15288

13732

كارشناسي ارشد

ماده چگال

اصفهان : دانشگاه صنعتي اصفهان

1398

يازده، 71ص.: مصور، جدول نمودار

مجتبي اعلايي

جواد هاشمي فر

اسماعيل عبدالحسيني، فرهاد شهبازي

1398/08/12

كتابنامه

فيزيك

فيزيك

1398/11/20

2571648

Calculation of Silicon clusters atomization energy by machine learning approach Akram Abedi Orang akram abedi@ph iut ac ir September 15 2019 Department of Physics Isfahan University of Technology Isfahan 84156 83111 Iran University Code IUT 77142 Degree M S c Language Persian supervisor Dr Mojtaba Alaei Abstract Machine learning is a subset of computer science and studies algorithms whose perfor mance improves with data through experience Machine learning can be defined as solving a scientific problem by gathering data sets and constructing a statistical model based on that data set Machine learning is found abundant in everyday life such as data mining robot control handwriting recognition pattern recognition e g finger print identification character and voice recognition software and etc The potential energy surface is a valuable multidimensional function which represents the potential energy of a system as a function of atomic positions The concept of poten tial energy surface is based on the Born Oppenheimer approximation Recent advances in machine learning have now introduced an alternative method of estimating poten tial energy surface instead of electronic structure calculations which is much faster and more accurate so computational costs are reduced In this thesis we decide to present a general machine learning based attitude in which we predict the formation energy of silicon clusters For this purpose first atomic positions with appropriate descriptors should be presented to the machine learning algorithm The descriptor converts the in put information of the atomic structure into machine understandable language we used the QMML computational package For this project This computation package includes the Column Matrix descriptor and the Many Body Tensor descriptor Gaussian approx imation potential is an important method of potential based on machine learning that is based on the kernel that obtained by combining a suitable structural descriptor and a kernel to create a relationship between structure and energy In this project we used both the Laplacian and Gaussian kernels and made good predictions on silicon clusters Keywords machine learning Born Oppenheimer potential energy surface silicon clusters Descriptors Coulomb matrix Many body tensor Laplacian kernel Gaussian kernel

مجتبي اعلايي

جواد هاشمي فر

اسماعيل عبدالحسيني، فرهاد شهبازي