Tri-modal machine learning powers efficient catalyst discovery for high-performance energy storage
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OpenAlex topics: Machine Learning in Materials Science · Advanced Battery Materials and Technologies · Electrocatalysts for Energy Conversion
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References (78)
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1. Chemical synthesis of K2S2 and K2S3 for probing electrochemical mechanisms in K–S batteries10.1021/acsenergylett.8b01719 ↗
2. Understanding fluxes as media for directed synthesis: in situ local structure of molten potassium polysulfides10.1021/ja303047e ↗
3. Iodine-doped sulfurized polyacrylonitrile with enhanced electrochemical performance for room-temperature sodium/potassium sulfur batteries10.1039/C9CC01612K ↗
4. High-energy and long-lifespan potassium-sulfur batteries enabled by concentrated electrolyte10.1002/adfm.202209145 ↗
5. Reactivity descriptors for sulfur redox kinetics in lithium–sulfur batteries: from mechanistic insights to machine learning driven catalyst design10.1039/D5CS00324E ↗
6. Hollow CuS nanoboxes as Li-free cathode for high-rate and long-life lithium metal batteries10.1002/aenm.202070028 ↗
7. Benchmarking graph neural networks for materials chemistry10.1038/s41524-021-00554-0 ↗
8. CuS and Cu2S as cathode materials for lithium batteries: a review10.1002/celc.201900066 ↗
9. High loading CuS-based cathodes for all-solid-state lithium sulfur batteries with enhanced volumetric capacity10.1016/j.ensm.2020.01.022 ↗
10. Synchrotron radiation spectroscopic studies of Mg2+ storage mechanisms in high-performance rechargeable magnesium batteries with Co-doped FeS2 cathodes
11. Cationic Co-doping in copper sulfide nanosheet cathodes for efficient magnesium storage10.1016/j.cej.2023.142433 ↗
12. Water splitting performance of metal and non-metal-doped transition metal oxide electrocatalysts10.1016/j.ccr.2022.214864 ↗
13. Hierarchical ultrathin carbon encapsulating transition metal doped MoP electrocatalysts for efficient and pH-universal hydrogen evolution reaction10.1016/j.nanoen.2020.104445 ↗
14. Co-doping strategy for developing perovskite oxides as highly efficient electrocatalysts for oxygen evolution reaction10.1002/advs.201500187 ↗
15. Ferrum-molybdenum dual incorporated cobalt oxides as efficient bifunctional anti-corrosion electrocatalyst for seawater splitting10.1016/j.apcatb.2023.122488 ↗
16. Machine learned features from density of states for accurate adsorption energy prediction10.1038/s41467-020-20342-6 ↗
17. A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts10.1038/s41524-020-00447-8 ↗
18. Machine learning assisted understanding and discovery of CO2 reduction reaction electrocatalyst10.1021/acs.jpcc.2c08343 ↗
19. Fast and accurate modeling of molecular atomization energies with machine learning10.1103/PhysRevLett.108.058301 ↗
20. Atom-centered symmetry functions for constructing high-dimensional neural network potentials10.1063/1.3553717 ↗
21. Comparing molecules and solids across structural and alchemical space10.1039/C6CP00415F ↗
22. SchNet - a deep learning architecture for molecules and materials10.1063/1.5019779 ↗
23. Graph neural networks for materials science and chemistry10.1038/s43246-022-00315-6 ↗
24. Neural message passing for quantum chemistry
25. Kipf · 2017
26. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties10.1103/PhysRevLett.120.145301 ↗
27. Graph networks as a universal machine learning framework for molecules and crystals10.1021/acs.chemmater.9b01294 ↗
28. Gasteiger · 2020
29. Atomistic line graph neural network for improved materials property predictions10.1038/s41524-021-00650-1 ↗
30. Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity10.1126/sciadv.adi3245 ↗
31. Structure-band gap relationships in hexagonal polytypes and low-dimensional structures of hybrid tin iodide perovskites10.1021/acs.inorgchem.6b02764 ↗
32. Charting the complete elastic properties of inorganic crystalline compounds10.1038/sdata.2015.9 ↗
33. Robocrystallographer: automated crystal structure text descriptions and analysis10.1557/mrc.2019.94 ↗
34. Multimodal learning of heat capacity based on transformers and crystallography pretraining10.1063/5.0201755 ↗
35. Lee · 2025
36. CrysMMNet: multimodal representation for crystal property prediction
37. Munjal · 2024
38. A unified approach to interpreting model predictions
39. Commentary: the Materials Project: a materials genome approach to accelerating materials innovation10.1063/1.4812323 ↗
40. The thermodynamic scale of inorganic crystalline metastability10.1126/sciadv.1600225 ↗
41. Thermodynamic limit for synthesis of metastable inorganic materials10.1126/sciadv.aaq0148 ↗
42. The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies10.1038/npjcompumats.2015.10 ↗
43. The role of decomposition reactions in assessing first-principles predictions of solid stability10.1038/s41524-018-0143-2 ↗
44. Machine learning-based discovery of vibrationally stable materials10.1038/s41524-022-00943-z ↗
45. DFT study on doping effect of different transition metals on MoS2 for enhancing the formation of S vacancies10.1016/j.ijhydene.2024.07.408 ↗
46. Water dissociation energy variations in hydrogen evolution reaction in transition metal-doped MoS2
47. DFT-machine learning joint exploration of transition metal-doped ferroelectric BaTiO3 for electrocatalytic hydrogen evolution10.1021/acsami.5c02406 ↗
48. Scaling relations in homogeneous catalysis: analyzing the Buchwald-Hartwig amination reaction10.1021/acscatal.9b04323 ↗
49. Scaling relationships and volcano plots in homogeneous catalysis10.1021/acs.jpclett.0c01991 ↗
50. Electric dipole descriptor for machine learning prediction of catalyst surface-molecular adsorbate interactions10.1021/jacs.0c01825 ↗
51. MatSciBERT: a materials domain language model for text mining and information extraction10.1038/s41524-022-00784-w ↗
52. Falaki · 2023
53. Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis10.1016/j.commatsci.2012.10.028 ↗
54. In situ microwave-assisted fabrication of hierarchically arranged metal sulfide counter electrodes to boost stability and efficiency of quantum dot-sensitized solar cells
55. Y-doped CuS promotes selective electroreduction of CO2 to ethanol10.1021/jacs.5c13850 ↗
56. Construction of Ru, O Co-doping MoS2 for hydrogen evolution reaction electrocatalyst and surface-enhanced raman scattering substrate: high-performance, recyclable, and durability improvement
57. Lithium-sulfur batteries based on nitrogen-doped carbon and an ionic-liquid electrolyte10.1002/cssc.201200101 ↗
58. Molecular clip strategy of modified sulfur cathodes for high-performance potassium sulfur batteries
59. Sulfur nanocomposite as a positive electrode material for rechargeable Potassium-Sulfur batteries10.1039/C7CC09913D ↗
60. Tuning the linkers in polymer-based cathodes to realize high sulfur content and high-performance Potassium-sulfur batteries10.1021/acs.jpcc.1c05283 ↗
61. Confined and covalent sulfur for stable room temperature potassium-sulfur battery10.1016/j.electacta.2018.10.040 ↗
62. A novel rechargeable potassium-sulfur battery based on liquid alloy anode10.1016/j.matlet.2019.01.108 ↗
63. Metal-organic framework-derived nitrogen-doped cobalt nanocluster inlaid porous carbon as high-efficiency catalyst for advanced Potassium-Sulfur batteries10.1021/acsnano.0c07658 ↗
64. Catalytic oxidation of K2S via atomic Co and pyridinic N synergy in Potassium–Sulfur batteries10.1021/jacs.1c06255 ↗
65. Reducing overpotential of solid-state sulfide conversion in Potassium-sulfur batteries10.1002/anie.202301681 ↗
66. Optimizing potassium polysulfides for high performance potassium-sulfur batteries10.1038/s41467-024-45405-w ↗
67. Strong d−π orbital coupling of Co–C4 atomic sites on graphdiyne boosts potassium–Sulfur battery electrocatalysis10.1021/jacs.3c09533 ↗
68. Free-standing, flexible and stable potassium-sulfur battery enabled by controllable porous carbon cloth10.1016/j.jpowsour.2020.228874 ↗
69. High-performance stable Potassium-sulfur batteries enabled by free-standing CNT film-based composite cathodes10.1007/s11664-021-08823-w ↗
70. Room-temperature potassium-sulfur batteries enabled by microporous carbon stabilized small-molecule sulfur cathodes
71. A reversible nonaqueous room-temperature potassium-sulfur chemistry for electrochemical energy storage10.1016/j.ensm.2018.06.020 ↗
72. Dynamic doping and interphase stabilization for cobalt-free and high-voltage lithium metal batteries10.1038/s41467-025-58110-z ↗
73. Therrien · 2025
74. A multi-modal transformer for predicting global minimum adsorption energy10.1038/s41467-025-58499-7 ↗
75. LLM-prop: predicting the properties of crystalline materials using large language models10.1038/s41524-025-01536-2 ↗
76. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set10.1016/0927-0256(96)00008-0 ↗
77. Projector augmented-wave method10.1103/PhysRevB.50.17953 ↗
78. Generalized gradient approximation made simple10.1103/PhysRevLett.77.3865 ↗