CENTER FOR THE SCIENCE OF
SYNTHESIS ACROSS SCALES

124. Amijai Saragovi, Harley Pyles, Evelyn B. Huddy, Wenzhao Dai, Xinqi Li, Andrew J. Borst, Nikita Hanikel, Alexis Courbet, Paul S. Kwon, Fátima A. Dávila-Hernández, Ryan Kibler, Luki Goldschmidt, Dionne K. Vafeados, Aza Allen, Kenneth D. Carr, Asim K. Bera, Alex Kang, Evans Brackenbrough, Sakshi Schmid, Yuna Bae, Lance Stewart, Shuai Zhang, James De Yoreo, and David Baker (2026). Programmed Mineralization of Metal-Oxide Compounds by De Novo Designed Protein Templates. Science. DOI: https://doi.org/10.1101/2024.06.24.600095 

 

123. Sonpal, A., and J. Pfaendtner (2026). Phosphoserine Charge State Drives Ion Condensation and Spatial Polyamine Presentation in Multirepeat Silaffin. J. Phys. Chem. B. DOI: https://doi.org/10.1021/acs.jpcb.6c03261  

 

122. Schmid, SY., B. Helfrecht, A. B.A. Legg, H. Pyles, J. Chen, J.R. Edison, M. Ziatdinov, Z. Preisler, O. Dollar, S. Whitelam, S.V. Kalinin, D. Baker, C.J. Mundy, S. Zhang, and J.J. De Yoreo (2026). Impact of solvent forces and broken symmetry on the assembly of designed proteins at a liquid-solid interface. Nat Commun, 17 (1): 2446. DOI: https://doi.org/10.1038/s41467-026-69170-0

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121. Close, EGS., AS Lipton, M. Zorman, H. Pyles, Y. Chen, H. Trinh, GW. Buchko, CL Chen, C. J. Mundy, D. Baker, W. Shaw (2026). Elucidating molecular level interfacial interactions between a de novo protein and nucleated calcite with solid-state NMR. RSC Materials Advances, 7 (7): 3545-3558. DOI: https://doi.org/10.1039/D5MA01322D

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120. Lowe, C.,  L.T. Yu, J. Tu, I. O’Reilly, E. Miura-Stempel, H.C. Larson, C.C. Bodinger, D. Baker, and B.M. Cossairt (2026). Nanomolar Sensitivity Chirality Transfer from Designed Helical Repeat Proteins to Achiral CdS Nanorods. Nano Letters, 26 (11): 3776–3783. DOI: https://doi.org/10.1021/acs.nanolett.5c06115

 

119. Shi, H., C.J. Mundy, G.K. Schenter, and J. Chun (2026). Energy conserving contact dynamics of nonspherical rigid-body particles. APL Computational Physics, 2 (1): 016108. DOI: https://doi.org/10.1063/5.0319853

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118. Xia, Y., S. Kim, M. Zhang, Y. Lin, S. Zhang, Y. Zhao, J. Liu, S. Jin, H. Heinz, W. Im, and J.J. De Yoreo (2026). Substrate-Directed Dimensional and Phase Control of Peptide Assemblies on Two-Dimensional van der Waals Materials. American Chemical Society, 148 (10): 10702-10713. DOI: https://doi.org/10.1021/jacs.5c20322 

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117. Berlaga, A., R. Zheng, Z. Zhang, J. Lee, D. Gandhi, CL Chen, and A.L. Ferguson (2026). Data-Driven Engineering of Thermostable Collagen-Mimetic Peptoid Triple Helices. Macromolecular Rapid Communications, 47 (8): 1022-13368. DOI: https://doi.org/10.1002/marc.202500917 

 

116. Zheng, R., M. Zhao, W. Zhou, TKH Trinh, S. Zhang, J.J. De Yoreo, A. L. Ferguson, and CL Chen (2026). Polymorphism in Self-Assembly of Short Peptoid Sequences. Polymer Science & Technology, 2 (3): 176-185.

DOI: https://doi.org/10.1021/polymscitech.5c00123

 

115. Lin, Z. and F. Baneyx (2026). Leveraging Hydration Forces for Size-Specific Nanoparticle Enrichment With a Redox-Responsive Silica-Binding Elastin-Like Polypeptide. ACS Nano, 20 (2): 1814–1825.

DOI: https://doi.org/10.1021/acsnano.5c04707 

 

114. Zhang, S., H. Pyles, D. Baker, and J. J De Yoreo (2025). Designing protein–material interfaces. MRS Bulletin, 50 (12): 1454-1464. DOI: https://doi.org/10.1557/s43577-025-01008-4 

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113. Sun, M., Z. Huang, M.Y. Yaman, M. Ziatdinov, S.V. Kalinin, and D. Ginger (2025). Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using on Far-Field Spectral Properties. J. Phys. Chem C, 129 (50): 22066–22074. DOI: https://doi.org/10.1021/acs.jpcc.5c07879

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112. Wixson, W. C., N.Y. Naser, A. Sonpal, J. Pfaendtner, and F. Baneyx (2025). Genetic Control of Morphological Transitions in a Coacervating Protein Template. Soft Matter, 22 (2): 398-410. DOI: https://doi.org/10.1039/D5SM01047K

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111. Chiang, H.T., N. Kern, Z.R. Wylie, A. Moeez, H. Zhang, D. McKeen, N.S. Herringer, O. Gang, A.L. Ferguson, Z. Sherman, and L.D. Pozzo (2025). Assembly of Small Silica Nanoparticles using Lipid-Tethered DNA ‘Bonds’. Soft Matter, 21 (48): 9398-9411. DOI: https://doi.org/10.1039/D5SM00924C

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110. Zhou, W., H. Botao, TKH Trinh, Z. Lin, AY Heble, J. Chen, J. J De Yoreo, J. Liu, CL Chen, and S. Zhang (2025). Tuning Molecular Interactions between Peptoids and Substrates to Achieve Surface-Agnostic Coating. ACS Appl. Mater. Interfaces, 17 (49): 67318–67327. DOI: https://doi.org/10.1021/acsami.5c18457

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109. Vaddi, K., H.T. Chiang, A. Grey, Z.R. Wiley, and L.D. Pozzo (2025). Autonomous Phase Mapping of Gold Nanoparticles Synthesis with Differentiable Models of Spectral Shape. npj Computational Materials, 11 (1): 335. DOI: https://doi.org/10.1038/s41524-025-01822-z

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108. Shi, C., H. Shen, Y. Bae, J. Heo, S. Zhang, L. Yao, D. Baker, and J. J De Yoreo (2025). The Anisotropic Adsorption of De Novo Allosteric Two-Component Protein Fibers on Mica Surfaces. Adv. Funct. Mater, 36 (23): e20864. 

DOI: https://doi.org/10.1002/adfm.202520864

 

107. Ghosh, A., M. Ziatdinov, and S.V. Kalinin (2025). Active deep kernel learning of molecular properties from structural embeddings. APL Mach Learn, 3 (4): 046103. DOI: https://doi.org/10.1063/5.0282700

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106. Larson, H., G. Dixon, Y. Chang, and B.M. Cossairt (2025). Expanding Parameter Space to Enable Low-Temperature Synthesis of Crystalline Indium Phosphide Quantum Dots. ACS Nano, 19 (38): 34384–34394. DOI: https://doi.org/10.1021/acsnano.5c12652 

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105. Wang, S., A. Favor, R.D. Kibler, J.M. Lubner, A.J. Borst, N. Coudray, R.L. Redler, H.T. Chiang, W. Sheffler, Y. Hsia, N.P. Bethel, Z. Li, D. C. Ekiert, G. Bhabha, L.D. Pozzo and D. Baker (2025). Bond-centric modular design of protein assemblies. Nat. Mater, 24 (10): 1644–1652. DOI: https://doi.org/10.1038/s41563-025-02297-5 

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104. Cai, Y., W. Idso, W. C. Wixson, N.Y. Naser, Z. Lin, and F. Baneyx. (2025). Steric Modulation of Protein-Mediated Nanoparticle Assembly: Controlling Cluster Size, Polydispersity, and FRET Responses by Rebalancing Short- and Long-Range Interactions. Small, 21 (35): 2503026. DOI: https://doi.org/10.1002/smll.202503026 

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​103. Zhang, Z.. H.T. Chiang, Y. Xia, N. Avakyan, R. Sonani, F. Wang, E. Egelman, J.J. De Yoreo, L.D. Pozzo, and F. A. Tezcan (2025). Design of Light- and Chemically-Responsive Protein Assemblies through Host-Guest Interactions. Chem, 11 (6): 102407. DOI: https://doi.org/10.1016/j.chempr.2024.102407

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102. Chiang, H.T., Z. Zhang, K., A.F. Tezcan, and L.D. Pozzo (2025). Efficient Analysis of Small-Angle Scattering Curves for Large Biomolecular Assemblies Using Monte Carlo Methods. J. Appl. Cryst, 58 (3): 963-975. DOI: https://doi.org/10.1107/S1600576725003267

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101. Schwarzer, M., J. Farebrother, J. Greaves, E. D. Cubuk, R. Agarwal, A. Courville, M. G. Bellemare, S.V. Kalinin, I. Mordatch, P. S. Castro, and K. M. Roccapriore. (2025). ​Learning and Controlling Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy. Adv. Mater. Interfaces, 12 (11): 2400998. DOI: https://doi.org/10.1002/admi.202400998

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100. Teng, F., H. Zhang, D. Nykypanchuk, R. Li, L. Yang, N. Tiwale, Z. Xi, M. Liu, M. He, and O. Gang. (2025). ​Macroscale-Area Patterning of Three-Dimensional DNA-Programmable Frameworks. Nat Commun, 16 (1): 3238. DOI: https://doi.org/10.1038/s41467-025-58422-0

 

99. Shi, C., Y. Bae, M. Zhang, and J. J. De Yoreo. (2025). Manipulating the Assembly and Architecture of Fibrillar Silk. Adv. Mater, 37 (24): 2501096. DOI: https://doi.org/10.1002/adma.202501096

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​98. Qi, X., S. Helland, C. Lowe, H. Larson, J. Cui, R. Zheng, M. Monahan,  C-L. Chen, J. J. De Yoreo, J. Pfaendtner, and B.M. Cossairt. (2025). Toward Computation-Guided Design of Tunable Organic–Inorganic CdS Quantum Dot Binary Superlattices. Nano Letters, 25 (100): 3989–3996. DOI: https://doi.org/10.1021/acs.nanolett.5c00024

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97. Larson, H., Z. Lin, F. Baneyx and B.M. Cossairt. (2025). Alternate InP Synthesis with Aminophosphines: Solution-Liquid-Solid Nanowire Growth. Nanoscale, 17 (11): 6593-6603. DOI: https://doi.org/10.1039/D4NR04907A

 

96. Li, Y., J. Zhu, Z. Zhang, J. Wei, F. Wang, G. Meisl, T. Knowles, E, Egelman and F. A Tezcan. (2025). Transforming an ATP-dependent enzyme into a dissipative, self-assembling system. Nat. Chem. Biol, 21 (6): 883–893. DOI: https://doi.org/10.1038/s41589-024-01811-1

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95.​ Raghavan, A., U. Pratiush, M. Valleti, R. Liu, R. Emery, H. Funakubo, Y. Liu, P. Rack and S.V. Kalinin. (2025).  Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization. J. Appl. Phys, 137 (3): 034901. DOI: https://doi.org/10.1063/5.0233070
 

94.​ McCutchin, C., K. Edgar, C-L. Chen, and P. Dove. (2024). Silica-Biomacromolecule Interactions: Toward a Mechanistic Understanding of Silicification. Biomacromolecules, 26 (1): 43–84. DOI: https://doi.org/10.1021/acs.biomac.4c00674.

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93.​ Berlaga, A., K. Torkelson, A. Seal, J. Pfaendtner, and A.L. Ferguson. (2024). A Modular and Extensible CHARMM-Compatible Model for All-Atom Simulation of Polypeptoids. J Chem Phys, 161 (24): 244901. DOI: https://doi.org/10.1063/5.0238570

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92.​ Lowe, C., H. Larson, Y. Cai, H.T. Chiang, L.D. Pozzo, F. Baneyx and B.M. Cossairt. (2024). Induced Chirality in QDs Using Thermoresponsive Elastin-like Polypeptides. Langmuir, 41 (1): 1047–1056. DOI: https://doi.org/10.1021/acs.langmuir.4c04339​​​

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91.​ Biswas, A., M. Valleti, R. Vasudevan, M. Ziatdinov, and S. V. Kalinin. (2024). Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization. ASME. J. Comput. Inf. Sci. Eng, 24 (12): 121005. DOI: https://doi.org/10.1115/1.4066856 

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90. Jin, B., Y. Chen, H. Pyles, M. D. Baer, B. A. Legg, Z. Wang, N. M. Washton, K. T. Mueller, D. Baker, G. K. Schenter, C. J. Mundy, and J. J. De Yoreo. (2024). Formation, chemical evolution and solidification of the dense liquid phase of calcium (bi)carbonate. Nat. Mater, 24 (1): 125-132. DOI: https://doi.org/10.1038/s41563-024-02025-5

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89. Naser, N.Y., W. C. Wixson, H. Larson, B.M. Cossairt, L.D. Pozzo and F. Baneyx. (2024). Biomimetic Mineralization of Positively Charged Silica Nanoparticles Templated by Thermoresponsive Protein Micelles: Applications to Electrostatic Assembly of Hierarchical and Composite Superstructures. Soft Matter, 21 (2): 166-178. DOI: https://doi.org/10.1039/D4SM00907J

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88. Chiang, HT., K. Vaddi, and L.D. Pozzo. (2024). Data-driven exploration of silver nanoplate formation in multidimensional chemical design spaces. Digital Discovery, 3 (11): 2252-2264. DOI: https://doi.org/10.1039/D4DD00211C

 

87. Barakati, K., H. Yuan, A. Goyald and S. V. Kalinin. (2024). Physics-based reward driven image analysis in microscopy. Digital Discovery, 3 (10): 2061-2069. DOI: https://doi.org/10.1039/D4DD00132J

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86. Torkelson, K. and J. Pfaendtner. (2024). Exploration of Tertiary Structure in Sequence-Defined Polymers Using Molecular Dynamics Simulations. Biomacromolecules, 25 (10): 6439–6450.

DOI: https://doi.org/10.1021/acs.biomac.4c00527

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85. Shi, C., M. Zorman, X. Zhao, M. B. Salmeron, J. Pfaendtner, XY. Liu, S. Zhang, and J.J. De Yoreo. (2024). Two-dimensional silk. Science Advances, 10 (38): eado4142. DOI: https://doi.org/10.1126/sciadv.ado4142

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84. Slautin, B.N., Y. Liu, H. Funakubo, R. Vasudevan, M. Ziatdinov, and S. V. Kalinin. (2024). Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning. ACS Nano, 18 (36): 24898–24908.

DOI: https://doi.org/10.1021/acsnano.4c05368 

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​83. Valleti, M., M. Ziatdinov, Y. Liu, and S.V. Kalinin. (2024). Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders. npj Computational Materials, 10 (1): 183.

DOI: https://doi.org/10.1038/s41524-024-01250-5 

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​82. Liu, Y., BD. Huey, M. Ziatdinov, and  S.V. Kalinin. (2024). Physical discovery in representation learning via conditioning on prior knowledge. J. Appl. Phys, 136 (6): 064902. DOI: https://doi.org/10.1063/5.0222403

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81. Vijayakumar, S. Alberstein, R. Zhang, Z.,Lu,  Y-S.. Chan, A., Wahl, C., Ha, J., Hunka, D., Boss, G., Sailor, M. and F. A.Tezcan. (2024). Designed 2D protein crystals as dynamic molecular gatekeepers for a solid-state device. Nature Communications, 15 (1): 6326. DOI: https://doi.org/10.1038/s41467-024-50567-8         

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80. Stegmann, A., B.A. Legg, J.J. De Yoreo, and S. Zhang. (2024). Machine learning-driven descriptions of protein dynamics at solid-liquid interfaces. J-Y Yoon & C. Yu (Eds.), Machine Learning and Artificial Intelligence in Chemical and Biological Sensing, 321-340. DOI: https://doi.org/10.1016/B978-0-443-22001-2.00013-5 

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​​79.  Roccapriore, K.M., R. Torsi, J.A. Robinson, S.V. Kalinin, and M. Ziatdinov. (2024). Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations. Science Advances, 10 (29): eadn5899.

DOI: https://doi.org/10.1126/sciadv.adn5899

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​78. Maradesa, A., B. Py , J. Huang, Y. Lu, P. Iurilli, A. Mrozinski, H.M. Law, Y. Wang, Z. Wang, J. Li, S. Xu, Q. Meyer, J. Liu, C. Brivio, A. Gavrilyuk, K. Kobayashi, A. Bertei, N.J. Williams, C. Zhao, M. Danzer, M. Zic, P. Wu, V. Yrjänä, S. Pereverzyev, Y. Chen, A. Weber, S. V. Kalinin, J.P. Schmidt, Y. Tsur, B.A. Boukamp, Q. Zhang, M. Gaberšček, R. O’Hayre, and F. Ciucci. (2024). Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method. Joule, 8 (7): 1958-1981. DOI: https://doi.org/10.1016/j.joule.2024.05.008

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​77. Slautin, B. N., U. Pratiush, I.N. Ivanov, Y. Liu, R. Pant, X. Zhang, I. Takeuchi, M. Ziatdinov and S.V. Kalinin. (2024) Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries. Digital Discovery, 3 (8): 1602-1611. DOI: https://doi.org/10.1039/D4DD00109E

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​76. Gupta, A. K., E. Zarkadoula, M. Ziatdinov, S.V. Kalinin, V.R. Paduri, J.A. Hachtel, Y. Zhang, C. Trautmann, W. J. Weber, and R. Sachan. (2024). Nanoscale core–shell structure and recrystallization of swift heavy ion tracks in SrTiO3. Nanoscale, 16 (30): 14366-14377. DOI: https://doi.org/10.1039/D4NR01974A

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​75. Zhai, Z., S.Y. Schmid, Z. Lin, S. Zhang, and F. Jiao. (2024). Unveiling the Nanoscale Architectures and Dynamics of Protein Assembly with in situ Atomic Force Microscopy. Aggregate, 5 (5): e604. DOI: https://doi.org/10.1002/agt2.604 ​

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​74. Ziatdinov, M.A., M.Y. Yaman, Y. Liu, D. Ginger, and S.V. Kalinin. (2024). Semi-supervised learning of images with strong rotational disorder: Assembling nanoparticle libraries. Digital Discovery, 3 (6): 1213-1220.

DOI: https://doi.org/10.1039/D3DD00196B   

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​​73. Shao L., D. Hu, SL. Zheng, TKH. Trinh, W. Zhou, H. Wang, Y. Zong, C. Li, and C-L. Chen. (2024). Hierarchical Self-Assembly of Multidimensional Functional Materials from Sequence-Defined Peptoids. Angew. Chem. Int. Ed, 63 (24): e202403263. DOI: https://doi.org/10.1002/anie.202403263

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72. Zheng, R., M. Zhao, JS. Du, TR. Sudarshan, Y. Zhou, AK. Paravastu, J.J. De Yoreo, A.L. Ferguson, and C-L. Chen. (2024) Assembly of short amphiphilic peptoids into nanohelices with controllable supramolecular chirality. Nat Commun, 15 (1): 3264. DOI: https://doi.org/10.1038/s41467-024-46839-y 

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71. Shen, H., E.M. Lynch, S. Akkineni, J.L. Watson, J. Decarreau, N.P. Bethel, I. Benna, W. Sheffler, D. Farrell, F. DiMaio, E. Derivery, J.J. De Yoreo, J. Kollman, and D. Baker. (2024). De novo design of pH-responsive self-assembling helical protein filaments. Nature Nanotechnology, 19 (7): 1016–1021. DOI: https://doi.org/10.1038/s41565-024-01641-1

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​70. Cai, Y., N.Y. Naser, J. Ma, and F. Baneyx. (2024). Precision loading and delivery of molecular cargo by size-controlled coacervation of gold nanoparticles functionalized with elastin-like peptides. Biomacromolecules, 25 (4): 2390-2398.  DOI: https://doi.org/10.1021/acs.biomac.3c01312

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​69. Lo, S., S.G. Baird, J. Schrier, B. Blaiszik, N. Carson, I. Foster, A. Aguilar-Granda, S.V. Kalinin, B. Maruyama, and M. Politi (2024). Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept. Digital Discovery, 3 (5): 842-868. DOI: https://doi.org/10.1039/D3DD00223C 

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​68. Smith, B.R., B. Pant, Y. Liu, J-C. Yang, S. Jesse, A. Khojandi, S.V. Kalinin, Y. Cao, and R.K. Vasudevan. (2024). Physics-informed models of domain-wall dynamics as a route for autonomous domain wall design via reinforcement learning. Digital Discovery, 3 (3): 456-466. DOI: https://doi.org/10.1039/D3DD00126A

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67. Du, J., Y. Bae, and J.J. De Yoreo. (2024). Non-classical crystallization in soft and organic materials. Nature Reviews Materials, 9 (4): 229–248. DOI: https://doi.org/10.1038/s41578-023-00637-y

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​66. Cai, Y., X. Qi, J. Boese, Y. Zhao, B. Hellner, J. Chun, C. Mundy, and F. Baneyx. (2024). Towards predictive control of reversible nanoparticle assembly with solid-binding proteins. Soft Matter, 20 (8): 1935-1942. DOI: https://doi.org/10.1039/D4SM00094C

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65. M. Valleti, R.K. Vasudevan, M.A. Ziatdinov, and S.V. Kalinin. (2024). Deep kernel methods learn better: From cards to process optimization. Machine Learning Science & Technology, 5 (1): 015012. DOI: https://10.1088/2632-2153/ad1a4f

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64. Torkelson, K., N.Y. Naser, X. Qi, Z. Li, W. Yang, K. Pushpavanam, C-L. Chen, F. Baneyx, and J. Pfaendtner. (2024). Rational design of novel biomimetic sequence-defined polymers for mineralization applications. Chemistry of Materials, 36 (2): 786-794. DOI: https://doi.org/10.1021/acs.chemmater.3c02216

 

63. Davila-Hernandez, F.A., B. Jin, H. Pyles, S. Zhang, Z. Wang, T.F. Huddy, A.K. Bera, A. Kang, C-L. Chen, J.J. De Yoreo, and D. Baker. (2023). Directing polymorph specific calcium carbonate formation with de novo protein templates. Nature Communications, 14 (1): 8191. DOI: https://doi.org/10.1038/s41467-023-43608-1

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62. Ziatdinov, M, C.Y.T. Wong, and S.V. Kalinin. (2023). Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders. Machine Learning: Science and Technology, 4 (4): 045033. DOI: https://doi.org/10.1088/2632-2153/ad073b

 

61. Lachowski, K.J., HT. Chiang, K. Torkelson, W. Zhou, S. Zhang, J. Pfaendtner, and LD. Pozzo. (2023). Anisotropic gold nanomaterial synthesis using peptide facet specificity and timed intervention. Langmuir, 39 (45): 15878-15888. DOI: https://doi.org/10.1021/acs.langmuir.3c01577​​

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60. Yang, W., B. Cai, K.J. Lachowski, Q. Yin, J.J. De Yoreo, L.D. Pozzo, and C-L. Chen. (2023). Insights into the biomimetic synthesis of 2D ZnO nanomaterials through peptoid engineering. Journal of Physical Chemistry Letters, 14 (43): 9732-9739. DOI: https://doi.org/10.1021/acs.jpclett.3c01882

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​59. Jin, B., Y. Chen, J. Tao, K.J. Lachowski, M.E. Bowden, Z. Zhang, L.D. Pozzo, N.M. Washton, K.T. Mueller, and J.J. De Yoreo. (2023). Multi-step nucleation of a crystalline silicate framework via a structurally precise prenucleation cluster. Angewandte Chemie, 62 (28): e202303770. DOI: https://doi.org/10.1002/anie.202303770

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​58. Schmid, S.Y., K. Lachowski, HT. Chiang, L. Pozzo, J.J. De Yoreo, and S. Zhang. (2023). Mechanisms of biomolecular self-assembly investigated through in situ observations of structures and dynamics. Angewandte Chemie, 62 (48): e202309725. DOI: https://doi.org/10.1002/anie.202309725

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​57. Larson, H. and B. Cossairt. (2023). Indium-poly(carboxylic acid) ligand interactions modify InP quantum dot nucleation and growth. Chemistry of Materials, 35 (15): 6152-6160. DOI: https://doi.org/10.1021/acs.chemmater.3c01309

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56. Alamdari, S. and J. Pfaendtner. (2023). Origins of conformational heterogeneity in peptoid helices formed by chiral N-1 phenylethyl sidechains. Journal of Physical Chemistry B, 127 (27): 6163-6170.

DOI: https://doi.org/10.1021/acs.jpcb.3c02576

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55. Alamdari, S., K. Torkelson, X. Wang, C-L. Chen, A.L. Ferguson, and J. Pfaendtner. (2023). Thermodynamic basis for stabilization of helical peptoids by chiral sidechains. Journal of Physical Chemistry B, 127 (27): 6171-6183.

DOI: https://doi.org/10.1021/acs.jpcb.3c01913

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​54. Zhao, M., S. Zhang, R. Zheng, S. Alamdari, C.J. Mundy, J. Pfaendtner, L. Pozzo, C-L. Chen, J.J. De Yoreo, and A.L. Ferguson. (2023). Computational and experimental determination of the properties, structure, and stability of peptoid nanosheets and nanotubes. Biomacromolecules, 24 (6): 2618-2632. DOI: https://doi.org/10.1021/acs.biomac.3c00107

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53. Yaman, M.Y., S.V. Kalinin, K.N. Guye, D.S. Ginger, and M. Ziatdinov. (2023). Learning and predicting photonic responses of plasmonic nanoparticle assemblies via dual variational autoencoders. Small, 19 (25): 2205893.

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