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您的检索式：作者名="Alejandro Strachan"
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| 1 | 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 |
| 2 | Thermal decomposition of RDX from reactive molecular dy- namics显示文摘 | Alejandro Strachan Edward M Kober Adri C T van Duin | 2005 | Journal of Chemical Physics2005，122，5： | 1 |
| 3 | Nonequilibrium melting and crystallization of a model Lennard-Jones system显示文摘 | Shen-Nian Luo Alejandro Strachan Damian C Swift | 2004 | Journal of Chemical Physics2004，120，： | 1 |
| 4 | Mapping microstructure to shock-induced temperature fields using deep learning显示文摘The response of materials to shock loading is important to planetary science,aerospace engineering,and energetic materials.Thermally activated processes,including chemical reactions and phase transitions,are significantly accelerated by energy localization into hotspots.These result from the interaction of the shockwave with the materials’microstructure and are governed by complex,coupled processes,including the collapse of porosity,interfacial friction,and localized plastic deformation.These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone.We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input.The accuracy of the Microstructure-Informed Shock-induced Temperature net(MISTnet)model is higher than the current state of the art and its evaluation requires a fraction of the computation cost. | Chunyu Li Juan Carlos Verduzco Brian H.Lee Robert J.Appleton Alejandro Strachan | 2023 | npj Computational Materials2023，，1： | 0 |
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