it is possible to create special initial conditions that lead to a process of entropy reduction in
an isolated system,5 in practice we have never found that such special conditions can spontaneously arise during an MD simulation for a many-body system.
Consider an impact process in an isolated system: a high-speed atom bombards and breaks a
microcrystal. If we stop the simulation after the crystal has been broken, reverse the velocities of
every single atom in the system, and then continue to run it, we can reverse the process—the atoms re-assemble into the original crystal. The entropy decreases in this spontaneous process in
 The Law of Momentum Conservation. As the Law of Conservation of Energy, this Law also
automatically emerges in an MD simulation. This Law dictates each collision among atoms.
The overall result is that the total linear and angular momentum of the system conserves.
 Other statistical laws. Important laws in statistical mechanics, such as the Theorem of Energy
Equipartition, Maxwell’s Theorem of Speed Distribution, and the Boltzmann Distribution are
all guaranteed in MD simulations. We can even simulate the Galton Board that demonstrates
the normal distribution.6
What needs to be done to make molecular dynamics modeling accessible to students?
Most MD programs involve using a pre-processor to prepare simulations and a post-processor to
analyze results. When a calculation is actually being done after it is submitted to a computing service,
the user is rarely given a chance to intervene. Moreover, many programs require the user be able to
work with command lines and scripts, and feel comfortable dealing with raw data. These requirements are prerequisites for scientists. But they become disadvantages when novice users in schools
try to use them without the aid of an expert.
The overarching goal of a learner-friendly MD program is that average students can use it to learn
science. The intermingled complexity of learning and science requires a highly integrated system that
is capable of supporting both. Instead of having students work with a set of distinct tools and switch
back and forth, the ideal technological solution encapsulates of the entire process of building models,
setting up conditions, running and controlling simulations, visualizing results, recording observations, testing, monitoring learning progress, and feedback into a single program with a unified
graphical user interface. In the following subsections, we will discuss the important facets of such a
system in details.
Simulations must be interactive
When a scientist performs an MD simulation, the goal is not to watch how it unfolds on a computer
screen. There is seldom a need to spend precious computing resources on visualizing the intermediate results on the fly. Neither is there a need for the user to alter the parameters and conditions
arbitrarily during a simulation. Often, a simulation starts with a fixed set of inputs, and records the
intermediate results while it is running. When it completes, the stored results can be analyzed to
retrieve the needed information corresponding to the given set of inputs.
For a program to be more educationally useful, however, opportunities must be provided to students
to interact with simulations themselves. To support inquiry, students must be allowed to adjust
parameters and add inputs at any time, and see the emergent behaviors of the simulated systems
the simulation without work and/or cooling from the outside world. This paradoxical result
seems to be a violation of the Second Law of Thermodynamics at first glance. But in practice, the
probability of encountering the state of a many-body system in which all velocities take the values and directions as if they were manually reversed as described above is so low that it can be
neglected. See:
instantaneously. Only through interacting with simulations freely in many different ways and
watching the results can students discover the cause-and-effect relationships revealed by the
simulations and therefore construct their own mental pictures about the important physical and
chemical concepts embodied in the simulations.
Technically, an educational simulation is required to do both the calculations and the visualizations
at the same time in order for students to see the entire process in all possible levels of details, and in
real time in order for them to manipulate the system and observe its responses right away. Translated
into programming terms, the MD code, the visualization tool and the graphical user interface must be
integrated seamlessly in the run time.
The requirement of interactivity, however, limits the sizes of simulatable systems on personal
computers. There are two solutions to this issue. The first one counts on the continuous improvements on computer power. As the Era of Multicore Computing is upon us, parallelizing the software
system to maximally use the power of multicore processors will allow larger systems to be simulated.
The second one uses a coarse-grained approach to reduce large systems into models with a tractable
number of particles, each of which represents a large number of grouped atoms that form a stable
structure with insignificant internal vibrations. These effective particles interact with each other to
move, join and break. For example, it is common to use a model in which an amino acid is represented by a single particle to study the mechanisms of protein folding [11]. Implicit solvation that
employs effective fields to simulate solvent-solute interactions can be used to save the computational
cost needed for the vast number of water molecules that have to be otherwise present in the simulations of molecules in aqueous solutions. The blue window in Fig. 2 shows a coarse-grained model for
micelle with implicit solvation.
Simulations should be easy
to create
Models are abstractions of data that
are composed of different objects at
different locations in the phase space
under different conditions and with
different initial settings. A simulation engine, if generic and powerful
enough, allows us to build as many
models as permitted by its capacity
to match the diversity of reality. It
becomes apparent that, to harness
the power of this theoretical
capability, users need to be able to
turn their modeling ideas into
computational models. A userfriendly system for constructing
simulations has twofold importance.
First, it allows educators to create
alternatives to traditional drawings
Figure 2: A screenshot of the 2D Model Builder in action. The
menu bar and the tool bar above the view window provide many
tools needed to construct models and set up simulations. Each
type of object also provides a pop-up menu and a property editor
from which the user can edit and modify the properties of an
object of its type.
and illustrations. Second, it allows advanced students to design their own models, a process during
which their modeling skills and creative thinking can be trained. Engaging students to construct
models and simulations may also serve as an introduction to molecular modeling, which will benefit
them if they end up choosing science and engineering as their future careers.
MW has a What-You-See-Is-What-You-Get (WYSIWYG) 2D model builder that makes models in a
way that is as easy as making shapes with a drawing program (see Fig. 2). With this model builder,
many types of objects can be added, and every object in a model can be cut, copied, pasted, draggedand-dropped, and edited through the supporting pop-up menus and property editors. User actions
are undoable and redoable. Annotations can be added to make a model easier to understand. For a
coarse-grained particle model, custom images can be attached to “decorate” the particles so that the
appearance of a model will bear a resemblance to illustrations commonly seen in textbooks, particularly for molecular biology (see the simplified graphical representation of lipid molecules that form
the micelle in Fig. 2).
Thanks to the integration of simulation and construction, there is no border between construction and
run in MW. The user can, at any time, run the model while constructing it. This characteristic feature,
stemming from the dynamic nature of MD models, is a major difference between building a static
model and building a dynamic one. In fact, test-running a model under construction is an important
part of the constructing process as it allows the user to build through trail-and-error cycles. Unstable
constructs can be automatically removed or spotted when a model runs (or a procedure of energy
minimization is called).
For users who are not satisfied
with the abstraction of 2D
WYSIWYG 3D model builder
is available for creating 3D
models (Fig. 3). It allows the
user to build molecules from
scratch by laying down atoms
in the 3D space with the
assistance of movable helper
planes and joining them by
radial, angular and torsional
bonds. More complex chemical
systems can be built using a set
includes all the amino acids
and nucleotides and many
small organic molecules. A
Figure 3: A screenshot of the 3D Model Builder in action. It can be used
crystal builder is also provided
to build models as complex as this nano car, which has four short carto build a limited number of
bon nano tubes as the tires and wheels.
crystal lattices. Atoms can be
selected, translated, rotated, duplicated, and deleted as blocks. Three different views are available for
the author to set the perspective to observe a simulation. These views include a regular view in which
the model is viewed as a whole at different zooming distance, a navigation view in which the user
can move the “camera” around to experience an immersive effect of “flying into a molecule”, and a
rover view in which the “camera” is attached to an atom to mimic the effect of “riding on an atom”
when a simulation runs.
There are many pre-made models that cover quite a breadth of science and are freely available in
MW. The collection constitutes a solid scientific foundation of MW. As the software development
continues, more examples will be added to consolidate and expand this foundation.
Simulations should be embedded in a learning environment
Simulations are not broadly useful in classroom without accompanying instructional materials. Many
educational applications provide lesson plans or worksheets for students to use, separated from the
software tools. But the optimal way of using simulations is to embed them in a learning environment
that provides all the essential elements needed for a learning process. We call such a complete
package a learning activity. A good learning activity motivates, scaffolds, and supports student
exploration of models and simulations. It also provides background information, opportunities for
reflection, and methods of monitoring student progress in a designed context and evaluating
learning. MW is a versatile learning environment that offers this kind of classroom-ready learning
activities. Moreover, it provides an authoring system for creating them.
A learning activity in MW usually
consists of multiple pages. A page is a
screen space in which text can be
typed and styled like in a word
processor and many kinds of components can be inserted and customized.
The fact that these elements can be
placed anywhere on a page and mixed
with characters, images and links
allows the author to design high
quality, visually appealing and selfexplanatory pages. Authors who have
experience in creating simple HTML
web pages should be able to author
similar pages in MW without a
Figure 4: This screenshot shows that an MD model for the
Brownian motion is customized and contextualized in an activity
that teaches the concept of scale.
A model container, within which a simulation runs, is a core component that can be inserted into a
page. With a rich set of pluggable components that can communicate with a model container through
command and data channels, a custom user interface can be built for each simulation. The user
interface can comprise controls of the simulation, buttons and sliders for changing the parameters,
and graphs for displaying the outputs. Customized user interfaces are important because they
establish a learning space that is constrained only within the topics covered in the activity. For
example, Fig. 4 shows a customized user interface for showing the Brownian motion. Although
standing behind the scene is the entire engine that is capable of doing numerous other kinds of
simulations, the end user of the activity needs not be concerned with anything beyond what is
presented on this succinct page in order to learn the intended subject.
Another kind of components that can be inserted into a page is questions. There are three types of
questions that the author can set up: multiple choice question, free response question and image
question. An image question is a type of question invented in MW, which requires the student to take
a snapshot image of a simulation as an answer to the question. These questions can be used to test the
student’s prior knowledge (pre-test) and measure the gains after learning through an activity (posttest), for example.
The authoring system, along with the model builders, has allowed us to create a myriad of learning
activities that can be found in the material repository within MW (the internal MW page when you
press the Home Button on the tool bar of MW). Some of these activities have become reasonably
sophisticated and self-contained enough in content to be qualified as interactive textbooks. For those
who are interested in learning how to create models and activities, a comprehensive online User’s
Manual, which is written using the same authoring system, provides numerous working examples.
Simulations should be easy to share
A pedagogy made possible by the easy-to-use model builders is to involve students in creating
simulations, a process that can be devised to embed instructional steps that lead to progressive
conceptual understanding. This learning path, different from inquiry using interactive media based
on existing simulations created by experts, seems to be practical at college level where students have
obtained adequate prior knowledge needed to understand the basics about simulations to get started.
From the point of view of social constructivism, the creation process and the end product must be
shared with others in order for the full effect of learning to take root. It is through the creation of a
molecular model that is shared and becomes what Papert calls a "public entity" that learning is
strongly reinforced [12]. In the process of building, sharing or collaborating, students learn their
subjects well because they have to think hard about them and figure out how to present their
modeling ideas to others.
There is no hurdle for students to share a simulation in MW. The MW system allows users to submit
their modeling work through the MW Space, a web application that facilitates social interactions in
virtual classrooms in a culture where models are the central elements. Students can easily upload
their models to their MW accounts, and decide with whom they will be shared. They can choose to
share with their classmates, their teachers, or the world users of MW. When the user opens an MW
page that contains a simulation, it is ready to run. No extra step is needed. All saved parameters,
configurations and initial conditions are restored in a snap when it is loaded. With the authoring
system, students can make procedures that should be followed to produce the desired results, and
the controls to achieve them, as they would with real experiments. They can also articulate their
motivations, explain what their simulations will show, and narrate how they were constructed. This
makes their work read more like presentations instead of just plain models that may be less comprehensible.
How do we know if students learn
To some extent, going through an MW activity for a student is similar to going through an experimental procedure in a wet lab. The common things are that a student needs to read instructions,
follow certain procedures, operate some instruments, observe what happens, record some data, and
write a report at the end. The Report System in MW monitors what students do during this process,
automatically generates reports, and allows teachers to track down students’ progresses.
Page 1
Page 2
Page 3
The end
The Data Collector and Storage
The MW Space
Student Account
The MW Space
Teacher Account
Figure 5: The flow of collecting student work during a learning activity, supported by the Report System.
The Report System consists of five parts. The first one is questions, which can be embedded into a
learning activity as described in the previous section. The second is a data collector that gathers and
stores a student’s inputs from questions. The third is a snapshot facility that allows students to take a
snapshot image of a simulation or a graph. A snapshot image captures what a student sees happening on the screen, which are sometimes difficult to describe merely with words or numbers. There is
also a set of tools for annotating a snapshot image, which students can use to highlight and explain
certain parts of the image. The fourth is a report generator that automatically converts student data
into a readable page that can be printed, saved or submitted. The fifth is the MW Space that registers
students and teachers, receives and stores reports in a database, and provides feedback to teachers.
Fig. 5 shows a schematic illustration of the workflow of the Report System.
The importance of the feedback loop among material developers, students and teachers must be
stressed. Producing learning materials, designing effective pedagogies, and implementing them in
classroom are essentially a dynamic, time-consuming trial-and-error process that deeply rests on
constructive interactions among the users, the implementers and the developers. It is important that
the teachers be able to track the learning processes of the students, and report problems and inefficacies to the developers. The materials can therefore be revised and the quality be improved.
With the Report System and its future improvements on critical issues such as data mining and data
analysis, MW can be a very useful tool for conducting educational research, particularly for studying
the effectiveness of using computational models in education. While few disagree upon the extensive
use of interactive media in teaching, many (including us) are still searching for the best design
strategy of these media and the best pedagogy of using them. The research on this avenue may yield
insights that would develop the next generation of educational media.
The author thanks B. Berenfeld, D. Damelin, D. Markman, A. Pallant, E. Rosenbaum, B. Tinker and R.
Tinker for their encouragement and numerous suggestions for developing and improving the
Molecular Workbench System. This article is based upon work supported by funding from the
National Science Foundation.
Molecular Literacy,; M. Patrick, unpublished manuscript.
World Index of Molecular Visualization Resources,
R. Tinker and Q. Xie, Applying Computational Science to Education: The Molecular Workbench
Paradigm, Computing in Science and Engineering, in press, 2008.
4. D. Hestenes, Notes for a Modeling Theory of Science, Cognition and Instruction, paper presented
at The 2006 GIREP conference: Modeling in Physics and Physics Education, Amsterdam,
5. T. Schlick, Molecular Modeling and Simulation, 1st Edition, Springer, 2002.
6. A. R. Leach, Molecular Modeling: Principles and Applications, 2nd Edition, Pearson Education,
7. D. Hestenes, Modeling Theory for Math and Science Education, paper presented at the Mathematical Modeling ICTMA-13: Education and Design Sciences, 2007.
8. A. Pallant and R. Tinker, Reasoning with Atomic-Scale Molecular Dynamic Models, Journal of
Science Education and Technology, 13, 51-66 (2004).
9. D. Marx and J. Hutter, Ab initio Molecular Dynamics: Theory and Implementation, in Modern
Methods and Algorithms of Quantum Chemistry, J. Grotendorst (Ed.), John von Neumann Institute for Computing, Jülich, NIC Series, Vol. 1, 2000.
10. Q. Xie and R. Tinker, Molecular Dynamics Simulations of Chemical Reactions for Use in Education, Journal of Chemical Education, 83, 77-83 (2006).
11. H. Jang, C. K. Hall, and Y. Zhou, Assembly and kinetic folding pathways of a tetrameric betasheet complex: Molecular dynamics simulations of simplified off-lattice protein models, Biophysical Journal, 86, 31-49 (2004).
12. S. Papert, Situating Constructionism, in I. Harel and S. Papert (Eds.), Constructionism, Ablex
Publishing Corporation, Norwood, NJ, 1991.