[{"content":" Background # Cells organise a surprising amount of their chemistry without membranes. Stress granules, nucleoli and a range of other compartments form when proteins and nucleic acids demix into a dense phase — liquid droplets that concentrate some molecules and exclude others, assemble in seconds and dissolve just as quickly. When that process goes wrong, the same droplets can age into the solid, fibrillar material associated with neurodegenerative disease.\nThis sits squarely in the blind spot of all-atom simulation. A condensate is micrometres across and holds thousands of chains, and the demixing plays out over milliseconds to seconds. Explicit-solvent molecular dynamics, at its best, follows a handful of chains for microseconds. That gap is not something faster hardware closes: it is several orders of magnitude in both length and time.\nCoarse-graining closes it by discarding what phase behaviour turns out not to depend on. Most condensate-forming proteins are intrinsically disordered, so there is no folded structure whose atomic detail has to be preserved — what sets the phase diagram is the pattern of charged, aromatic and hydrophobic residues along the sequence. A one-bead-per-residue representation with implicit solvent keeps exactly that pattern and drops the rest, and the saving is the difference between watching two chains touch and computing a converged coexistence curve from a slab of several hundred.\nThe point of that saving is not speed for its own sake. It is that the questions become the ones experiments actually ask. How far does swapping arginine for lysine move the saturation concentration? Which spacing of aromatic residues holds a droplet together, and which lets it dissolve? What does a disease-associated mutation do to the phase boundary? Every one of these is a scan across tens or hundreds of sequence variants — routine at residue resolution, hopeless atomistically.\nWhat those answers are worth depends on the model underneath them, which is why this work runs alongside our coarse-grained model development: a phase diagram computed with a potential that was never tested away from its reference state point is a prediction with an asterisk on it.\nApproaches used to investigate biomolecular condensates ","externalUrl":null,"permalink":"/zhulab/research/biomolecular-condensates/","section":"Research","summary":"Sequence-resolved simulation of how proteins demix into membraneless compartments, and what tips a liquid droplet towards a solid.","title":"Biomolecular condensates","type":"research"},{"content":" Dr Qiang Zhu is a computational chemist and biophysicist dedicated to advancing molecular simulations for solving challenging problems in chemistry, biology, and medicine. His research combines theoretical modelling, high-performance computing, and artificial intelligence to understand how molecules assemble, interact, and function in complex environments.\nHis current research focuses on biomolecular condensates, protein aggregation, drug discovery, and multiscale simulation method development. By creating new computational tools and integrating physics-based and AI-driven approaches, he seeks to reveal molecular mechanisms that are difficult to access experimentally and to accelerate the translation of fundamental discoveries into biomedical and technological applications.\nDr Zhu earned his PhD from Nanjing University and completed postdoctoral training at both Nanjing University and the University of California, Irvine. He is currently a Vice-Chancellor\u0026rsquo;s Research Fellow at the University of Wollongong, where he leads research on computational methods for biomolecular systems and therapeutic discovery.\n","externalUrl":null,"permalink":"/zhulab/team/qiang-zhu/","section":"Team","summary":"","title":"Dr Qiang Zhu","type":"team"},{"content":"","externalUrl":null,"permalink":"/zhulab/resources/grants/","section":"Resources","summary":"Funding that supports the group’s work.","title":"Grants","type":"resources"},{"content":" A short paragraph about this project, co-supervised with Prof. Haibo Yu.\n","externalUrl":null,"permalink":"/zhulab/team/harsh/","section":"Team","summary":"","title":"Harshvardhan Pande","type":"team"},{"content":" Background # A coarse-grained model replaces groups of atoms with single interaction sites and asks what effective forces should act between them. There are two ways to answer that. The common one is top-down: pick a functional form, tune its parameters until the model reproduces a chosen experimental observable, and ship it. The other is bottom-up: derive the effective interactions from atomistic simulations of the same system, so that the coarse model inherits its parameters from a more detailed description rather than from a fitting target. We work on the second.\nThe appeal of the bottom-up route is that it is, in principle, systematic — force matching, relative-entropy minimisation and iterative Boltzmann inversion all define a well-posed thing to compute. The difficulty is that the object being computed is not a potential energy but a free energy: every degree of freedom that was integrated out leaves its entropy behind, folded into the effective interaction. That has a consequence people underestimate. A free-energy-derived potential belongs to the state point it was derived at. Change the temperature, the concentration or the composition and there is no guarantee it still applies.\nThis is where coarse-grained models quietly fail. A potential matched to single-chain structure need not reproduce multi-chain thermodynamics — matching the pair correlation function does not fix the pressure or the phase behaviour. Implicit solvent makes it sharper still: the solvent entropy is temperature-dependent, so the effective interactions are too, and temperature is exactly the axis along which phase diagrams are drawn.\nOur interest is in both halves of that problem — deriving the interactions carefully, and then mapping the boundary of where they hold. A model that is honest about its own domain of validity is more useful than one that is slightly more accurate at a single state point.\nSchematic illustration of the time and length scale of computational methods utilized for addressing the biomolecular condensates. ","externalUrl":null,"permalink":"/zhulab/research/coarse-grained-models/","section":"Research","summary":"Deriving coarse-grained interactions bottom-up from atomistic reference data, and mapping the conditions under which they stop being predictive.","title":"Coarse-grained model development","type":"research"},{"content":"","externalUrl":null,"permalink":"/zhulab/resources/code/","section":"Resources","summary":"Open-source tools and data released with our papers.","title":"Code","type":"resources"},{"content":" A short paragraph about this project, co-supervised with Prof. Haibo Yu.\n","externalUrl":null,"permalink":"/zhulab/team/yutong/","section":"Team","summary":"","title":"Yutong Jiao","type":"team"},{"content":" Background # A classical force field is a fixed functional form with parameters fitted to a limited set of reference data, and it fails in characteristic ways on the properties it was never fitted to. Shear viscosity is a good example: standard all-atom parameters systematically underestimate the viscosity of alcohols, because the intermolecular hydrogen bonding that dominates their dynamics is not what the parameters were tuned against. Partition coefficients are another: what a molecule does at a water–octanol interface depends on how its charge distribution responds to the surrounding phase, and a fixed-charge model has no way to express that.\nMachine learning is the obvious lever, and the obvious trap. A neural network potential can fit almost anything, which means it can also fit the wrong thing convincingly — reproducing a training set while carrying no transferable physics and offering no way to see where it will break. Our interest is in the narrower and more useful version of the idea: build the physics into the representation, and use learning where a human fitting by hand would be guessing.\nOne direction is descriptors that encode what the model is missing. Atom-centred symmetry functions describe a local environment through geometry alone; by weighting them with the atomic partial charge and averaging them over a Boltzmann-weighted ensemble of conformers, polarization and conformational entropy enter the representation explicitly rather than being inferred. Trained on measured partition coefficients and summed over atomic contributions, that gives a model that transfers to molecules and datasets it never saw — and one whose predictions can be read back atom by atom.\nThe other direction runs the opposite way: use learning to interrogate a classical model rather than replace it. Across seventy-five organic liquids spanning nine orders of magnitude in viscosity, feature selection pointed squarely at hydrogen bonding and molecular topology, and showed that the van der Waals term — not the torsional one — was the part carrying the error. Varying that single term systematically was enough to bring the difficult liquids into line. The lesson generalises: knowing which parameter is wrong is worth more than a black box that is right on average.\nBoth threads feed the same question that runs through our coarse-grained model development — how to build effective interactions that stay predictive away from the conditions they were derived at, and how to know when they stop.\nSelected outputs # Q. Zhu et al., \u0026ldquo;Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions\u0026rdquo;, Phys. Chem. Chem. Phys. 24 (2022). 10.1039/D2CP02648A Q. Zhu et al., \u0026ldquo;Shear viscosity prediction of alcohols, hydrocarbons, halogenated, carbonyl, nitrogen-containing, and sulfur compounds using the variable force fields\u0026rdquo;, J. Chem. Phys. 154, 074502 (2021). 10.1063/5.0038267 ","externalUrl":null,"permalink":"/zhulab/research/machine-learning/","section":"Research","summary":"Learning representations that carry physics rather than hide it, and using learning to find out which parts of a classical force field are actually wrong.","title":"Machine learning for molecular models","type":"research"},{"content":"","date":"2 November 2026","externalUrl":null,"permalink":"/zhulab/news/","section":"News","summary":"","title":"News","type":"news"},{"content":"","date":"2 November 2026","externalUrl":null,"permalink":"/zhulab/tags/opportunities/","section":"Tags","summary":"","title":"Opportunities","type":"tags"},{"content":"I am thrilled to announce that a PhD position is now available in my research group, with the project primarily focusing on RNA-related research.\nI am very grateful for the support from our supervisory team, Prof Haibo Yu (UOW), Dr Dzung Do-Ha (UOW), and Dr Yi Shen (USyd), in making this opportunity possible.\nIf you are interested in this opportunity and would like to learn more about the project, please feel free to reach out to Haibo or me. We would be very happy to hear from you!\nA flyer detailing such program is attached here for your convenience.\n","date":"2 November 2026","externalUrl":null,"permalink":"/zhulab/news/2026-11-phd-opportunity/","section":"News","summary":"","title":"PhD Position Available in My Research Group","type":"news"},{"content":"","date":"2 November 2026","externalUrl":null,"permalink":"/zhulab/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"Molecular simulation gives an exact picture of a system one atom at a time — and almost never at the scale a real material lives on. Our group builds the bridge. We derive coarse-grained representations from atomistic data, test how far they can be trusted, and use them to study self-assembly, transport and phase behaviour in soft and hybrid materials.\nThe work sits between statistical mechanics, applied mathematics and software engineering, and everything we build is released openly.\n","date":"2 November 2026","externalUrl":null,"permalink":"/zhulab/","section":"Zhu Group","summary":"","title":"Zhu Group","type":"page"},{"content":"","date":"9 October 2026","externalUrl":null,"permalink":"/zhulab/tags/funding/","section":"Tags","summary":"","title":"Funding","type":"tags"},{"content":"Fortunately, Dr Qiang Zhu has been awarded A$30,000 through the Early and Mid-Career Researcher (EMCR) Near-Miss Funding Initiative.\nCongratulations, Qiang, on this well-deserved achievement!\n","date":"9 October 2026","externalUrl":null,"permalink":"/zhulab/news/2026-10-uow-near-miss-grant/","section":"News","summary":"","title":"UOW the Early and Mid-Career Researcher (EMCR) Near-Miss Funding Initiative Awarded","type":"news"},{"content":"","date":"15 September 2026","externalUrl":null,"permalink":"/zhulab/tags/internship/","section":"Tags","summary":"","title":"Internship","type":"tags"},{"content":"I\u0026rsquo;m excited to be offering a project through the 2027 QUBIC Undergraduate Summer Internship Program, co-mentored by Prof. Haibo Yu.\nThis project will give an undergraduate student the opportunity to work at the frontier of quantum biotechnology research on exploring how molecular chirality influences amyloid fibril formation, gaining hands-on experience in molecular dynamics simulation, data analysis, and scientific visualisation, while contributing to research relevant to Alzheimer\u0026rsquo;s disease.\nThe program runs for four weeks from January to February 2027 and is open to domestic undergraduate students currently in their second to final year of study.\nIf you\u0026rsquo;re curious about research, looking to develop practical skills, and interested in gaining experience at the intersection of quantum technologies and biotechnology, we\u0026rsquo;d love to hear from you.\n👉 Learn more about the project and apply: https://lnkd.in/gSQG3UdH\nApplications close 16 October.\n#QUBIC #QuantumTechnologies #BringingQuantumToLife #LifeChangingTechnologies #Internship\nUndergraduate Summer Internship Program ","date":"15 September 2026","externalUrl":null,"permalink":"/zhulab/news/2026-09-qubic-internship-available/","section":"News","summary":"","title":"Undergraduate Summer Internship Available through QUBIC","type":"news"},{"content":"It is my great pleasure to host Prof Yilun Ying from Nanjing University (my Alma Mater). She gave us a talk titled \u0026ldquo;Heteromeric Nanopores for Single-Peptide Sequencing\u0026rdquo;.\nProf Ying is giving a seminar to Molecular Horizon ","date":"9 September 2026","externalUrl":null,"permalink":"/zhulab/news/2026-09-hosting-yilun-ying/","section":"News","summary":"","title":"Hosting Prof Yilun Ying for Molecular Horizon Seminar","type":"news"},{"content":"","date":"9 September 2026","externalUrl":null,"permalink":"/zhulab/tags/seminar/","section":"Tags","summary":"","title":"Seminar","type":"tags"},{"content":"Dr Qiang Zhu and Dr Dzung Do-Ha have been awarded A$5,000 through the Molecular Horizons ECR Collaborative Grant scheme.\nOur thanks to Prof. Haibo Yu and Dr Dezerae Cox for their support of the application.\n","date":"6 August 2026","externalUrl":null,"permalink":"/zhulab/news/2026-08-mh-ecr-grant/","section":"News","summary":"","title":"Molecular Horizons ECR Collaborative Grant awarded","type":"news"},{"content":"Our work on the impact of N-terminal chirality and sequence variation on amyloid-β peptides has been published in the Journal of Chemical Information and Modeling.\nDOI: 10.1021/acs.jcim.6c01001\n","date":"24 July 2026","externalUrl":null,"permalink":"/zhulab/news/2026-07-jcim-amyloid-chirality/","section":"News","summary":"","title":"New paper in JCIM on the chirality effect in amyloid-β peptides","type":"news"},{"content":"","date":"24 July 2026","externalUrl":null,"permalink":"/zhulab/tags/publication/","section":"Tags","summary":"","title":"Publication","type":"tags"},{"content":"","date":"15 April 2026","externalUrl":null,"permalink":"/zhulab/tags/people/","section":"Tags","summary":"","title":"People","type":"tags"},{"content":"A warm welcome to Harshvardhan Pande and Yutong Jiao, who have both started PhDs with the group.\n","date":"15 April 2026","externalUrl":null,"permalink":"/zhulab/news/2026-04-welcome/","section":"News","summary":"","title":"Two new PhD students join the group","type":"news"},{"content":"","externalUrl":null,"permalink":"/zhulab/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"Group life: retreats, conferences, celebrations, and the occasional whiteboard that deserved a photo.\n","externalUrl":null,"permalink":"/zhulab/gallery/","section":"Gallery","summary":"","title":"Gallery","type":"gallery"},{"content":" We are always interested in hearing from people who like building models and writing code, whatever their background — chemistry, physics, maths, materials or computer science.\nPhD students # UOW and the Australian Government offer competitive scholarships for domestic and international candidates, covering tuition and a living stipend. Applications are assessed twice a year.\nTo apply, send a CV, an academic transcript, and a short note on which of our research directions interests you and why. A paragraph about something you have built or debugged is worth more than a list of grades.\nPostdoctoral researchers # There are currently no funded postdoctoral positions open. If you are applying for a fellowship and would like to host it here, get in touch early — proposals take time to put together properly.\nHonours \u0026amp; masters projects # UOW students are welcome to ask about projects at any point in the year. It helps to mention which subjects you have enjoyed and whether you have written code before.\nContact # Email: qiangzhu@uow.edu.au\nSchool of Chemistry and Molecular Bioscience University of Wollongong, Northfields Ave, Wollongong NSW 2522, Australia\n","externalUrl":null,"permalink":"/zhulab/join/","section":"Join us","summary":"","title":"Join us","type":"join"},{"content":" Group members are shown in bold. Preprints and code are linked where they exist; if you can\u0026rsquo;t reach a paper, email us and we\u0026rsquo;ll send a copy.\n2026 Deep-Penetrating Transdermal Lipopeptide Liposomes for Sustained IL-17 Inhibition and Prevention of Psoriatic Recurrence R. Cong, S. Zhou, L. Li, F. Li, J. Ren, Q. Zhu, H. Yang, J. Tu, X. Jiang, L. Jiang\nJournal of the American Chemical Society 148, 27884–27899 (2026) \u0026middot; DOI \u0026middot; mechanisms understanding Firefly-Geni: Automated Iteration-Guided Generative Discovery of TADF Molecules B. Zhang, X. Fang, D. Yang, Y. Wang, Y. Liu, Y. Xia, Q. Zhu, H. Yu, M. Zhu, J. Wen\n(2026) \u0026middot; DOI \u0026middot; machine learning Mechanistic Insights into Nanobody-Based Quenchbody Sensing via Structural Modeling and Molecular Simulations N. S. El Salamouni, J. H. Cater, Q. Zhu, L. M. Spenkelink, H. Yu\nJournal of Chemical Information and Modeling 66, 2879–2888 (2026) \u0026middot; DOI \u0026middot; mechanisms understanding N-Terminal Chirality and Sequence Variations Modulate the Conformational Landscape of Amyloid-Beta 42 Q. Zhu, H. Yu\nJournal of Chemical Information and Modeling 66, 10156–10167 (2026) \u0026middot; DOI \u0026middot; Code \u0026middot; mechanisms understanding Relay Storage of Protons and Zinc Ions Enables Practical High‐Mass‐Loading Organic Electrodes X. Yu, S. Yuan, L. Yan, T. Kong, K. Zhou, Y. Qi, Q. Zhu, Z. Li, P. Wang, S. Gu, J. Ma, Y. Wang\nAngewandte Chemie 138 (2026) \u0026middot; DOI \u0026middot; mechanisms understanding 2025 Automated Refinement of Property-Specific Polarizable Gaussian Multipole Water Models Using Bayesian Black-Box Optimization Y. Wu, Q. Zhu, Z. Huang, P. Cieplak, Y. Duan, R. Luo\nJournal of Chemical Theory and Computation 21, 3563–3575 (2025) \u0026middot; DOI \u0026middot; machine learning Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches Q. Zhu, Z. Raza, D. Do‐Ha, E. De Costa, P. Sasheva, L. McAlary, H. Mahmodi, W. P. Bowen, L. Ooi, I. Kabakova, H. Yu\nAdvanced Materials 37 (2025) \u0026middot; DOI \u0026middot; biomolecular condensatescoarse-grained Bridging Dimensionality Reduction and Stochastic Sampling: The DA2-MC Algorithm for Protein Dynamics R. Shen, Q. Zhu, L. Hu, J. Ma, W. Wang, H. Dong\nThe Journal of Physical Chemistry Letters 16, 4788–4795 (2025) \u0026middot; DOI \u0026middot; machine learningenhanced sampling Coarse-grained models for ionic liquids and applications to biological and electrochemical systems Y. Ge, Q. Zhu, X. Wang, J. Ma\nIndustrial Chemistry \u0026amp;amp; Materials 3, 383–411 (2025) \u0026middot; DOI \u0026middot; machine learningcoarse-grained Decoding Hairpin Structure Stability in Lin28-Mediated Repression Q. Zhu, L. Hu, C. Cui, M. Zang, H. Dong, J. Ma\nBiochemistry 64, 1276–1284 (2025) \u0026middot; DOI \u0026middot; mechanisms understanding End-to-End Modeling of Reaction Field Energy Using Data-Driven Geometric Graph Neural Networks Y. Wu, Q. Zhu, R. Luo\nJournal of Chemical Theory and Computation 21, 9710–9725 (2025) \u0026middot; DOI \u0026middot; machine learningproperties prediction External electric field control of charge transport in Y-shaped and linear non-fullerene acceptors L. Fu, J. Hu, R. Li, H. Geng, Q. Zhu, Z. Fan, B. Cheng\nJournal of Materials Chemistry A 13, 37854–37863 (2025) \u0026middot; DOI \u0026middot; mechanisms understanding LightChem: A Lightweight Domain-Specific Language Model for Molecular Property and Reaction Prediction in Chemistry Yu Zhang, J. Zhang, Y. Gu, Q. Tao, L. He, D. Li, Yijian Zhang, Dingwen Fan, Yijie Gao, J. Sun, J. Chen, R. Shi, Z. Wu, Q. Jia, L. Zheng, S. Yuan, F. Yang, Y. Chen, D. Zheng, Yilin Liu, Y. Cai, Z. Gu, C. Zhang, B. Cai, G. Li, Tianyu Zhang, Y. Zhou, Yuang Liu, Tianyue Zhang, S. Xing, Z. Li, X. Su, Dong Fan, H. Qian, X. Li, B. Zhang, Y. Xiao, X. Liu, Y. Zhu, Q. Zhu, Q. Liu, S. Chen, J. Wen, Z. Yu, G. Zeng, Y. Liang, Y. Wang, W. Xu, J. Wang, M. Yang, Yang Gao, S. Li, Y. Shi, X. Chen, H. Dong, W. Li, J. Ma, G. Wang\n(2025) \u0026middot; DOI \u0026middot; machine learningproperties prediction Proton coordination chemistry in pyrene-based anodes for ultralong-life aqueous proton batteries W. Yang, C. Zhan, Q. Zhu, L. Liu, B. Su, H. Yu, L. Zhang, L. Yan, J. Shu\nInorganic Chemistry Frontiers 12, 4213–4222 (2025) \u0026middot; DOI \u0026middot; mechanisms understanding 2024 Prediction of free radical reactions toward organic pollutants with easily accessible molecular descriptors G. Zhang, Q. Zhu, H. Zheng, S. Zhang, J. Ma\nChemosphere 346, 140660 (2024) \u0026middot; DOI \u0026middot; machine learningproperties prediction Shifting redox reaction equilibria on demand using an orthogonal redox cofactor D. Aspacio, Y. Zhang, Y. Cui, E. Luu, E. King, W. B. Black, S. Perea, Q. Zhu, Y. Wu, R. Luo, J. B. Siegel, H. Li\nNature Chemical Biology 20, 1535–1546 (2024) \u0026middot; DOI \u0026middot; mechanisms understanding Single-molecule sensing inside stereo- and regio-defined hetero-nanopores W. Liu, Q. Zhu, C.-N. Yang, Y.-H. Fu, J.-C. Zhang, M.-Y. Li, Z.-L. Yang, K.-L. Xin, J. Ma, M. Winterhalter, Y.-L. Ying, Y.-T. Long\nNature Nanotechnology 19, 1693–1701 (2024) \u0026middot; DOI \u0026middot; mechanisms understanding Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy A. U. Rehman, C. Zhao, Y. Wu, Q. Zhu, R. Luo\nInternational Journal of Molecular Sciences 25, 6201 (2024) \u0026middot; DOI \u0026middot; mechanisms understanding Understanding and fine tuning the propensity of ATP-driven liquid–liquid phase separation with oligolysine Q. Zhu, Y. Wu, R. Luo\nPhysical Chemistry Chemical Physics 26, 10568–10578 (2024) \u0026middot; DOI \u0026middot; biomolecular condensatescoarse-grained 2023 AmberTools D. A. Case, H. M. Aktulga, K. Belfon, D. S. Cerutti, G. A. Cisneros, V. W. D. Cruzeiro, N. Forouzesh, T. J. Giese, A. W. Götz, H. Gohlke, S. Izadi, K. Kasavajhala, M. C. Kaymak, E. King, T. Kurtzman, T.-S. Lee, P. Li, J. Liu, T. Luchko, R. Luo, M. Manathunga, M. R. Machado, H. M. Nguyen, K. A. O’Hearn, A. V. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, A. Risheh, S. Schott-Verdugo, A. Shajan, J. Swails, J. Wang, H. Wei, X. Wu, Y. Wu, S. Zhang, S. Zhao, Q. Zhu, T. E. Cheatham, D. R. Roe, A. Roitberg, C. Simmerling, D. M. York, M. C. Nagan, K. M. Merz\nJournal of Chemical Information and Modeling 63, 6183–6191 (2023) \u0026middot; DOI \u0026middot; method development Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds Y. Wu, H. Wei, Q. Zhu, R. Luo\nThe Journal of Physical Chemistry Letters 14, 9034–9041 (2023) \u0026middot; DOI \u0026middot; method developmentmachine learning Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids Y. Ge, X. Wang, Q. Zhu, Y. Yang, H. Dong, J. Ma\nJournal of Chemical Theory and Computation 19, 6718–6732 (2023) \u0026middot; DOI \u0026middot; method developmentmachine learningcoarse-grained Planar Chirality for Acid/Base Responsive Macrocyclic Pillararenes Induced by Amino Acid Derivatives: Molecular Dynamics Simulations and Machine Learning L. Fu, R. Wang, Q. Zhu, Y. Gu, L. Zheng, Y. Chen, J. Jiang, J. Ma\nJournal of Chemical Theory and Computation 19, 4364–4376 (2023) \u0026middot; DOI \u0026middot; machine learning Recent Advances in Biomolecular Recognition Q. Zhu, R. Luo\nInternational Journal of Molecular Sciences 24, 8310 (2023) \u0026middot; DOI Streamlining and Optimizing Strategies of Electrostatic Parameterization Q. Zhu, Y. Wu, S. Zhao, P. Cieplak, Y. Duan, R. Luo\nJournal of Chemical Theory and Computation 19, 6353–6365 (2023) \u0026middot; DOI \u0026middot; Code \u0026middot; method developmentpolarisable force field Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters Q. Zhu, Y. Ge, W. Li, J. Ma\nJournal of Chemical Theory and Computation 19, 396–411 (2023) \u0026middot; DOI \u0026middot; polarisable force field 2022 An electrostatic-variable coarse-grained model for predicting enthalpy of vaporization, surface tension, diffusivity, conductivity, and dielectric constant of aqueous ionic liquid Y. Ge, Q. Zhu, Y. Li, H. Dong, J. Ma\nJournal of Molecular Liquids 346, 118230 (2022) \u0026middot; DOI \u0026middot; method developmentcoarse-grained Batch-Scale Synthesis of Nanoparticle-Agminated Three-Dimensional Porous Cu@Cu2O Microspheres for Highly Selective Electrocatalysis of Nitrate to Ammonia M. Jiang, Q. Zhu, X. Song, Y. Gu, P. Zhang, C. Li, J. Cui, J. Ma, Z. Tie, Z. Jin\nEnvironmental Science \u0026amp;amp; Technology 56, 10299–10307 (2022) \u0026middot; DOI \u0026middot; mechanism understanding Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests Q. Jia, Y. Ni, Z. Liu, X. Gu, Z. Cui, M. Fan, Q. Zhu, Y. Wang, J. Ma\nJournal of Chemical Information and Modeling 62, 4928–4936 (2022) \u0026middot; DOI \u0026middot; method developmentmachine learningproperties prediction Identification of Single-Molecule Catecholamine Enantiomers Using a Programmable Nanopore W. Jia, C. Hu, Y. Wang, Y. Liu, L. Wang, S. Zhang, Q. Zhu, Y. Gu, P. Zhang, J. Ma, H.-Y. Chen, S. Huang\nACS Nano 16, 6615–6624 (2022) \u0026middot; DOI \u0026middot; mechanism understanding Machine learning assisted prediction of charge transfer properties in organic solar cells by using morphology-related descriptors L. Fu, H. Hu, Q. Zhu, L. Zheng, Y. Gu, Y. Wen, H. Ma, H. Yin, J. Ma\nNano Research 16, 3588–3596 (2022) \u0026middot; DOI \u0026middot; machine learning Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions Q. Zhu, Q. Jia, Z. Liu, Y. Ge, X. Gu, Z. Cui, M. Fan, J. Ma\nPhysical Chemistry Chemical Physics 24, 23082–23088 (2022) \u0026middot; DOI \u0026middot; method developmentmachine learningproperties prediction Nitrogen reduction reaction energy and pathways in metal-zeolites: deep learning and explainable machine learning with local acidity and hydrogen bonding features Y. Gu, Qin Zhu, Z. Liu, C. Fu, J. Wu, Qiang Zhu, Q. Jia, J. Ma\nJournal of Materials Chemistry A 10, 14976–14988 (2022) \u0026middot; DOI \u0026middot; mechanism understanding Surface Stability and Morphology of Calcium Phosphate Tuned by pH Values and Lactic Acid Additives: Theoretical and Experimental Study Hongwei Chen, C. Lv, L. Guo, M. Ma, X. Li, Z. Lan, J. Huo, H. Dong, X. Zhu, Q. Zhu, Y. Gu, Z. Liu, J. Liu, Hangrong Chen, X. Guo, J. Ma\nACS Applied Materials \u0026amp;amp; Interfaces 14, 4836–4851 (2022) \u0026middot; DOI \u0026middot; mechanism understanding Towards High‐Performance Aqueous Zinc Batteries via a Semi‐Conductive Bipolar‐Type Polymer Cathode L. Yan, Q. Zhu, Y. Qi, J. Xu, Y. Peng, J. Shu, J. Ma, Y. Wang\nAngewandte Chemie International Edition 61 (2022) \u0026middot; DOI \u0026middot; mechanism understanding 2021 Shear viscosity prediction of alcohols, hydrocarbons, halogenated, carbonyl, nitrogen-containing, and sulfur compounds using the variable force fields Q. Zhu, Y. Gu, L. Hu, T. Gaudin, M. Fan, J. Ma\nThe Journal of Chemical Physics 154 (2021) \u0026middot; DOI \u0026middot; machine learning 2020 A Two-Ended Data-Driven Accelerated Sampling Method for Exploring the Transition Pathways between Two Known States of Protein Y. Yuan, Q. Zhu, R. Song, J. Ma, H. Dong\nJournal of Chemical Theory and Computation 16, 4631–4640 (2020) \u0026middot; DOI \u0026middot; method developmentmachine learning 2019 A Data‐Driven Accelerated Sampling Method for Searching Functional States of Proteins Q. Zhu, Y. Yuan, J. Ma, H. Dong\nAdvanced Theory and Simulations 2 (2019) \u0026middot; DOI \u0026middot; method developmentmachine learning Direct Extracellular Electron Transfer of the Geobacter sulfurreducens Pili Relevant to Interaromatic Distances C. Shu, Q. Zhu, K. Xiao, Y. Hou, H. Ma, J. Ma, X. Sun\nBioMed Research International 2019, 1–12 (2019) \u0026middot; DOI \u0026middot; mechanism understanding 2018 On-Demand Electrical Switching of Antibody?Antigen Binding on Surfaces B. Santos Gomes, E. Cantini, S. Tommasone, J. S. Gibson, X. Wang, Q. Zhu, J. Ma, J. D. McGettrick, T. M. Watson, J. A. Preece, J. C. Kirkman-Brown, S. J. Publicover, P. M. Mendes\nACS Applied Bio Materials 1, 738–747 (2018) \u0026middot; DOI \u0026middot; mechanism understanding Synergistic steric pairing effects of terfluorenes with ternary side groups on β-conformation transition: experiments and computations X.-A. Yuan, M.-N. Yu, Q. Zhu, W.-W. Zhang, L.-H. Xie, W. Huang, J. Ma\nJournal of Materials Chemistry C 6, 1551–1561 (2018) \u0026middot; DOI \u0026middot; mechanism understanding 2017 Electrostatic Interactions of Water in External Electric Field: Molecular Dynamics Simulations Q. Zhu, Z. KAN, J. Ma\nJournal of Electrochemistry 23, 4 (2017) polarisation effects Entropy and Polarity Control the Partition and Transportation of Drug-like Molecules in Biological Membrane Q. Zhu, Y. Lu, X. He, T. Liu, H. Chen, F. Wang, D. Zheng, H. Dong, J. Ma\nScientific Reports 7 (2017) \u0026middot; DOI \u0026middot; mechanism understandingpolarisation effects Polarization Effects on the Cellulose Dissolution in Ionic Liquids: Molecular Dynamics Simulations with Polarization Model and Integrated Tempering Enhanced Sampling Method Z. Kan, Q. Zhu, L. Yang, Z. Huang, B. Jin, J. Ma\nThe Journal of Physical Chemistry B 121, 4319–4332 (2017) \u0026middot; DOI \u0026middot; mechanism understandingpolarisation effects Tuning the collective switching behavior of azobenzene/Au hybrid materials: flexible versus rigid azobenzene backbones and Au(111) surfaces versus curved Au nanoparticles C. Liu, D. Zheng, W. Hu, Q. Zhu, Z. Tian, J. Zhao, Y. Zhu, J. Ma\nNanoscale 9, 16700–16710 (2017) \u0026middot; DOI \u0026middot; mechanism understanding ","externalUrl":null,"permalink":"/zhulab/publications/","section":"Publications","summary":"","title":"Publications","type":"publications"},{"content":"Our work spans method development and application. Each direction below is an active project with students and collaborators attached.\n","externalUrl":null,"permalink":"/zhulab/research/","section":"Research","summary":"","title":"Research","type":"research"},{"content":"Funding that supports the group, and the code we release alongside our papers.\n","externalUrl":null,"permalink":"/zhulab/resources/","section":"Resources","summary":"","title":"Resources","type":"resources"},{"content":"We are a mix of chemists, physicists and software people. Everyone here writes code and everyone here talks to experimentalists.\n","externalUrl":null,"permalink":"/zhulab/team/","section":"Team","summary":"","title":"Team","type":"team"}]