Modeling is the development of a mathematical representation of an actual or proposed set (group) of interactions that can be employed to predict the functioning of the set (group) under possible conditions.\u00a0 Interest in modeling has grown since the late 1970 and early 1980s coincident with the development of more and more powerful computers.\u00a0 The size of models has grown and the additional sophistication of the evaluation has increased.<\/p>\n
What does this have to do with nanotechnology?\u00a0 It has been demonstrated that physical material properties change as the size of the material decreases into the low double-digit nanometers.\u00a0 The application of gold nanoparticles to produce the color of red in stained glass windows is an example of usage that is hundred of years old.\u00a0 The effect of sliver nanoparticles on bacteria in another example.\u00a0 Is it possible to develop a model that will predict the behavior of materials for any size or shape?\u00a0 The answer is: \u201cYes, BUT\u201d.<\/p>\n
One instance that raises a question about modeling goes back to the early 1960s.\u00a0 Professor Edward Lorenz (MIT) employed a computer simulation of weather patterns.\u00a0 Supposedly, he left to get a cup of coffee and when he returned, he was very surprised.\u00a0 He was rerunning a simulation he had previously made.\u00a0 Except, this time he rounded 0.506127 to 0.506.\u00a0 This small change in value changed the entire pattern of the projected two months of weather results. [Ref. #1]\u00a0 This result has become know as the butterfly effect, which is used to refer to a very tiny occurrence can change the course of what follows.\u00a0 This terminology as used in chaos theory, represents the dependence sensitivity of the initial modeling conditions that can produce significant changes in the resultant projections. [Ref. #2]<\/p>\n
In a quote I attribute to Professor Robert Shannon of Texas A&M University, he said: \u201cAll models are wrong!\u00a0 Some are useful.\u201d\u00a0 Once the natural occurring probability impacts the occurrence of the data employed, the results are uncertain.\u00a0 The interesting thought is that complex models run on 16-bit computers could have completely different results from the same model run on a 64-bit computer.\u00a0 In addition to this difference in precision, the initial starting conditions are important.\u00a0 In many models, the initial conditions are left empty or zero and the model is run to take remove the \u201cinitialization\u201d bias.\u00a0 Obviously, models, like weather forecasting, need initialization data.\u00a0 In the weather example, there are a number of tropical storm forecasting models that are compared.\u00a0 Each of the model\u2019s projection is actually based on a number of runs of that data to determine an average or best fit.\u00a0 In comparisons, the European model, which has more sensors than the US model, tends to be a bit more accurate.\u00a0 Trying to predict smaller effects is more difficult because the minor change in variables can cause greater effects when trying to restrict the weather impact to smaller regions.<\/p>\n
So, the question is what kind of results can be anticipated with modeling on nanotechnology.\u00a0 One example is the recently identified Schwarzite carbon structure. [Ref. #3]\u00a0 \u00a0\u00a0Material with these properties was predicted as early as the 1880s, but no one was able to create it to validate the theoretical (modeling) results.\u00a0 Now that it has been created, the predicted properties can be tested and evaluated.\u00a0 Once the material is in hand, then actual testing can be done.\u00a0 Predictions can point out directions to follow, but do not guarantee that the material will have the specific, predicted properties.<\/p>\n
These was recent article [Ref. #4] that implies computer simulation will be used to develop the laws of nature and end much work being done in theoretical physics.\u00a0 While there might be benefits gained and some people are indicating that artificial intelligence (AI) will provide interesting breakthroughs, these \u201cdiscoveries\u201d will still need to be proven.<\/p>\n
One thing that modeling can not do is to find surprises.\u00a0 University of Wisconsin physicists constructed a 2-D form of tungsten-ditelluride [Ref. #5] that has unanticipated properties, including \u201cspontaneous electrical polarization\u201d from combining two mono-layers of the material.\u00a0 Until models can be constructed that contain all the variables and correct relationships among particles, models will be \u201cwrong\u201d but useful.<\/p>\n
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References:<\/p>\n
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