Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning

As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly desirable. In particular, quantum circuits in place of classical convolutional filters for image detection-based tasks are being investigated for the ability to exploit quantum advantage. However, these attempts, referred to as quantum convolutional neural networks (QCNNs), lack the ability to efficiently process data with multiple channels and therefore are limited to relatively simple inputs. In this work, we present a variety of hardware-adaptable quantum circuit ansatzes for use as convolutional kernels, and demonstrate that the quantum neural networks we report outperform existing QCNNs on classification tasks involving multi-channel data. We envision that the ability of these implementations to effectively learn inter-channel information will allow quantum machine learning methods to operate with more complex data. This work is available as open source at https://github.com/anthonysmaldone/QCNN-Multi-Channel-Supervised-Learning

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction

Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints of complexes in the training and test sets. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/, and the HACNet Python package is available through PyPI

Density functional theory and DFT+U study of transition metal porphines adsorbed on Au(111) surfaces and effects of applied electric fields

We apply Density Functional Theory (DFT) and the DFT+U technique to study the adsorption of transition metal porphine molecules on atomistically flat Au(111) surfaces. DFT calculations using the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional correctly predict the palladium porphine (PdP) low-spin ground state. PdP is found to adsorb preferentially on gold in a flat geometry, not in an edgewise geometry, in qualitative agreement with experiments on substituted porphyrins. It exhibits no covalent bonding to Au(111), and the binding energy is a small fraction of an eV. The DFT+U technique, parameterized to B3LYP predicted spin state ordering of the Mn d-electrons, is found to be crucial for reproducing the correct magnetic moment and geometry of the isolated manganese porphine (MnP) molecule. Adsorption of Mn(II)P on Au(111) substantially alters the Mn ion spin state. Its interaction with the gold substrate is stronger and more site-specific than PdP. The binding can be partially reversed by applying an electric potential, which leads to significant changes in the electronic and magnetic properties of adsorbed MnP, and ~ 0.1 Angstrom, changes in the Mn-nitrogen distances within the porphine macrocycle. We conjecture that this DFT+U approach may be a useful general method for modeling first row transition metal ion complexes in a condensed-matter setting.Comment: 14 pages, 6 figure

Ab initio tensorial electronic friction for molecules on metal surfaces : nonadiabatic vibrational relaxation

Molecular adsorbates on metal surfaces exchange energy with substrate phonons and low-lying electron-hole pair excitations. In the limit of weak coupling, electron-hole pair excitations can be seen as exerting frictional forces on adsorbates that enhance energy transfer and facilitate vibrational relaxation or hot-electron-mediated chemistry. We have recently reported on the relevance of tensorial properties of electronic friction [M. Askerka et al., Phys. Rev. Lett. 116, 217601 (2016)] in dynamics at surfaces. Here we present the underlying implementation of tensorial electronic friction based on Kohn-Sham density functional theory for condensed phase and cluster systems. Using local atomic-orbital basis sets, we calculate nonadiabatic coupling matrix elements and evaluate the full electronic friction tensor in the Markov limit. Our approach is numerically stable and robust, as shown by a detailed convergence analysis. We furthermore benchmark the accuracy of our approach by calculation of vibrational relaxation rates and lifetimes for a number of diatomic molecules at metal surfaces. We find friction-induced mode-coupling between neighboring CO adsorbates on Cu(100) in a c(2×2) overlayer to be important for understanding experimental findings

NH\u3csub\u3e3\u3c/sub\u3e Binding to the S\u3csub\u3e2\u3c/sub\u3e State of the O\u3csub\u3e2\u3c/sub\u3e-Evolving Complex of Photosystem II: Analogue to H\u3csub\u3e2\u3c/sub\u3eO Binding during the S\u3csub\u3e2\u3c/sub\u3e → S\u3csub\u3e3\u3c/sub\u3e Transition

© 2015 American Chemical Society. Ammonia binds directly to the oxygen-evolving complex of photosystem II (PSII) upon formation of the S2 intermediate, as evidenced by electron paramagnetic resonance spectroscopy. We explore the binding mode by using quantum mechanics/molecular mechanics methods and simulations of extended X-ray absorption fine structure spectra. We find that NH3 binds as an additional terminal ligand to the dangling Mn4, instead of exchanging with terminal water. Because water and ammonia are electronic and structural analogues, these findings suggest that water binds analogously during the S2 → S3 transition, leading to rearrangement of ligands in a carrousel around Mn4

Theoretical EXAFS studies of a model of the oxygen-evolving complex of photosystem II obtained with the quantum cluster approach

The oxygen-evolving complex (OEC) of photosystem II is the only natural system that can form O2 from water and sunlight and it consists of a Mn4Ca cluster. In a series of publications, Siegbahn has developed a model of the OEC with the quantum mechanical (QM) cluster approach that is compatible with available crystal structures, able to form O2 with a reasonable energetic barrier, and has a significantly lower energy than alternative models. In this investigation, we present a method to restrain a QM geometry optimization toward experimental polarized extended X-ray absorption fine structure (EXAFS) data. With this method, we show that the cluster model is compatible with the EXAFS data and we obtain a refined cluster model that is an optimum compromise between QM and polarized EXAFS data. (C) 2012 Wiley Periodicals, Inc

Eigenvector Centrality Distribution for Characterization of Protein Allosteric Pathways

Determining the principal energy pathways for allosteric communication in biomolecules, that occur as a result of thermal motion, remains challenging due to the intrinsic complexity of the systems involved. Graph theory provides an approach for making sense of such complexity, where allosteric proteins can be represented as networks of amino acids. In this work, we establish the eigenvector centrality metric in terms of the mutual information, as a mean of elucidating the allosteric mechanism that regulates the enzymatic activity of proteins. Moreover, we propose a strategy to characterize the range of the physical interactions that underlie the allosteric process. In particular, the well known enzyme, imidazol glycerol phosphate synthase (IGPS), is utilized to test the proposed methodology. The eigenvector centrality measurement successfully describes the allosteric pathways of IGPS, and allows to pinpoint key amino acids in terms of their relevance in the momentum transfer process. The resulting insight can be utilized for refining the control of IGPS activity, widening the scope for its engineering. Furthermore, we propose a new centrality metric quantifying the relevance of the surroundings of each residue. In addition, the proposed technique is validated against experimental solution NMR measurements yielding fully consistent results. Overall, the methodologies proposed in the present work constitute a powerful and cost effective strategy to gain insight on the allosteric mechanism of proteins