Physical-Review-Letters_PRL.md

PRL (Physical Review Letters)

• Myint, P.C., Sterbentz, D.M., Brown, J.L., Stoltzfus, B.S., Delplanque, J.P.R. and Belof, J.L., 2023. Scaling law for the onset of solidification at extreme undercooling. Physical Review Letters, 131(10), p.106101.
  • "We utilize particle swarm optimization (PSO), which is a now widely used stochastic method."
    • Kennedy, J. & Eberhart, R. Particle swarm optimization. In IEEE International Conference on Neural Networks, 1942–1948 (Perth, Australia, 1995).
    • Myint, P. C., Benedict, L. X., Wu, C. J. & Belof, J. L. Minimization of Gibbs energy in high-pressure multiphase, multicomponent mixtures through particle swarm optimization. ACS Omega 6, 13341–13364 (2021).
• Thamm, M. and Rosenow, B., 2023. Machine learning optimization of Majorana hybrid nanowires. Physical Review Letters, 130(11), p.116202. [ www ] ( CMA-ES | Continuous Optimization )
• Yang, X., Li, J., Ding, Y., Xu, M., Zhu, X.F. and Zhu, J., 2022. Observation of transient parity-time symmetry in electronic systems. Physical Review Letters, 128(6), p.065701. [ www | Editors' Suggestion ] ( PSO | Continuous Optimization )
  • "The advanced particle swarm optimization (PSO) algorithm is used to accurately fit measured results of the free oscillating currents in the primary oscillator to yield the loss rates and the frequencies, which is difficult to be observed directly from the raw experimental results. The indicators identified by PSO are beneficial to reveal and reflect the characteristic of transient PT symmetry."
    • J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of the IEEE International Conference on Neural Networks (IEEE, Perth, 1995), p. 1942.
    • Y. Shi and R. Eberhart, A modified particle swarm optimizer, in Proceedings of the IEEE International Conference on Evolutionary Computation (IEEE, Anchorage, 1998), p. 69.
• Guan, P.W., Sun, Y., Hemley, R.J., Liu, H., Ma, Y. and Viswanathan, V., 2022. Low-pressure electrochemical synthesis of complex high-pressure superconducting superhydrides. Physical Review Letters, 128(18), p.186001. [ www ] ( PSO | Continuous Optimization )
  • "The crystal structures were identified by the particle swarm optimization using the CALYPSO code, with part of them already obtained in a previous Letter and additional structures reported here."
    • Y. Sun, J. Lv, Y. Xie, H. Liu, and Y. Ma, Phys. Rev. Lett. 123, 097001 (2019).
• Chen, B., Tian, M., Zhang, J., Li, B., Xiao, Y., Chow, P., Kenney-Benson, C., Deng, H., Zhang, J., Sereika, R. and Yin, X., 2022. Novel valence transition in elemental metal europium around 80 GPa. Physical Review Letters, 129(1), p.016401. [ www ] ( PSO | Continuous Optimization )
  • "Our structure search is based on a global optimization of potential-energy surfaces using the CALYPSO methodology, which has been successfully employed in predicting a large variety of crystal structures. Evolutionary calculations were performed at 20, 50, 100, and 150 GPa with 1, 2, 3, 4, and 8 formula units per cell, retaining 60% of lowest-enthalpy structures to produce the next-generation structures by a particle swarm optimization procedure and generating the remaining 40% structures randomly within the symmetry constraint."
• Lu, W., Liu, S., Liu, G., Hao, K., Zhou, M., Gao, P., Wang, H., Lv, J., Gou, H., Yang, G. and Wang, Y., 2022. Disproportionation of SO 2 at high pressure and temperature. Physical Review Letters, 128(10), p.106001. [ www ] ( PSO | Continuous Optimization )
  • "our CALYPSO structure-searching simulation is more stable than the ambient-pressure α phase of SO3 at the pressure of 72 GPa."
• Singh, N. and van Hecke, M., 2021. Design of pseudo-mechanisms and multistable units for mechanical metamaterials. Physical Review Letters, 126(24), p.248002. [ www ] ( PSO | Continuous Optimization )
  • "Evolutionary algorithms are eminently suited for this, and we choose here to use particle swarm optimization (PSO) due to its simplicity and ease of tuning."
  • "We suggest that these solutions perhaps are close to a shallow local minimum, and note that PSO is not guaranteed to find local minima with high accuracy."
    • J. Kennedy and R. Eberhart, Proc. IEEE Int. Conf. Neural Networks 4, 1942 (1995).
    • R. Poli, J. Kennedy, and T. Blackwell, Swarm Intell. 1, 33 (2007).
    • R. C. Eberhart and Y. H. Shi, IEEE Trans. Evol. Comput. 8, 201 (2004).
• Liu, Z., Zhuang, Q., Tian, F., Duan, D., Song, H., Zhang, Z., Li, F., Li, H., Li, D. and Cui, T., 2021. Proposed superconducting electride Li6C by sp-hybridized cage states at moderate pressures. Physical Review Letters, 127(15), p.157002. [ www ] ( PSO | Continuous Optimization )
  • "We predict a new energy-preferred anti-CdCl2-II phase after a bidirectional structure search via the swarm intelligence-based methodology (CALYPSO) and the evolutionary algorithm (USPEX)."
• Vinko, S.M., Vozda, V., Andreasson, J., Bajt, S., Bielecki, J., Burian, T., Chalupsky, J., Ciricosta, O., Desjarlais, M.P., Fleckenstein, H. and Hajdu, J., 2020. Time-resolved XUV opacity measurements of warm dense aluminum. Physical Review Letters, 124(22), p.225002. [ www ] ( CMA-ES | Continuous Optimization )
  • "We start by finding the best-fit solution using the stochastic CMA-ES optimization algorithm, and use it as a starting point for the ensemble MCMC."
    • N. Hansen, arXiv:1604.00772.
• Wintermantel, T.M., Wang, Y., Lochead, G., Shevate, S., Brennen, G.K. and Whitlock, S., 2020. Unitary and nonunitary quantum cellular automata with Rydberg arrays. Physical Review Letters, 124(7), p.070503. [ www ] ( PSO | Continuous Optimization )
  • "We maximize ... using the particle swarm optimization (PSO) algorithm."
  • J. Kennedy, Particle swarm optimization, in Encyclopedia of Machine Learning, edited by C. Sammut and G. I. Webb (Springer, Boston, 2010).
• Leditzky, F., Leung, D. and Smith, G., 2018. Dephrasure channel and superadditivity of coherent information. Physical Review Letters, 121(16), p.160501. [ www ] ( PSO | Continuous Optimization )
  • "The coherent information of a quantum channel has many such local extrema, as any pure input state yields zero coherent information. This suggests the use of gradient-free optimization algorithms such as particle swarm optimization to find improved local (or even global) extrema of the coherent information. Using particle swarm optimization, we were able to find improved quantum codes for the dephrasure channel that yield higher rates than the simple repetition code ρn (albeit with a lower threshold)."
    • J. Kennedy and R. Eberhart, in Proceedings of IEEE International Conference on Neural Networks, Vol. 4 (1995) pp. 1942–1948.
• Zhang, J., Lv, J., Li, H., Feng, X., Lu, C., Redfern, S.A., Liu, H., Chen, C. and Ma, Y., 2018. Rare helium-bearing compound FeO 2 He stabilized at deep-earth conditions. Physical Review Letters, 121(25), p.255703. [ www | Editors' Suggestion | Featured in Physics ] ( PSO | Continuous Optimization )
  • "The present structure search is based on a global optimization of free-energy surfaces using the CALYPSO methodology, which has been successfully employed in predicting a large variety of crystal structures. Evolutionary variable-cell calculations were performed at 100, 200, and 300 GPa with 1, 2, 3, 4, and 8 FeO2He formula units (f.u.) per cell, retaining 60% lowestenthalpy structures to produce the next-generation structures by a particle swarm optimization procedure and generating the remaining 40% structures randomly within the symmetry constraint. Most searches converge in 30 to 40 generations with about 1000 structures generated."
• Zhu, Z., Cai, X., Yi, S., Chen, J., Dai, Y., Niu, C., Guo, Z., Xie, M., Liu, F., Cho, J.H. and Jia, Y., 2017. Multivalency-driven formation of Te-based monolayer materials: A combined first-principles and experimental study. Physical Review Letters, 119(10), p.106101. [ www ] ( PSO | Continuous Optimization )
  • "Using the particle-swarm optimization method in combination with first-principles density functional theory calculations, here we predict a new category of 2D monolayers named tellurene, composed of the metalloid element Te, with stable 1T-MoS2-like, and metastable tetragonal and 2H-MoS2-like structures."
• Choi, H., Heuck, M. and Englund, D., 2017. Self-similar nanocavity design with ultrasmall mode volume for single-photon nonlinearities. Physical Review Letters, 118(22), p.223605. [ www | Featured in Physics ] ( PSO | Continuous Optimization )
  • "We performed these radiation loss minimizations by three-dimensional FDTD, using particle swarm optimization for the length of the slot, the lattice constant, and the positions and radii of the holes symmetrically about the cavity center."
• Peng, F., Sun, Y., Pickard, C.J., Needs, R.J., Wu, Q. and Ma, Y., 2017. Hydrogen clathrate structures in rare earth hydrides at high pressures: Possible route to room-temperature superconductivity. Physical Review Letters, 119(10), p.107001. [ www | Editors' Suggestion ] ( PSO | Continuous Optimization )
  • "Here we report an extensive exploration of the highpressure phase diagrams of RE (Sc, Y, La, Ce, Pr, etc.) hydrides, focusing on H-rich species by performing swarm-intelligence based CALYPSO structure searches."
• Zwicker, D., Murugan, A. and Brenner, M.P., 2016. Receptor arrays optimized for natural odor statistics. Proceedings of the National Academy of Sciences, 113(20), pp.5570-5575.
  • "Because of the stochastic nature of Monte Carlo sampling, the calculated I is not exact. Consequently, we use the stochastic, derivative-free numerical optimization method covariance matrix adaptation evolution strategy (CMA-ES) (47) to optimize the sensitivity matrix Sni with respect to I to produce Fig. 3B."
    • "N Hansen, The CMA evolution strategy: A comparing review. Towards a New Evolutionary Computation, eds JA Lozano, P Larrañaga, I Inza, E Bengoetxea (Springer, New York), pp. 75–102 (2006)."
• Zhang, M., Liu, H., Li, Q., Gao, B., Wang, Y., Li, H., Chen, C. and Ma, Y., 2015. Superhard BC 3 in cubic diamond structure. Physical Review Letters, 114(1), p.015502. [ www ] ( PSO | Continuous Optimization )
  • "We solve the crystal structure of recently synthesized cubic BC3 using an unbiased swarm structure search, which identifies a highly symmetric BC3 phase in the cubic diamond structure (d-BC3) that contains a distinct B-B bonding network along the body diagonals of a large 64-atom unit cell."
• Li, Y., Hao, J., Liu, H., Lu, S. and John, S.T., 2015. High-energy density and superhard nitrogen-rich BN compounds. Physical Review Letters, 115(10), p.105502. [ www ] ( PSO | Continuous Optimization )
  • "Structure predictions are performed using the particle swarm optimization technique implemented in the CALYPSO code."
• Li, Q., Zhou, D., Zheng, W., Ma, Y. and Chen, C., 2013. Global structural optimization of tungsten borides. Physical Review Letters, 110(13), p.136403. [ www ] ( PSO | Continuous Optimization )
  • "Our global structural optimization used the CALYPSO code with a variable-cell particle-swarm optimization algorithm, which has successfully predicted structures of various systems ranging from elemental solids to binary and ternary compounds."
• Lv, J., Wang, Y., Zhu, L. and Ma, Y., 2011. Predicted novel high-pressure phases of lithium. Physical Review Letters, 106(1), p.015503. [ www ] ( PSO | Continuous Optimization )
  • "We here report two unexpected orthorhombic high-pressure structures Aba2-40 and Cmca-56, by using a newly developed particle swarm optimization technique on crystal structure prediction."
• Zhao, Z., Xu, B., Zhou, X.F., Wang, L.M., Wen, B., He, J., Liu, Z., Wang, H.T. and Tian, Y., 2011. Novel superhard carbon: C-centered orthorhombic C8. Physical Review Letters, 107(21), p.215502. [ www ] ( PSO | Continuous Optimization )
  • "A novel carbon allotrope of C-centered orthorhombic C8 is predicted by using a recently developed particle-swarm optimization method on structural search."
• Zhu, L., Wang, H., Wang, Y., Lv, J., Ma, Y., Cui, Q., Ma, Y. and Zou, G., 2011. Substitutional alloy of Bi and Te at high pressure. Physical Review Letters, 106(14), p.145501. [ www ] ( PSO | Continuous Optimization )
  • "Here, we have solved the two long-puzzling low high-pressure phases as seven- and eightfold monoclinic structures, respectively, through particle-swarm optimization technique on crystal structure prediction."
• Hentschel, A. and Sanders, B.C., 2010. Machine learning for precise quantum measurement. Physical Review Letters, 104(6), p.063603. [ www ] ( PSO | Continuous Optimization )
  • "Here we show that PSO algorithms also deliver automated approaches to devising successful quantum measurement policies for implementation in the PU."
  • "Rerunning the PSO algorithm increases the developmental cost for the policies but does not affect their operational cost."
    • J. Kennedy and W. M. Spears, in Proceedings of the IEEE Congress on Evolutionary Computation, Anchorage, Alaska, 1998 (IEEE, New York, 1998), pp. 78–83.
    • J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence (Morgan Kaufmann, San Francisco, 2001).
• Whitelam, S. and Tamblyn, I., 2021. Neuroevolutionary learning of particles and protocols for self-assembly. Physical Review Letters, 127(1), p.018003. [ www ] ( NE )
• Gagnon, D., Fillion-Gourdeau, F., Dumont, J., Lefebvre, C. and MacLean, S., 2017. Suppression of multiphoton resonances in driven quantum systems via pulse shape optimization. Physical Review Letters, 119(5), p.053203. [ www ] ( DE | Parallel )
• Las Heras, U., Alvarez-Rodriguez, U., Solano, E. and Sanz, M., 2016. Genetic algorithms for digital quantum simulations. Physical Review Letters, 116(23), p.230504. [ www ] ( GA )
• Lovett, N.B., Crosnier, C., Perarnau-Llobet, M. and Sanders, B.C., 2013. Differential evolution for many-particle adaptive quantum metrology. Physical Review Letters, 110(22), p.220501. [ www ] ( DE )
• d’Avezac, M., Luo, J.W., Chanier, T. and Zunger, A., 2012. Genetic-algorithm discovery of a direct-gap and optically allowed superstructure from indirect-gap Si and Ge semiconductors. Physical Review Letters, 108(2), p.027401. [ www ] ( GA )
• Aaltonen, T., Adelman, J., Akimoto, T., Albrow, M.G., Gonzalez, B.A., Amerio, S., Amidei, D., Anastassov, A., Annovi, A., Antos, J. and Apollinari, G., 2009. Measurement of the top-quark mass with dilepton events selected using neuroevolution at CDF. Physical Review Letters, 102(15), p.152001. [ www ] ( NE )
• Dudiy, S.V. and Zunger, A., 2006. Searching for alloy configurations with target physical properties: impurity design via a genetic algorithm inverse band structure approach. Physical Review Letters, 97(4), p.046401. [ www ] ( GA )
• Johannesson, G.H., Bligaard, T., Ruban, A.V., Skriver, H.L., Jacobsen, K.W. and Nørskov, J.K., 2002. Combined electronic structure and evolutionary search approach to materials design. Physical Review Letters, 88(25), p.255506. [ www ] ( GA )
• Deaven, D.M. and Ho, K.M., 1995. Molecular geometry optimization with a genetic algorithm. Physical Review Letters, 75(2), p.288. [ www ] ( GA )
• Rata, I., Shvartsburg, A.A., Horoi, M., Frauenheim, T., Siu, K.M. and Jackson, K.A., 2000. Single-parent evolution algorithm and the optimization of Si clusters. Physical Review Letters, 85(3), p.546. [ www ] ( GA )
• López, C., Álvarez, A. and Hernández-García, E., 2000. Forecasting confined spatiotemporal chaos with genetic algorithms. Physical Review Letters, 85(11), p.2300. [ www ] ( GA )
• Prügel-Bennett, A. and Shapiro, J.L., 1994. Analysis of genetic algorithms using statistical mechanics. Physical Review Letters, 72(9), p.1305. [ www ] ( GA )
• Judson, R.S. and Rabitz, H., 1992. Teaching lasers to control molecules. Physical Review Letters, 68(10), p.1500. [ www ] ( GA )
• Grest, G.S., Soukoulis, C.M. and Levin, K., 1986. Cooling-rate dependence for the spin-glass ground-state energy: Implications for optimization by simulated annealing. Physical Review Letters, 56(11), p.1148. [ www ] ( SA )
• Zhao, X., Nguyen, M. C., Zhang, W. Y., Wang, C. Z., Kramer, M. J., Sellmyer, D. J., ... & Ho, K. M. (2014). Exploring the structural complexity of intermetallic compounds by an adaptive genetic algorithm. Physical review letters, 112(4), 045502. ( GA )
• Vilhelmsen, L. B., & Hammer, B. (2012). Systematic study of Au 6 to Au 12 gold clusters on MgO (100) F centers using density-functional theory. Physical review letters, 108(12), 126101. ( GA )
• Hensley, C. J., Yang, J., & Centurion, M. (2012). Imaging of isolated molecules with ultrafast electron pulses. Physical review letters, 109(13), 133202. ( GA )
• Bhattacharya, S., Levchenko, S. V., Ghiringhelli, L. M., & Scheffler, M. (2013). Stability and metastability of clusters in a reactive atmosphere: theoretical evidence for unexpected stoichiometries of Mg M O x. Physical review letters, 111(13), 135501.
• Fux, G. E., Butler, E. P., Eastham, P. R., Lovett, B. W., & Keeling, J. (2021). Efficient exploration of Hamiltonian parameter space for optimal control of non-Markovian open quantum systems. Physical Review Letters, 126(20), 200401. ( DE )
• Feichtner, T., Selig, O., Kiunke, M., & Hecht, B. (2012). Evolutionary optimization of optical antennas. Physical review letters, 109(12), 127701. ( EA )
• Pacheco, J. M., Traulsen, A., & Nowak, M. A. (2006). Coevolution of strategy and structure in complex networks with dynamical linking. Physical review letters, 97(25), 258103. ( ES )
• Campos, P. R., De Oliveira, V. M., Giro, R., & Galvao, D. S. (2004). Emergence of prime numbers as the result of evolutionary strategy. Physical review letters, 93(9), 098107. ( ES )
• Melnikov, A. A., Sekatski, P., & Sangouard, N. (2020). Setting up experimental bell tests with reinforcement learning. Physical review letters, 125(16), 160401. ( SA )