Browsing by Author "Fang, Lincan"

Now showing 1 - 7 of 7
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    Core-Selective Silver-Doping of Gold Nanoclusters by Surface-Bound Sulphates on Colloidal Templates: From Synthetic Mechanism to Relaxation Dynamics
    (WILEY-VCH VERLAG, 2023-01-04) Chandra, Sourov; Sciortino, Alice; Shandilya, Shruti; Fang, Lincan; Chen, Xi; Nonappa; Jiang, Hua; Johansson, Leena Sisko; Cannas, Marco; Ruokolainen, Janne; Ras, Robin H.A.; Messina, Fabrizio; Peng, Bo; Ikkala, Olli; Department of Applied Physics; Department of Bioproducts and Biosystems; Center of Excellence in Life-Inspired Hybrid Materials, LIBER; Molecular Materials; Computational Electronic Structure Theory; NanoMaterials; Bioproduct Chemistry; Soft Matter and Wetting; University of Palermo
    Ultra-small luminescent gold nanoclusters (AuNCs) have gained substantial interest owing to their low photobleaching and high biocompatibility. While the substitution of silver for gold at the central core of AuNCs has shown significant augmentation of photoluminescence with enhanced photostability, selective replacement of the central atom by silver is, however, energetically inhibited. Herein, a new strategy for in situ site-selective Ag-doping exclusively at the central core of AuNCs using sulphated colloidal surfaces as the templates is presented. This approach exceedingly improves the photoluminescence quantum efficiency of AuNCs by eliminating nonradiative losses in the multi-step relaxation cascade populating the emissive state. Density functional theory predicts the mechanism of specific doping at the central core, endorsing the preferential bonding between Ag+ ions and sulphates in water. Finally, the generic nature of the templating concept to allow core-specific doping of nanoclusters is unraveled.
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    Efficient Amino Acid Conformer Search with Bayesian Optimization
    (AMERICAN CHEMICAL SOCIETY, 2021-03-09) Fang, Lincan; Makkonen, Esko; Todorović, Milica; Rinke, Patrick; Chen, Xi; Department of Applied Physics; Computational Electronic Structure Theory; Computational Soft and Molecular Matter
    Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.
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    Elucidation of protein-ligand interactions by multiple trajectory analysis methods
    (Royal Society of Chemistry, 2024-02-05) Wu, Nian; Zhang, Ruotian; Peng, Xingang; Fang, Lincan; Chen, Kai; Jestilä, Joakim S.; Department of Applied Physics; Surfaces and Interfaces at the Nanoscale; Tsinghua University; Zhejiang University
    The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.
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    Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization
    (AMERICAN CHEMICAL SOCIETY, 2023-02-13) Fang, Lincan; Guo, Xiaomi; Todorovic, Milica; Rinke, Patrick; Chen, Xi; Department of Applied Physics; Computational Electronic Structure Theory; Tsinghua University; University of Turku
    Finding low-energy conformers of organic molecules is a complex problem due to the flexibilities of the molecules and the high dimensionality of the search space. When such molecules are on nanoclusters, the search complexity is exacerbated by constraints imposed by the presence of the cluster and other surrounding molecules. To address this challenge, we modified our previously developed active learning molecular conformer search method based on Bayesian optimization and density functional theory. Especially, we have developed and tested strategies to avoid steric clashes between a molecule and a cluster. In this work, we chose a cysteine molecule on a well-studied gold–thiolate cluster as a model system to test and demonstrate our method. We found that cysteine conformers in a cluster inherit the hydrogen bond types from isolated conformers. However, the energy rankings and spacings between the conformers are reordered.
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    Machine Learning for Structure Search of Ligand-protected Nanoclusters
    (Aalto University, 2024) Fang, Lincan; Xi, Chen, Prof., Lanzhou University, China; Teknillisen fysiikan laitos; Department of Applied Physics; Perustieteiden korkeakoulu; School of Science; Rinke, Patrick, Prof., Aalto University, Department of Applied Physics, Finland
    Understanding the atomic structures of ligand-protected nanoclusters is essential for their application in various fields. These structures not only determine the physical and chemical properties of ligand-protected nanoclusters but also play a crucial role in their stability and reactivity. Knowing the precise atomic structures allows us to tailor nanoclusters for specific functions. However, because of the extraordinarily high dimensionality of the search space which encompasses an exceptionally large number of all potential structures, it is difficult to use quantum mechanical methods, such as the density functional theory, to find the low-energy structures of ligand-protected nanoclusters. On this point, the structure search of ligand-protected nanoclusters could be more efficient and accurate by utilizing machine learning methods. In this dissertation, I developed machine learning methods to search the atomic structures of ligand-protected nanoclusters by decomposing the problem into three steps. For the first step, I developed a molecular conformer search procedure based on Bayesian optimization to search the structures of isolated molecules. Using four amino acids as examples, I showed that the procedure is both efficient and accurate. For the second step, I modified the procedure to search the structures of a single ligand on a nanocluster. I also developed and tested strategies to avoid steric clashes between a ligand and cluster parts. Moreover, I tested and demonstrated our modified procedure by searching structures for a cysteine molecule on a well-studied gold-thiolate cluster. As a result, I found that cysteine conformers in a cluster inherit the hydrogen bond types from isolated conformers, while the energy rankings and spacings between the conformers are reordered. In the final step, I applied a machine learning method based on kernel rigid regression (KRR) models to relax the structures of ligand-protected nanoclusters. Moreover, I used an active learning workflow to enhance the relaxation performance of the KRR models. To test and demonstrate our method, I applied it to search structures of Au25(Cys)18 -. We found that the low-energy structures with IItype hydrogen bonds (OH- -N, OH from trans-COOH and N from NH2) are dominant and the different configurations of the ligand layer indeed influence the properties of the clusters.
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    Machine-learning accelerated structure search for ligand-protected clusters
    (American Institute of Physics, 2024-03-07) Fang, Lincan; Laakso, Jarno; Rinke, Patrick; Chen, Xi; Department of Applied Physics; Computational Electronic Structure Theory; Computational Soft and Molecular Matter
    Finding low-energy structures of ligand-protected clusters is challenging due to the enormous conformational space and the high computational cost of accurate quantum chemical methods for determining the structures and energies of conformers. Here, we adopted and utilized a kernel rigid regression based machine learning method to accelerate the search for low-energy structures of ligand-protected clusters. We chose the Au25(Cys)18 (Cys: cysteine) cluster as a model system to test and demonstrate our method. We found that the low-energy structures of the cluster are characterized by a specific hydrogen bond type in the cysteine. The different configurations of the ligand layer influence the structural and electronic properties of clusters.
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    Molecular Conformer Search with Low-Energy Latent Space
    (AMERICAN CHEMICAL SOCIETY, 2022-07-12) Guo, Xiaomi; Fang, Lincan; Xu, Yong; Duan, Wenhui; Rinke, Patrick; Todorović, Milica; Chen, Xi; Department of Applied Physics; Computational Electronic Structure Theory; Computational Soft and Molecular Matter; Tsinghua University; University of Turku
    Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with 5-9 searching dimensions. Our results agree with previous studies.