Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects -- typically neglected by conventional quantum chemistry approaches -- we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potentials of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the introduction of a fully automated sampling scheme and the use of molecular forces during neural network potential training. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n-alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all these case studies we find excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.Comment: 12 pages, 9 figure

Wie sollte das deutsche Bildungssystem reformiert werden? - PISA und die Konsequenzen

Die jüngst veröffentlichte OECD-Studie PISA bestätigte erneut, dass das deutsche Bildungssystem im internationalen Vergleich nur mittelmäßige Leistungen hervorbringt. Wie könnte es reformiert werden

Representing molecule-surface interactions with symmetry-adapted neural networks

The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energy surface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g. by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.Comment: 13 pages including 8 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.htm

Fingerprints for spin-selection rules in the interaction dynamics of O2 at Al(111)

We performed mixed quantum-classical molecular dynamics simulations based on first-principles potential-energy surfaces to demonstrate that the scattering of a beam of singlet O2 molecules at Al(111) will enable an unambiguous assessment of the role of spin-selection rules for the adsorption dynamics. At thermal energies we predict a sticking probability that is substantially less than unity, with the repelled molecules exhibiting characteristic kinetic, vibrational and rotational signatures arising from the non-adiabatic spin transition.Comment: 4 pages including 3 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.htm

Wie sollte das deutsche Bildungssystem reformiert werden? - PISA und die Konsequenzen

Die jüngst veröffentlichte OECD-Studie PISA bestätigte erneut, dass das deutsche Bildungssystem im internationalen Vergleich nur mittelmäßige Leistungen hervorbringt. Wie könnte es reformiert werden? --

A Hybrid Density Functional Theory Benchmark Study on Lithium Manganese Oxides

The lithium manganese oxide spinel Li$_x$Mn$_2$O$_4$, with $0\leq x\leq 2$, is an important example for cathode materials in lithium ion batteries. However, an accurate description of Li$_x$Mn$_2$O$_4$ by first-principles methods like density functional theory is far from trivial due to its complex electronic structure, with a variety of energetically close electronic and magnetic states. It was found that the local density approximation as well as the generalized gradient approximation (GGA) are unable to describe Li$_x$Mn$_2$O$_4$ correctly. Here, we report an extensive benchmark for different Li$_x$Mn$_y$O$_z$ systems using the hybrid functionals PBE0 and HSE06, as well as the recently introduced local hybrid functional PBE0r. We find that all of these functionals yield energetic, structural, electronic, and magnetic properties in good agreement with experimental data. The notable benefit of the PBE0r functional, which relies on on-site Hartree-Fock exchange only, is a much reduced computational effort that is comparable to GGA functionals. Furthermore, the Hartree-Fock mixing factors in PBE0r are smaller than in PBE0, which improves the results for (lithium) manganese oxides. The investigation of Li$_x$Mn$_2$O$_4$ shows that two Mn oxidation states, +III and +IV, coexist. The Mn$^\text{III}$ ions are in the high-spin state and the corresponding MnO$_6$ octahedra are Jahn-Teller distorted. The ratio between Mn$^\text{III}$ and Mn$^\text{IV}$ and thus the electronic structure changes with the Li content while no major structural changes occur in the range from $x=0$ to $1$. This work demonstrates that the PBE0r functional provides an equally accurate and efficient description of the investigated Li$_x$Mn$_y$O$_z$ systems.Comment: 17 pages, 8 figure

Signatures of nonadiabatic O2 dissociation at Al(111): First-principles fewest-switches study

Recently, spin selection rules have been invoked to explain the discrepancy between measured and calculated adsorption probabilities of molecular oxygen reacting with Al(111). In this work, we inspect the impact of nonadiabatic spin transitions on the dynamics of this system from first principles. For this purpose the motion on two distinct potential-energy surfaces associated to different spin configurations and possible transitions between them are inspected by means of the Fewest Switches algorithm. Within this framework we especially focus on the influence of such spin transitions on observables accessible to molecular beam experiments. On this basis we suggest experimental setups that can validate the occurrence of such transitions and discuss their feasibility.Comment: 13 pages, 7 figure

Виртуальная образовательная среда таможенного вуза (на примере Санкт-Петербургского имени В. Б. Бобкова филиала Российской таможенной академии)

Продемонстрирован опыт использования информационных технологий, позволивший обеспечить тесную интеграцию всех элементов образовательной системы вуза на базе единой виртуальной образовательной среды таможенного вуза, являющейся системно-организационной совокупностью средств передачи данных, информационных ресурсов, протоколов взаимодействия, аппаратно-программного и организационно-методического обеспечения. Установлено, что высокая эффективность системы управления качеством образования достигается за счет оперативности принятия необходимых решений и возможности контроля результатов их реализации, в том числе, с помощью виртуальной образовательной среды