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Noncovalent Interactions of DNA Bases with Naphthalene and Graphene
Cho, Yeonchoo,Min, Seung Kyu,Yun, Jeonghun,Kim, Woo Youn,Tkatchenko, Alexandre,Kim, Kwang S. American Chemical Society 2013 Journal of chemical theory and computation Vol.9 No.4
<P>The complexes of a DNA base bound to graphitic systems are studied. Considering naphthalene as the simplest graphitic system, DNA base–naphthalene complexes are scrutinized at high levels of ab initio theory including coupled cluster theory with singles, doubles, and perturbative triples excitations [CCSD(T)] at the complete basis set (CBS) limit. The stacked configurations are the most stable, where the CCSD(T)/CBS binding energies of guanine, adenine, thymine, and cytosine are 9.31, 8.48, 8.53, 7.30 kcal/mol, respectively. The energy components are investigated using symmetry-adapted perturbation theory based on density functional theory including the dispersion energy. We compared the CCSD(T)/CBS results with several density functional methods applicable to periodic systems. Considering accuracy and availability, the optB86b nonlocal functional and the Tkatchenko–Scheffler functional are used to study the binding energies of nucleobases on graphene. The predicted values are 18–24 kcal/mol, though many-body effects on screening and energy need to be further considered.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jctcce/2013/jctcce.2013.9.issue-4/ct301097u/production/images/medium/ct-2012-01097u_0007.gif'></P>
Tkatchenko, Alexandre,Alfè,, Dario,Kim, Kwang S. American Chemical Society 2012 Journal of chemical theory and computation Vol.8 No.11
<P>Supramolecular host–guest systems play an important role for a wide range of applications in chemistry and biology. The prediction of the stability of host–guest complexes represents a great challenge to first-principles calculations due to an interplay of a wide variety of covalent and noncovalent interactions in these systems. In particular, van der Waals (vdW) dispersion interactions frequently play a prominent role in determining the structure, stability, and function of supramolecular systems. On the basis of the widely used benchmark case of the <I>buckyball catcher</I> complex (C<SUB>60</SUB>@C<SUB>60</SUB>H<SUB>28</SUB>), we assess the feasibility of computing the binding energy of supramolecular host–guest complexes from first principles. Large-scale diffusion Monte Carlo (DMC) calculations are carried out to accurately determine the binding energy for the C<SUB>60</SUB>@C<SUB>60</SUB>H<SUB>28</SUB> complex (26 ± 2 kcal/mol). On the basis of the DMC reference, we assess the accuracy of widely used and efficient density-functional theory (DFT) methods with dispersion interactions. The inclusion of vdW dispersion interactions in DFT leads to a large stabilization of the C<SUB>60</SUB>@C<SUB>60</SUB>H<SUB>28</SUB> complex. However, DFT methods including pairwise vdW interactions overestimate the stability of this complex by 9–17 kcal/mol compared to the DMC reference and the extrapolated experimental data. A significant part of this overestimation (9 kcal/mol) stems from the lack of dynamical dielectric screening effects in the description of the molecular polarizability in pairwise dispersion energy approaches. The remaining overstabilization arises from the isotropic treatment of atomic polarizability tensors and the lack of many-body dispersion interactions. A further assessment of a different supramolecular system – glycine anhydride interacting with an amide macrocycle – demonstrates that both the dynamical screening and the many-body dispersion energy are complex contributions that are very sensitive to the underlying molecular geometry and type of bonding. We discuss the required improvements in theoretical methods for achieving “chemical accuracy” in the first-principles modeling of supramolecular systems.</P>
Pronobis, Wiktor,Tkatchenko, Alexandre,Mü,ller, Klaus-Robert American Chemical Society 2018 Journal of chemical theory and computation Vol.14 No.6
<P>Machine learning (ML) based prediction of molecular properties across chemical compound space is an important and alternative approach to efficiently estimate the solutions of highly complex many-electron problems in chemistry and physics. Statistical methods represent molecules as descriptors that should encode molecular symmetries and interactions between atoms. Many such descriptors have been proposed; all of them have advantages and limitations. Here, we propose a set of general two-body and three-body interaction descriptors which are invariant to translation, rotation, and atomic indexing. By adapting the successfully used kernel ridge regression methods of machine learning, we evaluate our descriptors on predicting several properties of small organic molecules calculated using density-functional theory. We use two data sets. The GDB-7 set contains 6868 molecules with up to 7 heavy atoms of type CNO. The GDB-9 set is composed of 131722 molecules with up to 9 heavy atoms containing CNO. When trained on 5000 random molecules, our best model achieves an accuracy of 0.8 kcal/mol (on the remaining 1868 molecules of GDB-7) and 1.5 kcal/mol (on the remaining 126722 molecules of GDB-9) respectively. Applying a linear regression model on our novel many-body descriptors performs almost equal to a nonlinear kernelized model. Linear models are readily interpretable: a feature importance ranking measure helps to obtain qualitative and quantitative insights on the importance of two- and three-body molecular interactions for predicting molecular properties computed with quantum-mechanical methods.</P> [FIG OMISSION]</BR>
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
Schü,tt, K. T.,Kessel, P.,Gastegger, M.,Nicoli, K. A.,Tkatchenko, A.,Mü,ller, K.-R. American Chemical Society 2019 Journal of chemical theory and computation Vol.15 No.1
<P>SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.</P> [FIG OMISSION]</BR>
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Chmiela, Stefan,Sauceda, Huziel E.,Poltavsky, Igor,Mü,ller, Klaus-Robert,Tkatchenko, Alexandre Elsevier 2019 Computer physics communications Vol.240 No.-
<P><B>Abstract</B></P> <P>We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.</P>