Home Chemistry Neural community interatomic potential for laser-excited supplies

Neural community interatomic potential for laser-excited supplies

0
Neural community interatomic potential for laser-excited supplies

[ad_1]

  • Becker, C. A., Tavazza, F., Trautt, Z. T. & Buarque de Macedo, R. A. Concerns for selecting and utilizing drive fields and interatomic potentials in supplies science and engineering. Curr. Opin. Stable State Mater. Sci. 17, 277–283 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Handley, C. M. & Behler, J. Subsequent technology interatomic potentials for condensed techniques. Eur. Phys. J. B 87, 1–16 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Durrant, J. D. & McCammon, J. A. Molecular dynamics simulations and drug discovery. BMC Biol. 9, 71–80 (2011).

    Article 
    CAS 

    Google Scholar
     

  • Hollingsworth, S. A. & Dror, R. D. Molecular dynamics simulation for all. Neuron 99, 1129–1143 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Jones, R. O. Density useful principle: its origins, rise to prominence, and future. Rev. Mod. Phys. 87, 897–923 (2015).

    Article 

    Google Scholar
     

  • Silvestrelli, P. L., Alavi, A., Parrinello, M. & Frenkel, D. Ab initio molecular dynamics simulation of laser melting of silicon. Phys. Rev. Lett. 77, 3149–3152 (1996).

    Article 
    CAS 

    Google Scholar
     

  • Zijlstra, E. S., Kalitsov, A., Zier, T. & Garcia, M. E. Squeezed thermal phonons precurse nonthermal melting of silicon as a perform of fluence. Phys. Rev. X 3, 011005 (2013).

    CAS 

    Google Scholar
     

  • Thapa, R., Ugwumadu, C., Nepal, Okay., Trembly, J. & Drabold, D. Ab initio simulation of amorphous graphite. Phys. Rev. Lett. 128, 236402 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Deringer, V. L., Caro, M. A. & Csányi, G. Machine studying interatomic potentials as rising instruments for supplies science. Adv. Mater. 31, 1902765 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Behler, J. 4 generations of high-dimensional neural community potentials. Chem. Rev. 121, 10037–10072 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Dhaliwal, G., Nair, P. B. & Singh, C. V. Machine realized interatomic potentials utilizing random options. Npj Comput. Mater. 8, 7 (2022).

    Article 

    Google Scholar
     

  • Xu, C. & Smart, F. Latest advances in fibre lasers for nonlinear microscopy. Nat. Photon. 7, 875–882 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Chung, S. H. & Mazur, E. Surgical purposes of femtosecond lasers. J. Biophotonics 2, 557–572 (2009).

    Article 

    Google Scholar
     

  • Sugioka, Okay. & Cheng, Y. Ultrafast lasers–dependable instruments for superior supplies processing. Mild Sci. Appl. 3, e149 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Phillips, Okay. C., Gandhi, H. H., Mazur, E. & Sundaram, S. Okay. Ultrafast laser processing of supplies: A evaluate. Adv. Decide. Photon. 7, 684–712 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Shokeen, L. & Schelling, P. Okay. Thermodynamics and kinetics of silicon underneath situations of sturdy digital excitation. J. Appl. Phys. 109, 073503 (2011).

    Article 

    Google Scholar
     

  • Darkins, R., Ma, P.-W., Murphy, S. T. & Duffy, D. M. Simulating electronically pushed structural adjustments in silicon with two-temperature molecular dynamics. Phys. Rev. B 98, 024304 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Bauerhenne, B., Lipp, V. P., Zier, T., Zijlstra, E. S. & Garcia, M. E. Self-learning methodology for building of analytical interatomic potentials to explain laser-excited supplies. Phys. Rev. Lett. 124, 085501 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Cavalleri, A. et al. Femtosecond structural dynamics in VO2 throughout an ultrafast solid-solid section transition. Phys. Rev. Lett. 87, 237401 (2001).

    Article 
    CAS 

    Google Scholar
     

  • Collet, E. et al. Laser-induced ferroelectric structural order in an natural charge-transfer crystal. Science 300, 612–615 (2003).

    Article 
    CAS 

    Google Scholar
     

  • Sciaini, G. et al. Digital acceleration of atomic motions and disordering in bismuth. Nature 458, 56–59 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Buzzi, M., Först, M., Mankowsky, R. & Cavalleri, A. Probing dynamics in quantum supplies with femtosecond X-rays. Nat. Rev. Mater. 3, 299–311 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Johnson, S. L. et al. Immediately observing squeezed phonon states with femtosecond X-ray diffraction. Phys. Rev. Lett. 102, 175503 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Cheng, T. Okay. et al. Mechanism for displacive excitation of coherent phonons in Sb, Bi, Te, and Ti2O3. Appl. Phys. Lett. 59, 1923–1925 (1991).

    Article 
    CAS 

    Google Scholar
     

  • Hase, M., Kitajima, M., Constantinescu, A. M. & Petek, H. The beginning of a quasiparticle in silicon noticed in time-frequency area. Nature 426, 51–54 (2003).

    Article 
    CAS 

    Google Scholar
     

  • Gamaly, E. G. & Rode, A. V. Physics of ultra-short laser interplay with matter: From phonon excitation to final transformations. Prog. Quantum. Electron. 37, 215–323 (2013).

    Article 

    Google Scholar
     

  • Recoules, V., Clérouin, J., Zérah, G., Anglade, P. M. & Mazevet, S. Impact of intense laser irradiation on the lattice stability of semiconductors and metals. Phys. Rev. Lett. 96, 055503 (2006).

    Article 
    CAS 

    Google Scholar
     

  • Grigoryan, N. S., Zier, T., Garcia, M. E. & Zijlstra, E. S. Ultrafast structural phenomena: Concept of phonon frequency adjustments and simulations with code for extremely excited valence electron techniques. J. Decide. Soc. Am. B 31, 22–27 (2014).

    Article 

    Google Scholar
     

  • Fritz, D. M. et al. Ultrafast bond softening in bismuth: Mapping a strong’s interatomic potential with X-rays. Science 315, 633–636 (2007).

    Article 
    CAS 

    Google Scholar
     

  • Bauerhenne, B. Supplies Interplay with Femtosecond Lasers: Concept and Extremely-large-scale Simulations of Thermal and Nonthermal Phenomena (Springer, 2021).

  • Varlamova, O., Costache, F., Reif, J. & Bestehorn, M. Self-organized sample formation upon femtosecond laser ablation by circularly polarized gentle. Appl. Surf. Sci. 252, 4702–4706 (2006).

    Article 
    CAS 

    Google Scholar
     

  • Reif, J., Varlamova, O., Varlamov, S. & Bestehorn, M. The function of uneven excitation in self-organized nanostructure formation upon femtosecond laser ablation. AIP Conf. Proc. 1464, 428–441 (2012).

    Article 
    CAS 

    Google Scholar
     

  • Bonse, J. & Gräf, S. Ten open questions on laser-induced periodic floor constructions. Nanomaterials 11, 3326 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Clean, T. B., Brown, S. D., Calhoun, A. W. & Doren, D. J. Neural community fashions of potential power surfaces. J. Chem. Phys. 103, 4129–4137 (1995).

    Article 
    CAS 

    Google Scholar
     

  • Gassner, H., Probst, M., Lauenstein, A. & Hermansson, Okay. Illustration of intermolecular potential capabilities by neural networks. J. Phys. Chem. A 102, 4596–4605 (1998).

    Article 
    CAS 

    Google Scholar
     

  • Lorenz, S., Groß, A. & Scheffler, M. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks. Chem. Phys. Lett. 395, 210–215 (2004).

    Article 
    CAS 

    Google Scholar
     

  • Manzhos, S., Wang, X., Dawes, R. & Carrington, T. A nested molecule-independent neural community strategy for high-quality potential matches. J. Phys. Chem. A 110, 5295–5304 (2006).

    Article 
    CAS 

    Google Scholar
     

  • Behler, J. & Parrinello, M. Generalized neural-network illustration of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).

    Article 

    Google Scholar
     

  • Behler, J. Atom-centered symmetry capabilities for developing high-dimensional neural community potentials. J. Chem. Phys. 134, 074106 (2011).

    Article 

    Google Scholar
     

  • Behler, J., Martoňák, R., Donadio, D. & Parrinello, M. Metadynamics simulations of the high-pressure phases of silicon using a high-dimensional neural community potential. Phys. Rev. Lett. 100, 185501 (2008).

    Article 

    Google Scholar
     

  • Artrith, N. & Behler, J. Excessive-dimensional neural community potentials for steel surfaces: a prototype research for copper. Phys. Rev. B 85, 045439 (2012).

    Article 

    Google Scholar
     

  • Gastegger, M. & Marquetand, P. Excessive-dimensional neural community potentials for natural reactions and an improved coaching algorithm. J. Chem. Concept Comput. 11, 2187–2198 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Artrith, N., Morawietz, T. & Behler, J. Excessive-dimensional neural-network potentials for multicomponent techniques: Functions to zinc oxide. Phys. Rev. B 83, 153101 (2011).

    Article 

    Google Scholar
     

  • Morawietz, T., Sharma, V. & Behler, J. A neural community potential-energy floor for the water dimer based mostly on environment-dependent atomic energies and costs. J. Chem. Phys. 136, 064103 (2012).

    Article 

    Google Scholar
     

  • Eckhoff, M. & Behler, J. Excessive-dimensional neural community potentials for magnetic techniques utilizing spin-dependent atom-centered symmetry capabilities. Npj Comput. Mater. 7, 170 (2021).

    Article 

    Google Scholar
     

  • Ghasemi, S. A., Hofstetter, A., Saha, S. & Goedecker, S. Interatomic potentials for ionic techniques with density useful accuracy based mostly on cost densities obtained by a neural community. Phys. Rev. B 92, 045131 (2015).

    Article 

    Google Scholar
     

  • Xie, X., Persson, Okay. A. & Small, D. W. Incorporating digital info into machine studying potential power surfaces through approaching the ground-state digital power as a perform of atom-based digital populations. J. Chem. Concept Comput. 16, 4256–4270 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J. A fourth-generation high-dimensional neural community potential with correct electrostatics together with non-local cost switch. Nat. Commun. 12, 398 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, with out the electrons. Phys. Rev. Lett. 104, 136403 (2010).

    Article 

    Google Scholar
     

  • Bartók, A. P., Kermode, J., Bernstein, N. & Csányi, G. Machine studying a general-purpose interatomic potential for silicon. Phys. Rev. X 8, 041048 (2018).


    Google Scholar
     

  • Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J. Spectral neighbor evaluation methodology for automated technology of quantum-accurate interatomic potentials. J. Comput. Phys. 285, 316–330 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Wooden, M. A. & Thompson, A. P. Extending the accuracy of the snap interatomic potential kind. J. Chem. Phys. 148, 241721 (2018).

    Article 

    Google Scholar
     

  • Shapeev, A. V. Second tensor potentials: a category of systematically improvable interatomic potentials. Multiscale Mannequin. Simul. 14, 1153–1173 (2016).

    Article 

    Google Scholar
     

  • Podryabinkin, E. V. & Shapeev, A. V. Energetic studying of linearly parametrized interatomic potentials. Comput. Mat. Sci. 140, 171–180 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Schütt, Okay. et al. SchNet: a continuous-filter convolutional neural community for modeling quantum interactions. Adv. Neural Inf. Course of. Syst. 30, 991–1001 (2017).


    Google Scholar
     

  • Barry, M. C., Smart, Okay. E., Kalidindi, S. R. & Kumar, S. Voxelized atomic construction potentials: predicting atomic forces with the accuracy of quantum mechanics utilizing convolutional neural networks. J. Phys. Chem. Lett. 11, 9093–9099 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Gasteiger, J., Becker, F. & Günnemann, S. Gemnet: Common directional graph neural networks for molecules. Adv. Neural Inf. Course of. Syst. 34, 6790–6802 (2021).


    Google Scholar
     

  • Batzner, S. et al. E (3)-equivariant graph neural networks for data-efficient and correct interatomic potentials. Nat. Commun. 13, 2453 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Zier, T., Zijlstra, E. S. & Garcia, M. E. Silicon earlier than the bonds break. Appl. Phys. A 117, 1–5 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Alfé, D. & Gillian, M. J. Alternate-correlation power and section diagram of Si. Phys. Rev. B 68, 205212 (2003).

    Article 

    Google Scholar
     

  • Yamaguchi, Okay. & Itagaki, Okay. Measurement of excessive temperature warmth content material of silicon by drop calorimetry. J. Therm. Anal. Calorim. 69, 1059–1066 (2002).

    Article 
    CAS 

    Google Scholar
     

  • Jayaraman, A., Klement, W. & Kennedy, G. C. Melting and polymorphism at excessive pressures in some teams iv parts and iii-v compounds with the diamond/zincblende construction. Phys. Rev. 130, 540–547 (1963).

    Article 
    CAS 

    Google Scholar
     

  • Dorner, F., Sukurma, Z., Dellago, C. & Kresse, G. Melting Si: past density useful principle. Phys. Rev. Lett. 121, 195701 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Pilania, G., Gubernatis, J. E. & Lookman, T. Multi-fidelity machine studying fashions for correct bandgap predictions of solids. Comput. Mater. Sci. 129, 156–163 (2018).

    Article 

    Google Scholar
     

  • Hendrycks, D. & Gimpel, Okay. Gaussian error linear items (GELUs). Preprint at https://arxiv.org/abs/1606.08415 (2016).

  • Clevert, D.-A., Unterthiner, T. & Hochreiter, S. Quick and correct deep community studying by exponential linear items (ELUs). Preprint at https://arxiv.org/abs/1511.07289 (2015).

  • Kingma, D. P. & Ba, J. Adam: a way for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).

  • Zijlstra, E. S., Huntemann, N., Kalitsov, A., Garcia, M. E. & Von Barth, U. Optimized Gaussian foundation units for Goedecker-Teter-Hutter pseudopotentials. Mannequin. Simul. Mater. Sci. Eng. 17, 015009 (2009).

    Article 

    Google Scholar
     

  • Zijlstra, E. S., Kalitsov, A., Zier, T. & Garcia, M. E. Fractional diffusion in silicon. Adv. Mater. 25, 5605–5608 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Waldecker, L. et al. Coherent and incoherent structural dynamics in laser-excited antimony. Phys. Rev. B 95, 054302 (2017).

    Article 

    Google Scholar
     

  • Zijlstra, E. S. et al. Femtosecond-laser-induced bond breaking and structural modifications in silicon, TiO2, and faulty graphene: an ab initio molecular dynamics research. Appl. Phys. A 114, 1–9 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Andersen, H. C. Molecular dynamics simulations at fixed stress and/or temperature. J. Chem. Phys. 72, 2384–2393 (1980).

    Article 
    CAS 

    Google Scholar
     

  • Anisimov, S. et al. Electron emission from steel surfaces uncovered to ultrashort laser pulses. Zh. Eksp. Teor. Fiz 66, 375–377 (1974).


    Google Scholar
     

  • Ivanov, D. S. & Zhigilei, L. V. Mixed atomistic-continuum modeling of short-pulse laser melting and disintegration of steel movies. Phys. Rev. B 68, 064114 (2003).

    Article 

    Google Scholar
     

  • Sadasivam, S., Chan, M. Okay. Y. & Darancet, P. Concept of thermal rest of electrons in semiconductors. Phys. Rev. Lett. 119, 136602 (2017).

    Article 

    Google Scholar
     

  • Wu, C. & Zhigilei, L. V. Microscopic mechanisms of laser spallation and ablation of steel targets from large-scale molecular dynamics simulations. Appl. Phys. A 114, 11–32 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Shih, C.-Y. et al. Two mechanisms of nanoparticle technology in picosecond laser ablation in liquids: the origin of the bimodal dimension distribution. Nanoscale 10, 6900–6910 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Ivanov, D. S. et al. Experimental and theoretical investigation of periodic nanostructuring of Au with ultrashort UV laser pulses close to the harm threshold. Phys. Rev. Appl. 4, 064006 (2015).

    Article 

    Google Scholar
     

  • Ivanov, D. S. et al. The mechanism of nanobump formation in femtosecond pulse laser nanostructuring of skinny steel movies. Appl. Phys. A 92, 791–796 (2008).

    Article 
    CAS 

    Google Scholar
     

  • Abadi, M. et al. Tensorflow: a system for large-scale machine studying. in twelfth USENIX Symposium on Working Techniques Design and Implementation 265–283 (2016).

  • [ad_2]

    LEAVE A REPLY

    Please enter your comment!
    Please enter your name here