source: src/Potentials/Specifics/ManyBodyPotential_Tersoff.hpp@ d52819

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Last change on this file since d52819 was d52819, checked in by Frederik Heber <heber@…>, 12 years ago

Extracted initial parameter setting per specific potential to FunctionModel::setParametersToRandomInitialValues().

  • this is preparatory for generalizing potential fitting.
  • Property mode set to 100644
File size: 9.3 KB
Line 
1/*
2 * ManyBodyPotential_Tersoff.hpp
3 *
4 * Created on: Sep 26, 2012
5 * Author: heber
6 */
7
8#ifndef MANYBODYPOTENTIAL_TERSOFF_HPP_
9#define MANYBODYPOTENTIAL_TERSOFF_HPP_
10
11// include config.h
12#ifdef HAVE_CONFIG_H
13#include <config.h>
14#endif
15
16#include <boost/function.hpp>
17#include <cmath>
18#include <limits>
19
20#include "Potentials/EmpiricalPotential.hpp"
21#include "Potentials/SerializablePotential.hpp"
22#include "FunctionApproximation/FunctionModel.hpp"
23
24class TrainingData;
25
26/** This class is the implementation of the Tersoff potential function.
27 *
28 * \note The arguments_t argument list is here in the following order:
29 * -# first \f$ r_{ij} \f$,
30 * -# then all \f$ r_{ik} \f$ that are within the cutoff, i.e. \f$ r_{ik} < R + D\f$
31 *
32 */
33class ManyBodyPotential_Tersoff :
34 virtual public EmpiricalPotential,
35 virtual public FunctionModel,
36 virtual public SerializablePotential
37{
38 //!> grant unit test access to internal parts
39 friend class ManyBodyPotential_TersoffTest;
40 // some repeated typedefs to avoid ambiguities
41 typedef FunctionModel::arguments_t arguments_t;
42 typedef FunctionModel::result_t result_t;
43 typedef FunctionModel::results_t results_t;
44 typedef EmpiricalPotential::derivative_components_t derivative_components_t;
45 typedef FunctionModel::parameters_t parameters_t;
46public:
47 /** Constructor for class ManyBodyPotential_Tersoff.
48 *
49 * @param _triplefunction function that returns a list of triples (i.e. the
50 * two remaining distances) to a given pair of points (contained as
51 * indices within the argument)
52 */
53 ManyBodyPotential_Tersoff(
54 const ParticleTypes_t &_ParticleTypes,
55 boost::function< std::vector<arguments_t>(const argument_t &, const double)> &_triplefunction
56 );
57
58 /** Constructor for class ManyBodyPotential_Tersoff.
59 *
60 * @param _R offset for cutoff
61 * @param _S halfwidth for cutoff relative to \a _R
62 * @param A
63 * @param B
64 * @param lambda
65 * @param mu
66 * @param lambda3
67 * @param alpha
68 * @param beta
69 * @param chi
70 * @param omega
71 * @param n
72 * @param c
73 * @param d
74 * @param h
75 * @param offset
76 * @param _triplefunction function that returns a list of triples (i.e. the
77 * two remaining distances) to a given pair of points (contained as
78 * indices within the argument)
79 */
80 ManyBodyPotential_Tersoff(
81 const ParticleTypes_t &_ParticleTypes,
82 const double &_R,
83 const double &_S,
84 const double &_A,
85 const double &_B,
86 const double &_lambda,
87 const double &_mu,
88 const double &_lambda3,
89 const double &_alpha,
90 const double &_beta,
91 const double &_chi,
92 const double &_omega,
93 const double &_n,
94 const double &_c,
95 const double &_d,
96 const double &_h,
97 const double &_offset,
98 boost::function< std::vector<arguments_t>(const argument_t &, const double)> &_triplefunction);
99
100 /** Destructor of class ManyBodyPotential_Tersoff.
101 *
102 */
103 virtual ~ManyBodyPotential_Tersoff() {}
104
105 /** Evaluates the Tersoff potential for the given arguments.
106 *
107 * @param arguments single distance
108 * @return value of the potential function
109 */
110 results_t operator()(const arguments_t &arguments) const;
111
112 /** Evaluates the derivative of the Tersoff potential with respect to the
113 * input variables.
114 *
115 * @param arguments single distance
116 * @return vector with components of the derivative
117 */
118 derivative_components_t derivative(const arguments_t &arguments) const;
119
120 /** Evaluates the derivative of the function with the given \a arguments
121 * with respect to a specific parameter indicated by \a index.
122 *
123 * \param arguments set of arguments as input variables to the function
124 * \param index derivative of which parameter
125 * \return result vector containing the derivative with respect to the given
126 * input
127 */
128 results_t parameter_derivative(const arguments_t &arguments, const size_t index) const;
129
130 /** Return the token name of this specific potential.
131 *
132 * \return token name of the potential
133 */
134 const std::string& getToken() const
135 { return potential_token; }
136
137 /** Returns a vector of parameter names.
138 *
139 * This is required from the specific implementation
140 *
141 * \return vector of strings containing parameter names
142 */
143 const ParameterNames_t& getParameterNames() const
144 { return ParameterNames; }
145
146 /** States whether lower and upper boundaries should be used to constraint
147 * the parameter search for this function model.
148 *
149 * \return true - constraints should be used, false - else
150 */
151 bool isBoxConstraint() const {
152 return true;
153 }
154
155 /** Returns a vector which are the lower boundaries for each parameter_t
156 * of this FunctionModel.
157 *
158 * \return vector of parameter_t resembling lowest allowed values
159 */
160 parameters_t getLowerBoxConstraints() const {
161 parameters_t lowerbound(getParameterDimension(), -std::numeric_limits<double>::max());
162// lowerbound[R] = 0.;
163// lowerbound[S] = 0.;
164// lowerbound[lambda3] = 0.;
165// lowerbound[alpha] = 0.;
166 lowerbound[beta] = std::numeric_limits<double>::min();
167 lowerbound[n] = std::numeric_limits<double>::min();
168 lowerbound[c] = std::numeric_limits<double>::min();
169 lowerbound[d] = std::numeric_limits<double>::min();
170 return lowerbound;
171 }
172
173 /** Returns a vector which are the upper boundaries for each parameter_t
174 * of this FunctionModel.
175 *
176 * \return vector of parameter_t resembling highest allowed values
177 */
178 parameters_t getUpperBoxConstraints() const {
179 return parameters_t(getParameterDimension(), std::numeric_limits<double>::max());
180 }
181
182 /** Returns a bound function to be used with TrainingData, extracting distances
183 * from a Fragment.
184 *
185 * \param charges vector of charges to be extracted
186 * \return bound function extracting distances from a fragment
187 */
188 FunctionModel::extractor_t getFragmentSpecificExtractor(const charges_t &charges) const;
189
190private:
191 /** Prohibit private default constructor.
192 *
193 * We essentially need the triplefunction, hence without this function cannot
194 * be.
195 */
196 ManyBodyPotential_Tersoff();
197
198private:
199 /** This function represents the cutoff \f$ f_C \f$.
200 *
201 * @param distance variable of the function
202 * @return a value in [0,1].
203 */
204 result_t function_cutoff(
205 const double &distance
206 ) const;
207 /** This function has the exponential feature from the Morse potential.
208 *
209 * @param prefactor prefactor parameter to exp function
210 * @param lambda scale parameter of exp function's argument
211 * @param distance variable of the function
212 * @return
213 */
214 result_t function_smoother(
215 const double &prefactor,
216 const double &lambda,
217 const double &distance
218 ) const;
219
220 /** This function represents \f$ (1 + \alpha^n \eta^n)^{-1/2n} \f$.
221 *
222 * @param alpha prefactor to eta function
223 * @param r_ij distance argument
224 * @param eta result value of eta or zeta
225 * @return \f$ (1 + \alpha^n \eta^n)^{-1/2n} \f$
226 */
227 result_t function_prefactor(
228 const double &alpha,
229 const double &eta
230 ) const;
231
232 result_t
233 function_eta(
234 const argument_t &r_ij
235 ) const;
236
237 result_t
238 function_zeta(
239 const argument_t &r_ij
240 ) const;
241
242 result_t
243 function_theta(
244 const double &r_ij,
245 const double &r_ik,
246 const double &r_jk
247 ) const;
248
249 result_t
250 function_angle(
251 const double &r_ij,
252 const double &r_ik,
253 const double &r_jk
254 ) const;
255
256private:
257 result_t
258 function_derivative_c(
259 const argument_t &r_ij
260 ) const;
261
262 result_t
263 function_derivative_d(
264 const argument_t &r_ij
265 ) const;
266
267 result_t
268 function_derivative_h(
269 const argument_t &r_ij
270 ) const;
271
272public:
273 enum parameter_enum_t {
274 A,
275 B,
276 lambda,
277 mu,
278 beta,
279 n,
280 c,
281 d,
282 h,
283 offset,
284// R,
285// S,
286// lambda3,
287// alpha,
288// chi,
289// omega,
290 MAXPARAMS
291 };
292
293private:
294 //!> parameter vector with parameters as in enum parameter_enum_t
295 parameters_t params;
296
297public:
298 // some internal parameters which are fixed
299 const double R;
300 const double S;
301 const double lambda3;
302 const double alpha;
303 const double chi;
304 const double omega;
305
306public:
307 /** Setter for parameters as required by FunctionModel interface.
308 *
309 * \param _params given set of parameters
310 */
311 void setParameters(const parameters_t &_params);
312
313 /** Getter for parameters as required by FunctionModel interface.
314 *
315 * \return set of parameters
316 */
317 parameters_t getParameters() const
318 {
319 return params;
320 }
321
322 /** Sets the parameter randomly within the sensible range of each parameter.
323 *
324 * \param data container with training data for guesstimating range
325 */
326 void setParametersToRandomInitialValues(const TrainingData &data);
327
328 /** Getter for the number of parameters of this model function.
329 *
330 * \return number of parameters
331 */
332 size_t getParameterDimension() const
333 {
334 return MAXPARAMS;
335 }
336
337private:
338 //!> bound function that obtains the triples for the internal coordinationb summation.
339 const boost::function< std::vector< arguments_t >(const argument_t &, const double)> &triplefunction;
340
341 //!> static definitions of the parameter name for this potential
342 static const ParameterNames_t ParameterNames;
343
344 //!> static token of this potential type
345 static const std::string potential_token;
346};
347
348
349#endif /* MANYBODYPOTENTIAL_TERSOFF_HPP_ */
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