Why computational chemistry

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Betowski, unpublished work. Betowski, W. Winnik, A. Marcus, and S. Here, the authors use a machine learning algorithm with a convolutional neural network to retrieve a complex and large molecule, fenchone C 10 H 16 O , from laser-induced electron diffraction data without the need for fitting or ab initio calculations.

Research 08 November Open Access. Research 02 November Open Access. Research 01 November Open Access. Layered boron compounds attract enormous interest in applications. This work reports first-principles calculations coupled with global optimization to show that the outer boron surface in MgB 2 nanosheets undergo disordering and clustering, which is experimentally confirmed in synthesized MgB 2 nanosheets.

Editorial 11 November Machine learning algorithms are fast surpassing human abilities in multiple tasks, from image recognition to medical diagnostics. Now, machine learning algorithms have been shown to be capable of accurately predicting the folded structures of proteins. Computational studies have previously explored the effect of cations and hypothesized their vital role in electrocatalysis.

Now, experimental evidence shows that without a cation, CO 2 reduction simply does not take place. Research Highlights 16 July An article in Nature Chemistry uses the knowledge gathered in the Cambridge Structural Database to build a machine-learning model that predicts the oxidation states of metal—organic frameworks. This process of determining stationary points is called geometry optimisation. The determination of molecular structure by geometry optimisation became routine only when efficient methods for calculating the first derivatives of the energy with respect to all atomic coordinates became available.

Evaluation of the related second derivatives allows the prediction of vibrational frequencies if harmonic motion is assumed. In some ways more importantly it allows the characterisation of stationary points. The frequencies are related to the eigenvalues of the matrix of second derivatives the Hessian matrix. If the eigenvalues are all positive, then the frequencies are all real and the stationary point is a local minimum.

If one eigenvalue is negative an imaginary frequency , the stationary point is a transition structure. If more than one eigenvalue is negative the stationary point is a more complex one, and usually of little interest. When found, it is necessary to move the search away from it, if we are looking for local minima and transition structures. This leads to evaluating the total energy as a sum of the electronic energy at fixed nuclei positions plus the repulsion energy of the nuclei.

A notable exception are certain approaches called direct quantum chemistry, which treat electrons and nuclei on a common footing. Density functional methods and semi-empirical methods are variants on the major theme. For very large systems the total energy is determined using molecular mechanics. Methods that do not include any empirical or semi-empirical parameters in their equations - being derived directly from theoretical principles, with no inclusion of experimental data - are called ab initio methods.

This does not imply that the solution is an exact one; they are all approximate quantum mechanical calculations. It means that a particular approximation is rigorously defined on first principles quantum theory and then solved within an error margin that is qualitatively known beforehand.