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Multimuons in cosmic-ray events as seen in ALICE at the LHC

Journal of Cosmology and Astroparticle physics 2025年第4期2025卷 009-009页

作者： Acharya, S. Agarwal, A. Aglieri Rinella, G. Aglietta, L. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Akishina, V. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altamura, A.R. Altsybeev, I. Alvarado, J.R. Alvarez, C.O.R. Anaam, M.N. Andrei, C. Andreou, N. Andronic, A. Andronov, E. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Averyanov, D. Azmi, M.D. Baba, H. Badalà, A. Bae, J. Bae, Y. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Bala, R. Balbino, A. Baldisseri, A. Balis, B. Banoo, Z. Barbasova, V. Barile, F. Barioglio, L. Barlou, M. Barman, B. Barnaföldi, G.G. Barnby, L.S. Barreau, E. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J.L. Bearden, I.G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A.D.C. Bellini, F. Bellwied, R. Belokurova, S. Beltran, L.G.E. Beltran, Y.A.V. Bencedi, G. Bensaoula, A. Beole, S. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M.G. Betev, L. Bhaduri, P.P. Bhasin, A. Bhattacharjee, B. Bianchi, L. Bielčík, J. Bielčíková, J. Bigot, A.P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Bock, F. Bodova, T. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Borquez Carcamo, A.G. Botta, E. Bouziani, Y.E.M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buchakchiev, V.D. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Burmasov, N. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Cabanillas Noris, J.C. Cabrera, M.F.T. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J.M.M. Camerini, P. Canedo, F.D.M. Cantway, S.L. Carabas, M. Carballo, A.A. Carnesecchi, F. Caron, R. Carvalho, L.A.D. Castillo Castel A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation Yerevan Armenia AGH University of Krakow Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Bose Institute Department of Physics Centre for Astroparticle Physics and Space Science (CAPSS) Kolkata India California Polytechnic State University San Luis Obispo CA United States Central China Normal University Wuhan China Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN) Havana Cuba Centro de Investigación y de Estudios Avanzados (CINVESTAV) Mérida Mexico City Mexico Chicago State University Chicago IL United States China Institute of Atomic Energy Beijing China China University of Geosciences Wuhan China Chungbuk National University Cheongju South Korea Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia Creighton University Omaha NE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan South Korea Department of Physics Sejong University Seoul South Korea Department of Physics University of California Berkeley CA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell’Università Sezione INFN Cagliari Italy Dipartimento di Fisica dell’Università Sezione INFN Trieste Italy Dipartimento di Fisica dell’Università Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell’Università Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell’Università Sezione INFN Padova Italy Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università Gruppo Collegato INFN Salerno Italy Dipartimento DISAT del Politecnico Sezione

ALICE is a large experiment at the CERN Large Hadron Collider. Located 52 meters underground, its detectors are suitable to measure muons produced by cosmic-ray interactions in the atmosphere. In this paper, the studies of the cosmic muons registered by ALICE during Run 2 (2015–2018) are described. The analysis is limited to multimuon events defined as events with more than four detected muons (N_µ > 4) and in the zenith angle range 0^◦ < θ < 50^◦. The results are compared with Monte Carlo simulations using three of the main hadronic interaction models describing the air shower development in the atmosphere: QGSJET-II-04, EPOS-LHC, and SIBYLL 2.3d. The interval of the primary cosmic-ray energy involved in the measured muon multiplicity distribution is about 4×10¹⁵ < E_prim < 6×10¹⁶ eV. In this interval none of the three models is able to describe precisely the trend of the composition of cosmic rays as the energy increases. However, QGSJET-II-04 is found to be the only model capable of reproducing reasonably well the muon multiplicity distribution, assuming a heavy composition of the primary cosmic rays over the whole energy range, while SIBYLL 2.3d and EPOS-LHC underpredict the number of muons in a large interval of multiplicity by more than 20% and 30%, respectively. The rate of high muon multiplicity events (N_µ > 100) obtained with QGSJET-II-04 and SIBYLL 2.3d is compatible with the data, while EPOS-LHC produces a significantly lower rate (55% of the measured rate). For both QGSJET-II-04 and SIBYLL 2.3d, the rate is close to the data when the composition is assumed to be dominated by heavy elements, an outcome compatible with the average energy E_prim ∼ 10¹⁷ eV of these events. This result places significant constraints on more exotic production mechanisms. © 2025 The Author(s).

关键词： cosmic ray experiments cosmic rays detectors

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Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics

arXiv

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arXiv 2021年

作者： Wang, Dongdong Zhang, Linfeng Wang, Yanze Chang, Junhan Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States DP Technology Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China School of Mathematical Sciences Peking University Beijing China Department of Mathematics Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time, they have long faced a number of issues such as the inefficiency when dealing with a large number of collective variables (CVs) or systems with high free energy barriers. In this work, we show that with the clustering and adaptive tuning techniques, the reinforced dynamics (RiD) scheme can be used to efficiently explore the configuration space and free energy landscapes with a large number of CVs or systems with high free energy barriers. We illustrate this by studying various representative and challenging examples. Firstly we demonstrate the efficiency of adaptive RiD compared with other methods, and construct the 9-dimensional free energy landscape of peptoid trimer which has energy barriers of more than 8 kcal/mol. We then study the folding of the protein chignolin using 18 CVs. In this case, both the folding and unfolding rates are observed to be equal to 4.30 µs−1. Finally, we propose a protein structure refinement protocol based on RiD. This protocol allows us to efficiently employ more than 100 CVs for exploring the landscape of protein structures and it gives rise to an overall improvement of 14.6 units over the initial Global Distance Test-High Accuracy (GDT-HA) score. © 2021, CC BY.

关键词： Wave functions

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DeePCG: Constructing coarse-grained models via deep neural networks

arXiv

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arXiv 2018年

作者： Weinan, E. Zhang, Linfeng Han, Jiequn Wang, Han Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Caep Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton Institute for Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- A nd/or three-body contributions. The scheme, called Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Proton emission in ultraperipheral Pb-Pb collisions at sNN=5.02 TeV

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Physical Review C 2025年第5期111卷 054906-054906页

作者： S. Acharya A. Agarwal G. Aglieri Rinella L. Aglietta M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov V. Akishina M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt A. R. Altamura I. Altsybeev J. R. Alvarado C. O. R. Alvarez M. N. Anaam C. Andrei N. Andreou A. Andronic E. Andronov V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck D. Averyanov M. D. Azmi H. Baba A. Badalà J. Bae Y. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung R. Bala A. Baldisseri B. Balis Z. Banoo V. Barbasova F. Barile L. Barioglio M. Barlou B. Barman G. G. Barnaföldi L. S. Barnby E. Barreau V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova L. G. E. Beltran Y. A. V. Beltran G. Bencedi A. Bensaoula S. Beole Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin B. Bhattacharjee L. Bianchi J. Bielčík J. Bielčíková A. P. Bigot A. Bilandzic A. Binoy G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume F. Bock T. Bodova J. Bok L. Boldizsár M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo E. Botta Y. E. M. Bouziani D. C. Brandibur L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno V. D. Buchakchiev M. D. Buckland D. Budnikov H. Buesching S. Bufalino P. Buhler N. Burmasov Z. Buthelezi A. Bylinkin S. A. Bysiak J. C. Cabanillas Noris M. F. T. Cabrera H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo S. L. Cantway M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos M. Castoldi F. Catalano S. Cattaruzzi R. Cerri I. Chakaberia P. Chakraborty S. Chandra Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento di Fisica dell'Università and Sezione INFN Turin Italy Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell'Università and Sezione INFN Bologna Italy Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Affiliated with an institute covered by a cooperation agreement with CERN Frankfurt Institute for Advanced Studies Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico University of Houston Houston Texas USA Sungkyunkwan University Suwon City Republic of Korea Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany INFN Sezione di Bari Bari Italy Physik Department Technische Universität München Munich Germany High Energy Physics Group Universidad Autónoma de Puebla Puebla Mexico Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania University of Derby Derby United Kingdom Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy INFN Sezione di Bologna Bologna Italy Lawrence Berkeley National Laboratory Berkeley California USA SUBATECH I

The first measurements of proton emission accompanied by neutron emission in the electromagnetic dissociation (EMD) of Pb208 nuclei in the ALICE experiment at the Large Hadron Collider are presented. The EMD protons and neutrons emitted at very forward rapidities are detected by the proton and neutron zero degree calorimeters of the ALICE experiment. The emission cross sections of zero, one, two, and three protons accompanied by at least one neutron were measured in ultraperipheral Pb208−Pb208 collisions at a center-of-mass energy per nucleon pair sNN=5.02TeV. The 0p and 3p cross sections are described by the RELDIS model within their measurement uncertainties, while the 1p and 2p cross sections are underestimated by the model by 17–25%. According to this model, these 0p, 1p, 2p, and 3p cross sections are associated, respectively, with the production of various isotopes of Pb, Tl, Hg, and Au in the EMD of Pb208. The cross sections of the emission of a single proton accompanied by the emission of one, two, or three neutrons in EMD were also measured. The data are significantly overestimated by the RELDIS model, which predicts that the (1p,1n), (1p,2n), and (1p,3n) cross sections are very similar to the cross sections for the production of the thallium isotopes Tl206,205,204.

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First polarisation measurement of coherently photoproduced J/ψ in ultra-peripheral Pb–Pb collisions at s_NN=5.02 TeV

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physics Letters, Section B: Nuclear, Elementary Particle and High-Energy physics 2025年 865卷

作者： Acharya, S. Adamová, D. Adler, A. Aglieri Rinella, G. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altsybeev, I. Alvarado, J.R. Anaam, M.N. Andrei, C. Andronic, A. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Aresti, M. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Azmi, M.D. Badalà, A. Bae, J. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Balbino, A. Baldisseri, A. Balis, B. Banerjee, D. Banoo, Z. Barbera, R. Barile, F. Barioglio, L. Barlou, M. Barnaföldi, G.G. Barnby, L.S. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J.L. Bearden, I.G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A.D.C. Bellini, F. Bellwied, R. Belokurova, S. Belyaev, V. Bencedi, G. Beole, S. Bercuci, A. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M.G. Betev, L. Bhaduri, P.P. Bhasin, A. Bhat, M.A. Bhattacharjee, B. Bianchi, L. Bianchi, N. Bielčík, J. Bielčíková, J. Biernat, J. Bigot, A.P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Boca, G. Bock, F. Bodova, T. Bogdanov, A. Boi, S. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Borquez Carcamo, A.G. Bossi, H. Botta, E. Bouziani, Y.E.M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Cai, M. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J.M.M. Camerini, P. Canedo, F.D.M. Cantway, S.L. Carabas, M. Carballo, A.A. Carnesecchi, F. Caron, R. Carvalho, L.A.D. Castillo Castellanos, J. Catalano, F. Ceballos Sanchez, C. Chakaberia, I. Chakraborty, P. Chandra, S. Chapeland, S. Chartier, M. Chattopa A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation Yerevan Armenia AGH University of Krakow Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Bose Institute Department of Physics Centre for Astroparticle Physics and Space Science (CAPSS) Kolkata India California Polytechnic State University San Luis Obispo CA United States Central China Normal University Wuhan China Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN) Havana Cuba Centro de Investigación y de Estudios Avanzados (CINVESTAV) Mexico City and Mérida Mexico Chicago State University Chicago IL United States China Institute of Atomic Energy Beijing China Chungbuk National University Cheongju South Korea Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia COMSATS University Islamabad Islamabad Pakistan Creighton University Omaha NE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan South Korea Department of Physics Sejong University Seoul South Korea Department of Physics University of California Berkeley CA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell'Università Sezione INFN Cagliari Italy Dipartimento di Fisica dell'Università Sezione INFN Trieste Italy Dipartimento di Fisica dell'Università Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Padova Italy Dipartimento di Fisica ‘E.R. Caianiello’ dell'Università Gruppo Collegato INFN Salerno Italy Dipartimento DISAT del Politecnico

The first measurement of the polarisation of coherently photoproduced J/ψ mesons in ultra-peripheral Pb–Pb collisions, using data at sNN=5.02 TeV, is presented. The J/ψ meson is measured via its dimuon decay channe...

The first measurement of the polarisation of coherently photoproduced J/ψ mesons in ultra-peripheral Pb–Pb collisions, using data at s_NN=5.02 TeV, is presented. The J/ψ meson is measured via its dimuon decay channel in the forward rapidity interval −4.02.5 using the ALICE detector at the CERN LHC. An event sample corresponding to an integrated luminosity of 750 μb⁻¹ ± 5% (syst) is analysed. Hadronic activity is highly suppressed since the interaction is mediated by a photon. The polar and azimuthal angle distributions of the decay muons are measured, and the polarisation parameters λ_θ, λ_φ, λ_θφ are extracted. The analysis is carried out in the helicity frame. The results are found to be consistent with a transversely polarised J/ψ. These values are compared with previous measurements by the H1 and ZEUS experiments. The polarisation parameters of coherent J/ψ photoproduction in Pb–Pb collisions are found to be consistent with the s-channel helicity conservation hypothesis. © 2025 CERN for the benefit of the ALICE Collaboration

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A deep potential model with long-range electrostatic interactions

arXiv

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arXiv 2021年

作者： Zhang, Linfeng Wang, Han Muniz, Maria Carolina Panagiotopoulos, Athanassios Z. Car, Roberto Weinan, E. DP Technology Beijing China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make possible molecular simulations with the accuracy of quantum mechanical density functional theory, at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei+core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the deep potential long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples, the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal. © 2021, CC BY.

关键词： Electrostatics

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Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

arXiv

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arXiv 2020年

作者： Huang, Jianxing Zhang, Linfeng Wang, Han Zhao, Jinbao Cheng, Jun Weinan, E. State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Department of Mathematics Princeton University PrincetonNJ08544 United States

Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here we implement the Deep Potential Generator (DP-GEN) to set up an efficient protocol to automatically generate interatomic potentials for Li10GeP2S12-type solid-state electrolyte materials (Li10GeP2S12, Li10SiP2S12 and Li10SnP2S12). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of diffusion process to a wide temperature range (300K-1000K) and systems with large size (~1000 atoms). Important technical aspects like the statistical error and size effect are carefully investigated and benchmark tests including the effect of density functional, thermal expansion and configurational disorder are performed. The computed data that considering these factors agrees well with the experimental results and we find that the 3 structures shows different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Solid electrolytes

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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

arXiv

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arXiv 2017年

作者： Wang, Han Zhang, Linfeng Han, Jiequn Weinan, E. Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing100871 China

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model. Copyright © 2017, The Authors. All rights reserved.

关键词： Potential energy

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Modular scintillation cameras: A progress report

Modular scintillation cameras: A progress report

引用

Medical Imaging II 1988

作者： Aarsvold, J.N. Barrett, H.H. Chen, J. Landesman, A.L. Milster, T.D. Patton, D.D. Roney, T.J. Rowe, R.K. Seacat, R.H. Strimbu, L.M. University of Arizona Program in Applied Mathematics TucsonAZ85721 United States University of Arizona Optical Sciences Center TucsonAZ85721 United States University of Arizona Department of Radiology University Medical Center TucsonAZ85724 United States University of Arizona Department of Physics TucsonAZ85721 United States IBM Corporation General Products Division Department 78G/21-2 TucsonAZ85744 United States University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Modular scintillation cameras are gamma cameras with relatively small crystal faces, a small number of photomultiplier tubes (PMTs), and independent processing electronics. Our prototypical module has a 10 cm square crystal face, four PMTs, and digital processing electronics. Scintillation event information is transferred to images by mapping digitized PMT response combinations to optimal position estimates of event locations. In our prototype, a look-up table is used to perform this mapping. To encode scintillation event information more effectively, we use nonlinear compression of each of the PMT signals. Also introduced are logarithmic matched filtering and likelihood windowing, two processing techniques that result from exploitations of the Poisson model of the distribution of photopeak events. Logarithmic matched filtering is a method of obtaining estimates of mean detector response functions having greater accuracy than that indicated by the digitization of the PMT responses. Likelihood windowing is the utilization of a likelihood threshold, rather than the familiar energy window, as a discriminant of photopeak and scatter events. We have implemented each of the above on our prototypical module. Performance characteristics of this module include energy resolution of 10% full width at half maximum (FWHM) at 140 keV and spatial resolution of better than 4mm FWHM over 90% of the crystal. © 1988 SPIE. All rights reserved.

关键词： Photomultipliers

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Erratum: “Poisson stochastic master equation unravelings and the measurement problem: A quantum stochastic calculus perspective” [J. Math. Phys. 61, 032101 (2020)]

引用

Journal of Mathematical physics 2022年第10期63卷

作者： Dustin Keys Jan Wehr 1Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA 2Department of Mathematics and Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA

The article was published in March of 2020.1 There is an error in the Hudson–Parthasarathy equation, first appearing as Eq. (15). The equation should read

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