Publications

Publications#

Summary#

Here’s a summarized list, limited to a maximum of five papers per research area, prioritizing those with the highest citation counts and aiming for representative diversity within each category:

1. Computational Tools and Databases:

  • AmberTools (Case et al., 2023, 505 cites): Provides widely-used software tools for biomolecular simulations (molecular dynamics, etc.).

  • DeePMD-kit v2 (Zeng et al., 2023, 173 cites): A software package for building deep learning-based potential energy models in computational chemistry and materials science.

  • Greengenes2 (McDonald et al., 2023, 126 cites): A database and reference tree for organizing microbial genomic data, essential for microbiome studies.

  • BioLiP2 (Zhang et al., 2023, 49 cites): A database of biologically relevant interactions between ligands and proteins.

  • DeepMSA2 (Zheng et al., 2024, 33 cites): Uses metagenomics data and deep learning for improved protein structure prediction (both monomers and complexes).

2. Chemistry (Catalysis, Synthesis, Materials):

  • Tunable Metal Hydroxide Frameworks (Yuan et al., 2022, 200 cites): Develops new materials for catalyzing the oxygen evolution reaction, important for energy storage.

  • Electrolyte Design for Li Batteries (Holoubek et al., 2022, 142 cites): Focuses on improving low-temperature lithium metal batteries by understanding ion-pairing.

  • CO2 Reduction (Chang et al., 2022, 111 cites): Uses metal-coordinated phthalocyanines to understand and improve the electrochemical reduction of CO2.

  • Hydrogen Electrocatalysis (Sun et al., 2023, 83 cites): Investigates the role of water’s hydrogen-bond network in hydrogen production on platinum.

  • Stereoselective amino acid synthesis (Cheng et al., 2023, 52 cites): Development of synergistic photoredox-pyridoxal radical biocatalysis for producing amino acids.

3. Biology and Medicine:

  • AI and Race in Medical Imaging (Gichoya et al., 2022, 239 cites): Demonstrates that AI models can identify a patient’s self-reported race from medical images, raising significant ethical questions.

  • Enzyme Function Prediction (Yu et al., 2023, 132 cites): Uses contrastive learning (a machine learning technique) to predict enzyme function.

  • Tumor growth and exosomes (Wu et al., 2023, 107 cites): Explores how stiff matrices in the tumor microenvironment promote exosome secretion, leading to tumor growth.

  • Evolutionary Constraint (Christmas et al., 2023, 105 cites): Compares genomes across hundreds of placental mammals to understand evolutionary constraints and innovations.

  • Multi-omics Clustering (Lin et al., 2022, 53 cites): Develops a multimodal deep learning method for clustering single-cell multi-omics data.

4. Physics and Astronomy:

  • Neutrino Emission (Anonymous et al., 2022, 158 cites): Presents evidence for neutrino emission from the active galaxy NGC 1068.

  • Jets in Accretion Flows (Narayan et al., 2022, 84 cites): Studies the geometry, power, and black hole spin-down of jets from magnetically arrested hot accretion flows.

  • Parker Solar Probe (Raouafi et al., 2023, 74 cites): Summarizes discoveries from the Parker Solar Probe mission during its four years at solar cycle minimum.

  • High Thermal Conductivity (Cheng et al., 2022, 70 cites): Reports high thermal conductivity in wafer-scale cubic silicon carbide crystals.

  • Dark Matter Profile (Ou et al., 2024, 40 cites): Infers the dark matter profile of the Milky Way from its circular velocity curve.

5. Machine Learning and Computational Methods: (Note: Many papers listed under “Computational Tools” also involve Machine Learning. This section highlights papers where ML is applied to a specific scientific problem.)

  • Machine Learning for Antibody Aggregation (Lai et al., 2022, 47 cites): Predicts antibody aggregation and viscosity to aid high-concentration formulation development.

  • Machine Learning for Catalyst Complexity (Mou et al., 2023, 104 Cites): Applies machine learning to computational heterogeneous catalysis.

  • Machine Learning for Carbon Dot Synthesis (Senanayake et al., 2022, 29 cites): Predicts the emission color and wavelength of carbon dots using machine learning.

  • Machine Learning for Materials Discovery (Pal et al., 2022, 28 cites): Uses a scale-invariant machine-learning model to accelerate the discovery of quaternary chalcogenides.

  • Deep Learning for Ammonia Synthesis (Wan et al., 2022, 28 cites): Uses deep learning to investigate the effects of electric fields and dipoles on catalytic ammonia synthesis.

Top 5% Most Cited Publications#

The following publications have achieved more than 30 citations and are in the top 5% of all papers in the collection. These papers have accumulated over 6381 citations, which is 38.786% of all citations in the full dataset.

  1. (505 cites) Case, D. A., and Coauthors, 2023: AmberTools. Journal of Chemical Information and Modeling, 63, 6183–6191, https://doi.org/10.1021/acs.jcim.3c01153.

  2. (239 cites) Gichoya, J. W., and Coauthors, 2022: AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health, 4, e406–e414, https://doi.org/10.1016/s2589-7500(22)00063-2.

  3. (200 cites) Yuan, S., and Coauthors, 2022: Tunable metal hydroxide–organic frameworks for catalysing oxygen evolution. Nature Materials, 21, 673–680, https://doi.org/10.1038/s41563-022-01199-0.

  4. (173 cites) Zeng, J., and Coauthors, 2023: DeePMD-kit v2: A software package for deep potential models. The Journal of Chemical Physics, 159, https://doi.org/10.1063/5.0155600.

  5. (158 cites) , and Coauthors, 2022: Evidence for neutrino emission from the nearby active galaxy NGC 1068. Science, 378, 538–543, https://doi.org/10.1126/science.abg3395.

  6. (142 cites) Holoubek, J., and Coauthors, 2022: Electrolyte design implications of ion-pairing in low-temperature Li metal batteries. Energy & Environmental Science, 15, 1647–1658, https://doi.org/10.1039/d1ee03422g.

  7. (132 cites) Yu, T., H. Cui, J. C. Li, Y. Luo, G. Jiang, and H. Zhao, 2023: Enzyme function prediction using contrastive learning. Science, 379, 1358–1363, https://doi.org/10.1126/science.adf2465.

  8. (128 cites) Carvalho, O. Q., R. Marks, H. K. K. Nguyen, M. E. Vitale-Sullivan, S. C. Martinez, L. Árnadóttir, and K. A. Stoerzinger, 2022: Role of Electronic Structure on Nitrate Reduction to Ammonium: A Periodic Journey. Journal of the American Chemical Society, 144, 14809–14818, https://doi.org/10.1021/jacs.2c05673.

  9. (126 cites) McDonald, D., and Coauthors, 2023: Greengenes2 unifies microbial data in a single reference tree. Nature Biotechnology, 42, 715–718, https://doi.org/10.1038/s41587-023-01845-1.

  10. (115 cites) Makam, P., S. S. R. K. C. Yamijala, V. S. Bhadram, L. J. W. Shimon, B. M. Wong, and E. Gazit, 2022: Single amino acid bionanozyme for environmental remediation. Nature Communications, 13, https://doi.org/10.1038/s41467-022-28942-0.

  11. (111 cites) Chang, Q., and Coauthors, 2022: Metal-Coordinated Phthalocyanines as Platform Molecules for Understanding Isolated Metal Sites in the Electrochemical Reduction of CO2. Journal of the American Chemical Society, 144, 16131–16138, https://doi.org/10.1021/jacs.2c06953.

  12. (109 cites) Kleinmans, R., and Coauthors, 2023: ortho-Selective Dearomative [2π + 2σ] Photocycloadditions of Bicyclic Aza-Arenes. Journal of the American Chemical Society, 145, 12324–12332, https://doi.org/10.1021/jacs.3c02961.

  13. (107 cites) Wu, B., and Coauthors, 2023: Stiff matrix induces exosome secretion to promote tumour growth. Nature Cell Biology, 25, 415–424, https://doi.org/10.1038/s41556-023-01092-1.

  14. (105 cites) Christmas, M. J., and Coauthors, 2023: Evolutionary constraint and innovation across hundreds of placental mammals. Science, 380, https://doi.org/10.1126/science.abn3943.

  15. (105 cites) Lei, Y., and Coauthors, 2022: Perovskite superlattices with efficient carrier dynamics. Nature, 608, 317–323, https://doi.org/10.1038/s41586-022-04961-1.

  16. (104 cites) Mou, T., H. S. Pillai, S. Wang, M. Wan, X. Han, N. M. Schweitzer, F. Che, and H. Xin, 2023: Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nature Catalysis, 6, 122–136, https://doi.org/10.1038/s41929-023-00911-w.

  17. (84 cites) Narayan, R., A. Chael, K. Chatterjee, A. Ricarte, and B. Curd, 2022: Jets in magnetically arrested hot accretion flows: geometry, power, and black hole spin-down. Monthly Notices of the Royal Astronomical Society, 511, 3795–3813, https://doi.org/10.1093/mnras/stac285.

  18. (83 cites) Sun, Q., and Coauthors, 2023: Understanding hydrogen electrocatalysis by probing the hydrogen-bond network of water at the electrified Pt–solution interface. Nature Energy, 8, 859–869, https://doi.org/10.1038/s41560-023-01302-y.

  19. (77 cites) Wang, H., H. Shao, A. Das, S. Dutta, H. T. Chan, C. Daniliuc, K. N. Houk, and F. Glorius, 2023: Dearomative ring expansion of thiophenes by bicyclobutane insertion. Science, 381, 75–81, https://doi.org/10.1126/science.adh9737.

  20. (74 cites) Raouafi, N. E., and Coauthors, 2023: Parker Solar Probe: Four Years of Discoveries at Solar Cycle Minimum. Space Science Reviews, 219, https://doi.org/10.1007/s11214-023-00952-4.

  21. (71 cites) Choi, C., and Coauthors, 2022: Efficient electrocatalytic valorization of chlorinated organic water pollutant to ethylene. Nature Nanotechnology, 18, 160–167, https://doi.org/10.1038/s41565-022-01277-z.

  22. (70 cites) Cheng, Z., and Coauthors, 2022: High thermal conductivity in wafer-scale cubic silicon carbide crystals. Nature Communications, 13, https://doi.org/10.1038/s41467-022-34943-w.

  23. (70 cites) Luo, D., and Coauthors, 2023: Autonomous self-burying seed carriers for aerial seeding. Nature, 614, 463–470, https://doi.org/10.1038/s41586-022-05656-3.

  24. (70 cites) Zhou, J., and Coauthors, 2024: Healable and conductive sulfur iodide for solid-state Li–S batteries. Nature, 627, 301–305, https://doi.org/10.1038/s41586-024-07101-z.

  25. (70 cites) Petrov, P., and Coauthors, 2022: Data-driven Expectations for Electromagnetic Counterpart Searches Based on LIGO/Virgo Public Alerts. The Astrophysical Journal, 924, 54, https://doi.org/10.3847/1538-4357/ac366d.

  26. (69 cites) Zhang, C., and S. Mirarab, 2022: Weighting by Gene Tree Uncertainty Improves Accuracy of Quartet-based Species Trees. Molecular Biology and Evolution, 39, https://doi.org/10.1093/molbev/msac215.

  27. (67 cites) Yu, S., Z. Levell, Z. Jiang, X. Zhao, and Y. Liu, 2023: What Is the Rate-Limiting Step of Oxygen Reduction Reaction on Fe–N–C Catalysts? Journal of the American Chemical Society, 145, 25352–25356, https://doi.org/10.1021/jacs.3c09193.

  28. (66 cites) Athavale, S. V., S. Gao, A. Das, S. C. Mallojjala, E. Alfonzo, Y. Long, J. S. Hirschi, and F. H. Arnold, 2022: Enzymatic Nitrogen Insertion into Unactivated C–H Bonds. Journal of the American Chemical Society, 144, 19097–19105, https://doi.org/10.1021/jacs.2c08285.

  29. (65 cites) Lee, H., and Coauthors, 2023: Electron–phonon physics from first principles using the EPW code. npj Computational Materials, 9, https://doi.org/10.1038/s41524-023-01107-3.

  30. (62 cites) Ye, C.-X., X. Shen, S. Chen, and E. Meggers, 2022: Stereocontrolled 1,3-nitrogen migration to access chiral α-amino acids. Nature Chemistry, 14, 566–573, https://doi.org/10.1038/s41557-022-00895-3.

  31. (62 cites) Liang, Q., L. A. Wells, K. Han, S. Chen, M. C. Kozlowski, and T. Jia, 2023: Synthesis of Sulfilimines Enabled by Copper-Catalyzed S-Arylation of Sulfenamides. Journal of the American Chemical Society, 145, 6310–6318, https://doi.org/10.1021/jacs.2c12947.

  32. (57 cites) Miu, E. V., J. R. McKone, and G. Mpourmpakis, 2022: The Sensitivity of Metal Oxide Electrocatalysis to Bulk Hydrogen Intercalation: Hydrogen Evolution on Tungsten Oxide. Journal of the American Chemical Society, 144, 6420–6433, https://doi.org/10.1021/jacs.2c00825.

  33. (54 cites) Liu, Y., and Coauthors, 2023: Expanding PROTACtable genome universe of E3 ligases. Nature Communications, 14, https://doi.org/10.1038/s41467-023-42233-2.

  34. (53 cites) Lin, X., T. Tian, Z. Wei, and H. Hakonarson, 2022: Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications, 13, https://doi.org/10.1038/s41467-022-35031-9.

  35. (52 cites) Cheng, L., D. Li, B. K. Mai, Z. Bo, L. Cheng, P. Liu, and Y. Yang, 2023: Stereoselective amino acid synthesis by synergistic photoredox-pyridoxal radical biocatalysis. Science, 381, 444–451, https://doi.org/10.1126/science.adg2420.

  36. (50 cites) Rodriguez-R, L. M., and Coauthors, 2024: An ANI gap within bacterial species that advances the definitions of intra-species units. mBio, 15, https://doi.org/10.1128/mbio.02696-23.

  37. (49 cites) Zhang, C., X. Zhang, L. Freddolino, and Y. Zhang, 2023: BioLiP2: an updated structure database for biologically relevant ligand–protein interactions. Nucleic Acids Research, 52, D404–D412, https://doi.org/10.1093/nar/gkad630.

  38. (49 cites) Lv, L., H. Qian, A. B. Crowell, S. Chen, and Z. Li, 2022: Pd/NHC-Controlled Regiodivergent Defluorinative Allylation of gem-Difluorocyclopropanes with Allylboronates. ACS Catalysis, 12, 6495–6505, https://doi.org/10.1021/acscatal.2c01391.

  39. (48 cites) Thakur, N., and Coauthors, 2023: Anionic phospholipids control mechanisms of GPCR-G protein recognition. Nature Communications, 14, https://doi.org/10.1038/s41467-023-36425-z.

  40. (48 cites) Chhetri, M., and Coauthors, 2023: Dual-site catalysts featuring platinum-group-metal atoms on copper shapes boost hydrocarbon formations in electrocatalytic CO2 reduction. Nature Communications, 14, https://doi.org/10.1038/s41467-023-38777-y.

  41. (47 cites) Lai, P.-K., A. Gallegos, N. Mody, H. A. Sathish, and B. L. Trout, 2022: Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics. mAbs, 14, https://doi.org/10.1080/19420862.2022.2026208.

  42. (47 cites) Li, S., Z. Qin, H. Wu, M. Li, M. Kunz, A. Alatas, A. Kavner, and Y. Hu, 2022: Anomalous thermal transport under high pressure in boron arsenide. Nature, 612, 459–464, https://doi.org/10.1038/s41586-022-05381-x.

  43. (46 cites) Bazavov, A., and Coauthors, 2022: Semileptonic form factors for $\(B\rightarrow D^*\ell \nu \)\( at nonzero recoil from \)\(2+1\)$-flavor lattice QCD. The European Physical Journal C, 82, https://doi.org/10.1140/epjc/s10052-022-10984-9.

  44. (46 cites) Lee, S., and Coauthors, 2022: Shape memory in self-adapting colloidal crystals. Nature, 610, 674–679, https://doi.org/10.1038/s41586-022-05232-9.

  45. (46 cites) Johnson, M. D., and Coauthors, 2023: Key Science Goals for the Next-Generation Event Horizon Telescope. Galaxies, 11, 61, https://doi.org/10.3390/galaxies11030061.

  46. (45 cites) Ensslen, T., K. Sarthak, A. Aksimentiev, and J. C. Behrends, 2022: Resolving Isomeric Posttranslational Modifications Using a Biological Nanopore as a Sensor of Molecular Shape. Journal of the American Chemical Society, 144, 16060–16068, https://doi.org/10.1021/jacs.2c06211.

  47. (44 cites) He, J., Y. Xia, W. Lin, K. Pal, Y. Zhu, M. G. Kanatzidis, and C. Wolverton, 2021: Accelerated Discovery and Design of Ultralow Lattice Thermal Conductivity Materials Using Chemical Bonding Principles. Advanced Functional Materials, 32, https://doi.org/10.1002/adfm.202108532.

  48. (44 cites) Xu, D., and Coauthors, 2023: Ultrafast imaging of polariton propagation and interactions. Nature Communications, 14, https://doi.org/10.1038/s41467-023-39550-x.

  49. (42 cites) Kim, D. S., and Coauthors, 2023: Electrostatic moiré potential from twisted hexagonal boron nitride layers. Nature Materials, 23, 65–70, https://doi.org/10.1038/s41563-023-01637-7.

  50. (41 cites) Li, Y., C. Zhang, C. Feng, R. Pearce, P. Lydia Freddolino, and Y. Zhang, 2023: Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nature Communications, 14, https://doi.org/10.1038/s41467-023-41303-9.

  51. (41 cites) Muñoz, K. A., and Coauthors, 2024: A Gram-negative-selective antibiotic that spares the gut microbiome. Nature, 630, 429–436, https://doi.org/10.1038/s41586-024-07502-0.

  52. (40 cites) Ou, X., A.-C. Eilers, L. Necib, and A. Frebel, 2024: The dark matter profile of the Milky Way inferred from its circular velocity curve. Monthly Notices of the Royal Astronomical Society, 528, 693–710, https://doi.org/10.1093/mnras/stae034.

  53. (40 cites) Telloni, D., and Coauthors, 2022: Observation of a Magnetic Switchback in the Solar Corona. The Astrophysical Journal Letters, 936, L25, https://doi.org/10.3847/2041-8213/ac8104.

  54. (40 cites) Rao, R. G., R. Blume, M. T. Greiner, P. Liu, T. W. Hansen, K. S. Dreyer, D. D. Hibbitts, and J.-P. Tessonnier, 2022: Oxygen-Doped Carbon Supports Modulate the Hydrogenation Activity of Palladium Nanoparticles through Electronic Metal–Support Interactions. ACS Catalysis, 12, 7344–7356, https://doi.org/10.1021/acscatal.2c01063.

  55. (39 cites) Healy, J., and C. O. Lousto, 2022: Fourth RIT binary black hole simulations catalog: Extension to eccentric orbits. Physical Review D, 105, https://doi.org/10.1103/physrevd.105.124010.

  56. (39 cites) Dutta, S., and Coauthors, 2024: Double Strain-Release [2π+2σ]-Photocycloaddition. Journal of the American Chemical Society, 146, 5232–5241, https://doi.org/10.1021/jacs.3c11563.

  57. (39 cites) Apolinar, O., and Coauthors, 2022: Three-Component Asymmetric Ni-Catalyzed 1,2-Dicarbofunctionalization of Unactivated Alkenes via Stereoselective Migratory Insertion. Journal of the American Chemical Society, 144, 19337–19343, https://doi.org/10.1021/jacs.2c06636.

  58. (37 cites) Bezares, M., M. Bošković, S. Liebling, C. Palenzuela, P. Pani, and E. Barausse, 2022: Gravitational waves and kicks from the merger of unequal mass, highly compact boson stars. Physical Review D, 105, https://doi.org/10.1103/physrevd.105.064067.

  59. (37 cites) Zhang, C., and S. Mirarab, 2022: ASTRAL-Pro 2: ultrafast species tree reconstruction from multi-copy gene family trees. Bioinformatics, 38, 4949–4950, https://doi.org/10.1093/bioinformatics/btac620.

  60. (36 cites) Kim, S., J. Chung, H. Arlt, A. J. Pak, R. V. Farese, T. C. Walther, and G. A. Voth, 2022: Seipin transmembrane segments critically function in triglyceride nucleation and lipid droplet budding from the membrane. eLife, 11, https://doi.org/10.7554/elife.75808.

  61. (36 cites) Chatterjee, K., and R. Narayan, 2022: Flux Eruption Events Drive Angular Momentum Transport in Magnetically Arrested Accretion Flows. The Astrophysical Journal, 941, 30, https://doi.org/10.3847/1538-4357/ac9d97.

  62. (36 cites) Ye, C.-X., D. R. Dansby, S. Chen, and E. Meggers, 2023: Expedited synthesis of α-amino acids by single-step enantioselective α-amination of carboxylic acids. Nature Synthesis, 2, 645–652, https://doi.org/10.1038/s44160-023-00267-w.

  63. (36 cites) Sarker, P., and Coauthors, 2023: Hydration behaviors of nonfouling zwitterionic materials. Chemical Science, 14, 7500–7511, https://doi.org/10.1039/d3sc01977b.

  64. (35 cites) Schreier, M., P. Kenis, F. Che, and A. S. Hall, 2023: Trends in Electrocatalysis: The Microenvironment Moves to Center Stage. ACS Energy Letters, 8, 3935–3940, https://doi.org/10.1021/acsenergylett.3c01623.

  65. (35 cites) Kim, J., Y. Kimura, B. Puchala, T. Yamazaki, U. Becker, and W. Sun, 2023: Dissolution enables dolomite crystal growth near ambient conditions. Science, 382, 915–920, https://doi.org/10.1126/science.adi3690.

  66. (35 cites) Qian, H., H. D. Nguyen, L. Lv, S. Chen, and Z. Li, 2023: Chemo‐, Stereo‐ and Regioselective Fluoroallylation/Annulation of Hydrazones with gem‐Difluorocyclopropanes via Tunable Palladium/NHC Catalysis. Angewandte Chemie International Edition, 62, https://doi.org/10.1002/anie.202303271.

  67. (34 cites) Stivala, C. E., J. R. Zbieg, P. Liu, and M. J. Krische, 2022: Chiral Amines via Enantioselective π-Allyliridium-C,O-Benzoate-Catalyzed Allylic Alkylation: Student Training via Industrial–Academic Collaboration. Accounts of Chemical Research, 55, 2138–2147, https://doi.org/10.1021/acs.accounts.2c00302.

  68. (34 cites) The Event Horizon Telescope Collaboration, and Coauthors, 2024: First Sagittarius A Event Horizon Telescope Results. VII. Polarization of the Ring*. The Astrophysical Journal Letters, 964, L25, https://doi.org/10.3847/2041-8213/ad2df0.

  69. (34 cites) Peng, Y., R. Ji, T. Phan, W. Gao, V. I. Levitas, and L. Xiong, 2022: An atomistic-to-microscale computational analysis of the dislocation pileup-induced local stresses near an interface in plastically deformed two-phase materials. Acta Materialia, 226, 117663, https://doi.org/10.1016/j.actamat.2022.117663.

  70. (34 cites) Abbasi, R., and Coauthors, 2022: Searches for Neutrinos from Gamma-Ray Bursts Using the IceCube Neutrino Observatory. The Astrophysical Journal, 939, 116, https://doi.org/10.3847/1538-4357/ac9785.

  71. (34 cites) Zhu, Y., and Coauthors, 2023: Probing Ultralate Reionization: Direct Measurements of the Mean Free Path over 5 < z < 6. The Astrophysical Journal, 955, 115, https://doi.org/10.3847/1538-4357/aceef4.

  72. (34 cites) Gavini, V., and Coauthors, 2023: Roadmap on electronic structure codes in the exascale era. Modelling and Simulation in Materials Science and Engineering, 31, 063301, https://doi.org/10.1088/1361-651x/acdf06.

  73. (34 cites) Bazavov, A., and Coauthors, 2023: Light-quark connected intermediate-window contributions to the muon <mml:math xmlns:mml=”http://www.w3.org/1998/Math/MathML” display=”inline”>mml:mig</mml:mi>mml:mo−</mml:mo>mml:mn2</mml:mn></mml:math> hadronic vacuum polarization from lattice QCD. Physical Review D, 107, https://doi.org/10.1103/physrevd.107.114514.

  74. (33 cites) Magaña Zertuche, L., and Coauthors, 2022: High precision ringdown modeling: Multimode fits and BMS frames. Physical Review D, 105, https://doi.org/10.1103/physrevd.105.104015.

  75. (33 cites) Zheng, W., Q. Wuyun, Y. Li, C. Zhang, L. Freddolino, and Y. Zhang, 2024: Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data. Nature Methods, 21, 279–289, https://doi.org/10.1038/s41592-023-02130-4.

  76. (32 cites) Peng, J., L. Giordano, T. C. Davenport, and Y. Shao-Horn, 2022: Stability Design Principles of Manganese-Based Oxides in Acid. Chemistry of Materials, 34, 7774–7787, https://doi.org/10.1021/acs.chemmater.2c01233.

  77. (32 cites) Tandoc, C., Y.-J. Hu, L. Qi, and P. K. Liaw, 2023: Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys. npj Computational Materials, 9, https://doi.org/10.1038/s41524-023-00993-x.

  78. (32 cites) Liao, Z., L. Chen, G. Duan, Y. Wang, C. Liu, R. Yu, and Q. Si, 2023: Electron correlations and superconductivity in <mml:math xmlns:mml=”http://www.w3.org/1998/Math/MathML”>mml:mrowmml:msubmml:miLa</mml:mi>mml:mn3</mml:mn></mml:msub>mml:msubmml:miNi</mml:mi>mml:mn2</mml:mn></mml:msub>mml:msub<mml:mi mathvariant=”normal”>O</mml:mi>mml:mn7</mml:mn></mml:msub></mml:mrow></mml:math> under pressure tuning. Physical Review B, 108, https://doi.org/10.1103/physrevb.108.214522.

  79. (32 cites) Jankins, T. C., W. C. Bell, Y. Zhang, Z.-Y. Qin, J. S. Chen, M. Gembicky, P. Liu, and K. M. Engle, 2022: Low-valent tungsten redox catalysis enables controlled isomerization and carbonylative functionalization of alkenes. Nature Chemistry, 14, 632–639, https://doi.org/10.1038/s41557-022-00951-y.

  80. (32 cites) Pu, T., A. Setiawan, B. Mosevitzky Lis, M. Zhu, M. E. Ford, S. Rangarajan, and I. E. Wachs, 2022: Nature and Reactivity of Oxygen Species on/in Silver Catalysts during Ethylene Oxidation. ACS Catalysis, 12, 4375–4381, https://doi.org/10.1021/acscatal.1c05939.

  81. (31 cites) Wang, J., H. N. Do, K. Koirala, and Y. Miao, 2023: Predicting Biomolecular Binding Kinetics: A Review. Journal of Chemical Theory and Computation, 19, 2135–2148, https://doi.org/10.1021/acs.jctc.2c01085.

  82. (31 cites) Lee, T.-S., H.-C. Tsai, A. Ganguly, and D. M. York, 2023: ACES: Optimized Alchemically Enhanced Sampling. Journal of Chemical Theory and Computation, 19, 472–487, https://doi.org/10.1021/acs.jctc.2c00697.

  83. (31 cites) Qian, H., Z. P. Cheng, Y. Luo, L. Lv, S. Chen, and Z. Li, 2023: Pd/IPrBIDEA-Catalyzed Hydrodefluorination of gem-Difluorocyclopropanes: Regioselective Synthesis of Terminal Fluoroalkenes. Journal of the American Chemical Society, 146, 24–32, https://doi.org/10.1021/jacs.3c07992.

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Journals#

Overall, more than 470 distinct journals are represented in the dataset. The list below provides a view into the most frequent journals.

A brief journal distribution summary shows :

  • Broad Scope: The dataset encompasses a wide range of scientific disciplines, with publications spread across more than 470 distinct journals.

  • Chemistry Dominance: The Journal of the American Chemical Society (J. Am. Chem. Soc.) has the highest number of publications (80, 3.8%), indicating a strong representation of chemistry research.

  • Astrophysics Prominence: The Astrophysical Journal (ApJ) is the second most frequent journal (75, 3.5%), showcasing a significant focus on astrophysics.

  • Physics Emphasis: Several Physical Review journals (Phys. Rev. D, Phys. Rev. B, Phys. Rev. Materials) appear prominently, highlighting a substantial contribution from various areas of physics, including particle physics, condensed matter physics, and materials physics.

  • Multidisciplinary Research: Nature Communications and Proceedings of the National Academy of Sciences of the United States of America are present, signifying the inclusion of high-impact, multidisciplinary research.

  • Computational Chemistry and Materials: Journals like Journal of Chemical Theory and Computation, Journal of Chemical Information and Modeling, Journal of Physical Chemistry B, and Journal of Physical Chemistry C demonstrate a focus on computational chemistry and materials science.

  • Catalysis Focus: Specialized journals like ACS Catal. and Angew Chem Int Ed show the importance of the catalysis.

  • Overall, a strong representation of chemistry, physics (especially astrophysics and various subfields), and materials science. With additional showing of multidisplinary research.

Journal Name

Publication Count (% total)

J. Am. Chem. Soc.

80 (3.8%)

ApJ

75 (3.5%)

Phys. Rev. D

62 (2.9%)

Nat Commun

42 (2.0%)

J. Phys. Chem. B

38 (1.8%)

Phys. Rev. B

35 (1.7%)

Proc. Natl. Acad. Sci. U.S.A.

32 (1.5%)

J. Chem. Theory Comput.

30 (1.4%)

J. Chem. Inf. Model.

26 (1.2%)

J. Phys. Chem. C

25 (1.2%)

ApJL

24 (1.1%)

ACS Catal.

22 (1.0%)

J. Fluid Mech.

21 (0.99%)

Phys. Rev. Materials

21 (0.99%)

Angew Chem Int Ed

21 (0.99%)

Publication Abstract Topic Visualization#

This visualization shows the topics which emerge from analysis of the abstracts and titles of all publications.