ab initio molecular dynamics ( aimd ) 
 simulations enable the accurate 
 description of reactive molecule  surface scattering especially 
 if energy transfer involving surface phonons is important . however , 
 presently , the computational expense of aimd rules out its application 
 to systems where reaction probabilities are smaller than about 1% . 
 here 
 we show that this problem can be overcome by a high - dimensional 
 neural network fit of the molecule 
 surface interaction potential , 
 which also incorporates the dependence on phonons by taking into account 
 all degrees of freedom of the surface explicitly . as shown for n2 + ru(0001 ) , which is a prototypical case for highly activated 
 dissociative chemisorption , the method allows an accurate description 
 of the coupling of molecular and surface atom motion and accurately 
 accounts for vibrational properties of the employed slab model of 
 ru(0001 ) . 
 the neural network potential allows reaction probabilities 
 as low as 105 to be computed , showing good agreement 
 with experimental results .