Journal of the Chinese Ceramic Society, Volume. 53, Issue 7, 1920(2025)
Defect Regulation of Ionic Conductivity in Li6PS5Cl Based on a Large-Atom Model for Solid-State Electrolytes
[1] [1] GOODENOUGH J B, PARK K S. The Li-ion rechargeable battery: A perspective[J]. J Am Chem Soc, 2013, 135(4): 1167–1176.
[2] [2] TARASCON J M. Is lithium the new gold?[J]. Nature Chem, 2010, 2(6): 510.
[3] [3] TARASCON J M, ARMAND M. Issues and challenges facing rechargeable lithium batteries[J]. Nature, 2001, 414(6861): 359–367.
[4] [4] DUNN B, KAMATH H, TARASCON J M. Electrical energy storage for the grid: A battery of choices[J]. Science, 2011, 334(6058): 928–935.
[5] [5] WACHSMAN E D, LEE K T. Lowering the temperature of solid oxide fuel cells[J]. Science, 2011, 334(6058): 935–939.
[6] [6] MANTHIRAM A. An outlook on lithium ion battery technology[J]. ACS Cent Sci, 2017, 3(10): 1063–1069.
[7] [7] TAKADA K. Progress and prospective of solid-state lithium batteries[J]. Acta Mater, 2013, 61(3): 759–770.
[8] [8] JANEK J, ZEIER W G. A solid future for battery development[J]. Nat Energy, 2016, 1(9): 16141.
[9] [9] WANG S, FU J M, LIU Y S, et al. Design principles for sodium superionic conductors[J]. Nat Commun, 2023, 14(1): 7615.
[10] [10] KAMAYA N, HOMMA K, YAMAKAWA Y, et al. A lithium superionic conductor[J]. Nat Mater, 2011, 10(9): 682–686.
[11] [11] KATO Y, HORI S, SAITO T, et al. High-power all-solid-state batteries using sulfide superionic conductors[J]. Nat Energy, 2016, 1(4): 16030.
[12] [12] DEISEROTH H J, KONG S T, ECKERT H, et al. Li6PS5X: A class of crystalline Li-rich solids with an unusually high Li+ mobility[J]. Angew Chem Int Ed, 2008, 47(4): 755–758.
[13] [13] XUAN M J, XIAO W D, XU H J, et al. Ultrafast solid-state lithium ion conductor through alloying induced lattice softening of Li6PS5Cl[J]. J Mater Chem A, 2018, 6(39): 19231–19240.
[14] [14] YU C, GANAPATHY S, HAGEMAN J, et al. Facile synthesis toward the optimal structure-conductivity characteristics of the argyrodite Li6PS5Cl solid-state electrolyte[J]. ACS Appl Mater Interfaces, 2018, 10(39): 33296–33306.
[15] [15] ZHANG Q, CAO D X, MA Y, et al. Sulfide-based solid-state electrolytes: Synthesis, stability, and potential for all-solid-state batteries[J]. Adv Mater, 2019, 31(44): 1901131.
[16] [16] UITZ M, EPP V, BOTTKE P, et al. Ion dynamics in solid electrolytes for lithium batteries[J]. J Electroceram, 2017, 38(2): 142–156.
[17] [17] STAMMINGER A R, ZIEBARTH B, MROVEC M, et al. Ionic conductivity and its dependence on structural disorder in halogenated argyrodites Li6PS5X (X = Br, Cl, I)[J]. Chem Mater, 2019, 31(21): 8673–8678.
[18] [18] XIA L R, TANG J, CHEN Y F, et al. Exploring the effects of defect concentrations and distribution on Li diffusion in Li3OBr solid-state electrolyte using a deep potential model[J]. J Mater Chem A, 2024, 12(11): 6724–6732.
[19] [19] SHI S Q, QI Y, LI H, et al. Defect thermodynamics and diffusion mechanisms in Li2CO3 and implications for the solid electrolyte interphase in Li-ion batteries[J]. J Phys Chem C, 2013, 117(17): 8579–8593.
[20] [20] GORAI P, FAMPRIKIS T, SINGH B, et al. Devil is in the defects: Electronic conductivity in solid electrolytes[J]. Chem Mater, 2021, 33(18): 7484–7498.
[21] [21] HUANG G C, HUANG F Q, DONG W J. Machine learning in energy storage material discovery and performance prediction[J]. Chem Eng J, 2024, 492: 152294.
[22] [22] PAN J, CHENG Y T, QI Y. General method to predict voltage-dependent ionic conduction in a solid electrolyte coating on electrodes[J]. Phys Rev B, 2015, 91(13): 134116.
[23] [23] LI J, ZHOU M S, WU H H, et al. Machine learning-assisted property prediction of solid-state electrolyte[J]. Adv Energy Mater, 2024, 14(20): 2304480.
[24] [24] DAW M S, BASKES M I. Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals[J]. Phys Rev B, 1984, 29(12): 6443–6453.
[25] [25] BEHLER J, PARRINELLO M. Generalized neural-network representation of high-dimensional potential-energy surfaces[J]. Phys Rev Lett, 2007, 98(14): 146401.
[26] [26] BARTK A P, PAYNE M C, KONDOR R, et al. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons[J]. Phys Rev Lett, 2010, 104(13): 136403.
[27] [27] ZHANG L F, HAN J Q, WANG H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics[J]. Phys Rev Lett, 2018, 120(14): 143001.
[28] [28] HAJIBABAEI A, KIM K S. Universal machine learning interatomic potentials: Surveying solid electrolytes[J]. J Phys Chem Lett, 2021, 12(33): 8115–8120.
[29] [29] ZHOU R, LUO K, MARTIN S W, et al. Insights into lithium sulfide glass electrolyte structures and ionic conductivityviamachine learning force field simulations[J]. ACS Appl Mater Interfaces, 2024, 16(15): 18874–18887.
[30] [30] ASHERI A, FATHIDOOST M, GLAVAS V, et al. Data-driven multiscale simulation of solid-state batteriesviamachine learning[J]. Comput Mater Sci, 2023, 226: 112186.
[31] [31] MISHRA A K, RAJPUT S, KARAMTA M, et al. Exploring the possibility of machine learning for predicting ionic conductivity of solid-state electrolytes[J]. ACS Omega, 2023, 8(18): 16419–16427.
[32] [32] BAKTASH A, REID J C, ROMAN T, et al. Diffusion of lithium ions in Lithium-argyrodite solid-state electrolytes[J]. NPJ Comput Mater, 2020, 6: 162.
[33] [33] GUO H Y, WANG Q, STUKE A, et al. Accelerated atomistic modeling of solid-state battery materials with machine learning[J]. Front Energy Res, 2021, 9: 695902.
[34] [34] DAWSON J A, CANEPA P, CLARKE M J, et al. Toward understanding the different influences of grain boundaries on ion transport in sulfide and oxide solid electrolytes[J]. Chem Mater, 2019, 31(14): 5296–5304.
[35] [35] WANG R Y, GUO M Y, GAO Y X, et al. A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy[EB/OL]. 2024: 2406.18263. https://arxiv.org/abs/2406.18263v2.
[36] [36] KRESSE G, FURTHMLLER J. Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set[J]. Phys Rev B Condens Matter, 1996, 54(16): 11169–11186.
[37] [37] PERDEW J P, BURKE K, ERNZERHOF M. Generalized gradient approximation made simple[J]. Phys Rev Lett, 1996, 77(18): 3865–3868.
[38] [38] PACK J D, MONKHORST H J. “Special points for Brillouin-zone integrations”: A reply[J]. Phys Rev B, 1977, 16(4): 1748–1749.
[39] [39] LEE J, JU S, HWANG S, et al. Disorder-dependent Li diffusion in Li6PS5Cl investigated by machine-learning potential[J]. ACS Appl Mater Interfaces, 2024, 16(35): 46442–46453.
Get Citation
Copy Citation Text
LU Zhihao, WU Hongyu, GAO Yuxiang, ZHONG Zhicheng. Defect Regulation of Ionic Conductivity in Li6PS5Cl Based on a Large-Atom Model for Solid-State Electrolytes[J]. Journal of the Chinese Ceramic Society, 2025, 53(7): 1920
Special Issue:
Received: Feb. 13, 2025
Accepted: Aug. 12, 2025
Published Online: Aug. 12, 2025
The Author Email: