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Register a new userTroubleshooting Unstable Molecules in Chemical Space
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Added by
Raghunathan Ramakrishnan Dr.
Publication date
2020
fields
Physics
and research's language is
English
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A key challenge in automated chemical compound space explorations is ensuring veracity in minimum energy geometries---to preserve intended bonding connectivities. We discuss an iterative high-throughput workflow for connectivity preserving geometry optimizations exploiting the nearness between quantum mechanical models. The methodology is benchmarked on the QM9 dataset comprising DFT-level properties of 133,885 small molecules; of which 3,054 have questionable geometric stability. We successfully troubleshoot 2,988 molecules and ensure a bijective mapping between desired Lewis formulae and final geometries. Our workflow, based on DFT and post-DFT methods, identifies 66 molecules as unstable; 52 contain $-{rm NNO}-$, the rest are strained due to pyramidal sp$^2$ C. In the curated dataset, we inspect molecules with long CC bonds and identify ultralong contestants ($r>1.70$~AA{}) supported by topological analysis of electron density. We hope the proposed strategy to play a role in big data quantum chemistry initiatives.
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The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust `local machine learning (ML) strategy capturing the effect of neighbourhood on an atoms `near-sighted property -- chemical shielding; (2) an accurate reference dataset generated with a state-of-the-art first principles method for training. Herein we report the QM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in 134k molecules of the QM9 dataset in gas and five common solvent phases. Using these data for training, we present benchmark results for the prediction transferability of kernel-ridge regression models with popular local descriptors. Our best model trained on 100k samples, accurately predict isotropic shielding of 50k `hold-out atoms with a mean error of less than $1.9$ ppm. For rapid prediction of new query molecules, the models were trained on geometries from an inexpensive theory. Furthermore, by using a $Delta$-ML strategy, we quench the error below $1.4$ ppm. Finally, we test the transferability on non-trivial benchmark sets that include benchmark molecules comprising 10 to 17 heavy atoms and drugs.
We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures - comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers) - as well as 100 non-equilibrium structural variations thereof to reach a total of $approx$ 4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.
The yield of strong-field ionization, by a linearly polarized probe pulse, is studied experimentally and theoretically, as a function of the relative orientation between the laser field and the molecule. Experimentally, carbonyl sulfide, benzonitrile and naphthalene molecules are aligned in one or three dimensions before being singly ionized by a 30 fs laser pulse centered at 800 nm. Theoretically, we address the behaviour of these three molecules. We consider the degree of alignment and orientation and model the angular dependence of the total ionization yield by molecular tunneling theory accounting for the Stark shift of the energy level of the ionizing orbital. For naphthalene and benzonitrile the orientational dependence of the ionization yield agrees well with the calculated results, in particular the observation that ionization is maximized when the probe laser is polarized along the most polarizable axis. For OCS the observation of maximum ionization yield when the probe is perpendicular to the internuclear axis contrasts the theoretical results.
Quantum indistinguishability plays a crucial role in many low-energy physical phenomena, from quantum fluids to molecular spectroscopy. It is, however, typically ignored in most high temperature processes, particularly for ionic coordinates, implicitly assumed to be distinguishable, incoherent and thus well-approximated classically. We explore chemical reactions involving small symmetric molecules, and argue that in many situations a full quantum treatment of collective nuclear degrees of freedom is essential. Supported by several physical arguments, we conjecture a Quantum Dynamical Selection (QDS) rule for small symmetric molecules that precludes chemical processes that involve direct transitions from orbitally non-symmetric molecular states. As we propose and discuss, the implications of the Quantum Dynamical Selection rule include: (i) a differential chemical reactivity of para- and ortho-hydrogen, (ii) a mechanism for inducing inter-molecular quantum entanglement of nuclear spins, (iii) a new isotope fractionation mechanism, (iv) a novel explanation of the enhanced chemical activity of Reactive Oxygen Species, (v) illuminating the importance of ortho-water molecules in modulating the quantum dynamics of liquid water, (vi) providing the critical quantum-to-biochemical linkage in the nuclear spin model of the (putative) quantum brain, among others.
In this exploratory qualitative study, we describe instructors self-reported practices for teaching and assessing students ability to troubleshoot in electronics lab courses. We collected audio data from interviews with 20 electronics instructors from 18 institutions that varied by size, selectivity, and other factors. In addition to describing participants instructional practices, we characterize their perceptions about the role of troubleshooting in electronics, the importance of the ability to troubleshoot more generally, and what it means for students to be competent troubleshooters. One major finding of this work is that, while almost all instructors in our study said that troubleshooting is an important learning outcome for students in electronics lab courses, only half of instructors said they directly assessed students ability to troubleshoot. Based on our findings, we argue that there is a need for research-based instructional materials that attend to both cognitive and non-cognitive aspects of troubleshooting proficiency. We also identify several areas for future investigation related to troubleshooting instruction in electronics lab courses.
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