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您的检索式：作者名="Md Mahbubul Islam"
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| 1 | The ReaxFF reactive force-field: development, applications and future directions显示文摘The reactive force-field(ReaxFF)interatomic potential is a powerful computational tool for exploring,developing and optimizing material properties.Methods based on the principles of quantum mechanics(QM),while offering valuable theoretical guidance at the electronic level,are often too computationally intense for simulations that consider the full dynamic evolution of a system.Alternatively,empirical interatomic potentials that are based on classical principles require significantly fewer computational resources,which enables simulations to better describe dynamic processes over longer timeframes and on larger scales.Such methods,however,typically require a predefined connectivity between atoms,precluding simulations that involve reactive events.The ReaxFF method was developed to help bridge this gap.Approaching the gap from the classical side,ReaxFF casts the empirical interatomic potential within a bond-order formalism,thus implicitly describing chemical bonding without expensive QM calculations.This article provides an overview of the development,application,and future directions of the ReaxFF method. | Thomas P Senftle Sungwook Hong Md Mahbubul Islam Sudhir B Kylasa Yuanxia Zheng Yun Kyung Shin Chad Junkermeier Roman Engel-Herbert Michael J Janik Hasan Metin Aktulga Toon Verstraelen Ananth Grama Adri CT van Duin | 2016 | npj Computational Materials2016，，1： | 22 |
| 2 | Neural network reactive force field for C,H,N,and O systems显示文摘Reactive force fields have enabled an atomic level description of a wide range of phenomena,from chemistry at extreme conditions to the operation of electrochemical devices and catalysis.While significant insight and semi-quantitative understanding have been drawn from such work,the accuracy of reactive force fields limits quantitative predictions.We developed a neural network reactive force field(NNRF)for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine(RDX).NNRF was trained using energies and forces of a total of 3100 molecules(11,941 geometries)and 15 condensed matter systems(32,973 geometries)obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions.The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained.The root mean square(RMS)error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials. | Pilsun Yoo Michael Sakano Saaketh Desai Md Mahbubul Islam Peilin Liao Alejandro Strachan | 2021 | npj Computational Materials2021，，1： | 3 |
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