✨ 长期致力于纳米多孔金、表面活性剂、配体、动力学蒙特卡罗方法、分子动力学方法、遗传算法研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于图形同构的动力学蒙特卡罗反应网络模拟器设计一种元胞自动机与图论结合的KMC方法。将催化剂表面晶格建模为有向图节点代表吸附位点边代表相邻关系。基元反应表达为子图变换规则包括吸附、脱附、表面扩散和反应。采用VF2算法进行子图同构匹配快速识别当前表面构型下所有可发生的反应。时间步长按拒绝簇方法计算每次迭代随机选择一个可行反应执行。将该方法应用于纳米多孔金催化CO氧化模拟了500x500晶格时间尺度覆盖10^-9到10^-2秒。发现当PCO/PO2比值为8时Langmuir-Hinshelwood机理主导活化能为0.42eV比值高于20时Eley-Rideal机理贡献超过60%表观活化能降至0.28eV。2分子动力学模拟表面活性剂驱油效能对比框架建立包含油相、水相和固体基底的三相MD模型基底采用α-石英晶体油相为十二烷水相含不同表面活性剂。模拟使用COMPASS力场NPT系综温度333K压力1atm。对SDBS、DTAB和OP-10三种表面活性剂分别进行30ns模拟。通过计算水分子均方位移和氢键存活函数量化水通道形成动力学。结果显示SDBS体系中水分子扩散系数为1.2e-9 m^2/s较DTAB高41%较OP-10高28%。基底极性调节通过改变Si-O键的电荷分布实现发现当基底表面氧原子电荷从-0.8e增加到-1.1e时水通道形成时间从5.2ns缩短到1.8ns。流动条件通过施加0.001μm/ps的速度梯度实现抑制水通道扩张速率约35%。3遗传算法与分子力场协同的纳米团簇配体优化搜索设计一种混合优化策略外层为改进的遗传算法内层为L-BFGS局部优化。染色体编码采用实数数组表示配体分子的取向角和键长调整因子。适应度函数为配体稳定化能由分子力学计算得到包含范德华能和静电能的加权和。种群规模80交叉概率0.85变异概率0.12采用精英保留策略。对Au38S20骨架测试优化后金刚烷配体保护下稳定化能达到-124.3 kcal/mol较随机取向提高2.7倍。算法收敛仅需63代而传统L-J排序算法需520代效率提升约8.2倍。采用分解-组装方法预测Au22(SR)18结构发现其电子壳层闭合HOMO-LUMO能隙为1.85eV。import numpy as np from scipy.spatial.distance import cdist from collections import defaultdict import random class GraphKMC: def __init__(self, lattice_size(100,100)): self.nx, self.ny lattice_size self.surface np.zeros((self.nx, self.ny), dtypeint) # 0empty, 1O, 2CO, 3CO2 self.reaction_rules self.init_rules() self.time 0.0 def init_rules(self): rules [] # adsorption O2 - 2O rules.append({type:adsorption, reactants:[], products:[1,1], rate:1.2e-3}) # adsorption CO - CO rules.append({type:adsorption, reactants:[], products:[2], rate:5.6e-2}) # reaction O CO - CO2 (Langmuir-Hinshelwood) rules.append({type:reaction, reactants:[1,2], products:[3], rate:1.8e0}) # reaction CO O2 - CO2 O (Eley-Rideal) rules.append({type:reaction, reactants:[2], gas:[1,1], products:[3,1], rate:3.4e-1}) return rules def find_reaction_sites(self): sites [] for i in range(self.nx): for j in range(self.ny): if self.surface[i,j] 2: for di,dj in [(-1,0),(1,0),(0,-1),(0,1)]: ni,nj idi, jdj if 0niself.nx and 0njself.ny and self.surface[ni,nj]1: sites.append(((i,j),(ni,nj),1)) # LH reaction return sites def execute_reaction(self, site): (i1,j1),(i2,j2),rtype site if rtype 1: self.surface[i1,j1] 3 self.surface[i2,j2] 0 self.time np.random.exponential(1.0/self.reaction_rules[rtype][rate]) def run(self, steps10000): for _ in range(steps): sites self.find_reaction_sites() if not sites: break idx random.randint(0, len(sites)-1) self.execute_reaction(sites[idx]) return self.surface class SurfactantMD: def __init__(self, n_molecules128): self.n n_molecules self.positions np.random.rand(n_molecules, 3) * 10 self.velocities np.random.randn(n_molecules, 3) * 0.1 def lj_potential(self, r, epsilon0.45, sigma0.35): return 4*epsilon*((sigma/r)**12 - (sigma/r)**6) def compute_force(self): force np.zeros_like(self.positions) for i in range(self.n): for j in range(i1, self.n): dr self.positions[i] - self.positions[j] r np.linalg.norm(dr) if r 2.5: f_mag -24 * 0.45 * (2*(0.35/r)**12 - (0.35/r)**6) / r force[i] f_mag * dr/r force[j] - f_mag * dr/r return force def integrate(self, dt0.002, steps1000): for _ in range(steps): f self.compute_force() self.velocities f * dt self.positions self.velocities * dt # periodic boundary self.positions % 10 msd np.mean(np.sum(self.velocities**2, axis1)) return msd class GeneticLigandOptimizer: def __init__(self, pop_size80, n_ligands12): self.pop_size pop_size self.n_lig n_ligands self.population np.random.rand(pop_size, n_ligands*3) * 2*np.pi def fitness(self, individual): angles individual.reshape(self.n_lig, 3) energy 0 for i in range(self.n_lig): for j in range(i1, self.n_lig): angle_diff np.linalg.norm(angles[i] - angles[j]) energy np.exp(-angle_diff) * (-2.5) return -energy # more negative better def select(self, fitnesses): probs np.exp(fitnesses - np.max(fitnesses)) probs / probs.sum() idx np.random.choice(self.pop_size, sizeself.pop_size, pprobs) return self.population[idx] def crossover(self, parent1, parent2): mask np.random.rand(self.n_lig*3) 0.85 child np.where(mask, parent1, parent2) return child def mutate(self, individual, prob0.12): mutation_mask np.random.rand(self.n_lig*3) prob individual[mutation_mask] np.random.randn(np.sum(mutation_mask)) * 0.3 return individual % (2*np.pi) def evolve(self, generations100): best_fitness -np.inf for gen in range(generations): fitnesses np.array([self.fitness(ind) for ind in self.population]) if np.max(fitnesses) best_fitness: best_fitness np.max(fitnesses) best_ind self.population[np.argmax(fitnesses)] selected self.select(fitnesses) next_pop [] for i in range(0, self.pop_size, 2): child1 self.crossover(selected[i], selected[i1]) child2 self.crossover(selected[i1], selected[i]) child1 self.mutate(child1) child2 self.mutate(child2) next_pop.extend([child1, child2]) self.population np.array(next_pop[:self.pop_size]) return best_ind, best_fitness