Journal of Computational Chemistry

doi: 10.1002/jcc.25131pmid: N/A

doi: 10.1002/jcc.24936pmid: N/A

Furmanchuk, Al'ona; Saal, James E.; Doak, Jeff W.; Olson, Gregory B.; Choudhary, Alok; Agrawal, Ankit

doi: 10.1002/jcc.25067pmid: 28960343

The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.

Sumi, Tomonari; Maruyama, Yutaka; Mitsutake, Ayori; Mochizuki, Kenji; Koga, Kenichiro

doi: 10.1002/jcc.25101pmid: 29116647

Recently, we proposed a reference‐modified density functional theory (RMDFT) to calculate solvation free energy (SFE), in which a hard‐sphere fluid was introduced as the reference system instead of an ideal molecular gas. Through the RMDFT, using an optimal diameter for the hard‐sphere reference system, the values of the SFE calculated at room temperature and normal pressure were in good agreement with those for more than 500 small organic molecules in water as determined by experiments. In this study, we present an application of the RMDFT for calculating the temperature and pressure dependences of the SFE for solute molecules in water. We demonstrate that the RMDFT has high predictive ability for the temperature and pressure dependences of the SFE for small solute molecules in water when the optimal reference hard‐sphere diameter determined for each thermodynamic condition is used. We also apply the RMDFT to investigate the temperature and pressure dependences of the thermodynamic stability of an artificial small protein, chignolin, and discuss the mechanism of high‐temperature and high‐pressure unfolding of the protein. © 2017 Wiley Periodicals, Inc.

Wang, Bao; Wang, Chengzhang; Wu, Kedi; Wei, Guo‐Wei

doi: 10.1002/jcc.25107pmid: 29127720

Implicit solvent models divide solvation free energies into polar and nonpolar additive contributions, whereas polar and nonpolar interactions are inseparable and nonadditive. We present a feature functional theory (FFT) framework to break this ad hoc division. The essential ideas of FFT are as follows: (i) representability assumption: there exists a microscopic feature vector that can uniquely characterize and distinguish one molecule from another; (ii) feature‐function relationship assumption: the macroscopic features, including solvation free energy, of a molecule is a functional of microscopic feature vectors; and (iii) similarity assumption: molecules with similar microscopic features have similar macroscopic properties, such as solvation free energies. Based on these assumptions, solvation free energy prediction is carried out in the following protocol. First, we construct a molecular microscopic feature vector that is efficient in characterizing the solvation process using quantum mechanics and Poisson–Boltzmann theory. Microscopic feature vectors are combined with macroscopic features, that is, physical observable, to form extended feature vectors. Additionally, we partition a solvation dataset into queries according to molecular compositions. Moreover, for each target molecule, we adopt a machine learning algorithm for its nearest neighbor search, based on the selected microscopic feature vectors. Finally, from the extended feature vectors of obtained nearest neighbors, we construct a functional of solvation free energy, which is employed to predict the solvation free energy of the target molecule. The proposed FFT model has been extensively validated via a large dataset of 668 molecules. The leave‐one‐out test gives an optimal root‐mean‐square error (RMSE) of 1.05 kcal/mol. FFT predictions of SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 challenge sets deliver the RMSEs of 0.61, 1.86, 1.64, 0.86, and 1.14 kcal/mol, respectively. Using a test set of 94 molecules and its associated training set, the present approach was carefully compared with a classic solvation model based on weighted solvent accessible surface area. © 2017 Wiley Periodicals, Inc.

Maeda, Satoshi; Harabuchi, Yu; Takagi, Makito; Saita, Kenichiro; Suzuki, Kimichi; Ichino, Tomoya; Sumiya, Yosuke; Sugiyama, Kanami; Ono, Yuriko

doi: 10.1002/jcc.25106pmid: 29135034

This article reports implementation and performance of the artificial force induced reaction (AFIR) method in the upcoming 2017 version of GRRM program (GRRM17). The AFIR method, which is one of automated reaction path search methods, induces geometrical deformations in a system by pushing or pulling fragments defined in the system by an artificial force. In GRRM17, three different algorithms, that is, multicomponent algorithm (MC‐AFIR), single‐component algorithm (SC‐AFIR), and double‐sphere algorithm (DS‐AFIR), are available, where the MC‐AFIR was the only algorithm which has been available in the previous 2014 version. The MC‐AFIR does automated sampling of reaction pathways between two or more reactant molecules. The SC‐AFIR performs automated generation of global or semiglobal reaction path network. The DS‐AFIR finds a single path between given two structures. Exploration of minimum energy structures within the hypersurface in which two different electronic states degenerate, and an interface with the quantum mechanics/molecular mechanics method, are also described. A code termed SAFIRE will also be available, as a visualization software for complicated reaction path networks. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.