Molecular Conformer Search with Low-Energy Latent Space

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

Core‐Selective Silver‐Doping of Gold Nanoclusters by Surface‐Bound Sulphates on Colloidal Templates: From Synthetic Mechanism to Relaxation Dynamics

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Efficient Amino Acid Conformer Search with Bayesian Optimization

Funding Information: This work was supported by the Academy of Finland (project numbers 308647, 314298, and 316601) and through their Flagship program: Finnish Center for Artificial Intelligence FCAI. We thank CSC, the Finnish IT Center for Science, and Aalto Science IT for computational resources. This work is supported by COST (European Cooperation in Science and Technology) Action 18234. L.F. thanks Guoxu Zhang, Marc Dvorak, Jingrui Li, and Annika Stuke for the help with FHI-Aims. He also acknowledges financial support from the Chinese Scholarship Council (grant no. [2017]3109). 1 Publisher Copyright: © 2020 American Chemical Society. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.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.Peer reviewe

Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization

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.Peer reviewe

Molecular Conformer Search with Low-Energy Latent Space

Funding Information: X.G., L.F. and X.C. acknowledge the financial support from the Academy of Finland (project numbers 308647, 314298, 335571). X.G, Y.X. and W.D. acknowledge the financial support from the Basic Science Center Project of NSFC (grant no. 51788104), the National Science Fund for Distinguished Young Scholars (grant no. 12025405), the National Natural Science Foundation of China (grant no. 11874035), and the Beijing Advanced Innovation Center for Future Chip (ICFC), M.T. and P.R. have received funding from the Academy of Finland via the Artificial Intelligence for Microscopic Structure Search (AIMSS) project no. 316601 and the Flagship programme: Finnish Center for Artificial Intelligence FCAI. X.G. and X.C. Generous computational resources were provided by CSC – IT Center for Science, Finland, and the Aalto Science-IT project. L.F. also acknowledges financial support from the Chinese Scholarship Council (grant no. [2017]3109). Publisher Copyright: © 2022 The Authors. Published by American Chemical Society.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.Peer reviewe

Core-Selective Silver-Doping of Gold Nanoclusters by Surface-Bound Sulphates on Colloidal Templates: From Synthetic Mechanism to Relaxation Dynamics

Funding Information: This work was carried out under the ERC Advanced grant (DRIVEN, ERC‐2016‐AdG‐742829), Academy of Finland's Centre of Excellence in Life‐Inspired Hybrid Materials (LIBER, 346108), Academy of Finland (No. 321443, 328942, 308647, and 318891) and Photonic Research and Innovation (PREIN) as well as FinnCERES flagships. L.F. and X.C. thanks for support from CSC (IT Center for Science, Finland) for providing computation resources. The authors acknowledge the provision of facilities and technical support by Aalto University OtaNano – Nanomicroscopy Center (Aalto‐NMC). | openaire: EC/H2020/742829/EU//DRIVENUltra-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.Peer reviewe