Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal-Organic Frameworks.

A collection of more than 20,000 experimentally derived crystal structures for metal-organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals.

Adsorption-Based Separation of Near-Azeotropic Mixtures-A Challenging Example for High-Throughput Development of Adsorbents.

Adsorption of gas mixtures is central to adsorption-based gas separations, and the number of adsorbate mixture/adsorbent systems that exist is staggering. Because examples of machine learning (ML) models predicting single-component adsorption of arbitrary molecules in large libraries of crystalline adsorbents have been developed, it is interesting to determine whether these models can accurately predict mixture adsorption. Here, we use molecular simulations to generate mixture adsorption data with a set of 12 near-azeotropic molecules in a diverse set of MOFs.

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes.

Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD(50) with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes.

A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure.

An accurate model for prediction of autoignition temperature of pure compounds.

Accurate prediction of pure compounds autoignition temperature (AIT) is of great importance. In this study, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to evaluate the AIT of pure compounds. 1025 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model.

A new group contribution-based model for estimation of lower flammability limit of pure compounds.

In the present study, a new method is presented for estimation of lower flammability limit (LFL) of pure compounds. This method is based on a combination of a group contribution method and neural networks. The parameters of the model are the occurrences of a new collection of 105 functional groups.

A QSPR model for estimation of lower flammability limit temperature of pure compounds based on molecular structure.

In this study, a quantitative structure-property relationship was presented to estimate lower flammability limit temperature (LFLT) of pure compounds. This relationship is a multi-linear equation and has six parameters. These chemical structure-based parameters were selected from 1664 molecular-based parameters by genetic algorithm multivariate linear regression (GA-MLR).

Prediction of upper flammability limit percent of pure compounds from their molecular structures.

In this study, a quantitative structure-property relationship (QSPR) is presented to predict the upper flammability limit percent (UFLP) of pure compounds. The obtained model is a five parameters multi-linear equation. The parameters of the model are calculated only from chemical structure.

Prediction of some important physical properties of sulfur compounds using quantitative structure-properties relationships.

In this work, physical properties of sulfur compounds (critical temperature (Tc), critical pressure (Pc), and Pitzer's acentric factor (omega)) are predicted using quantitative structure-property relationship technique. Sulfur compounds present in petroleum cuts are considered environmental hazards. Genetic algorithm based multivariate linear regression (GA-MLR) is used to select most statistically effective molecular descriptors on the properties.

Prediction of molecular diffusivity of pure components into air: a QSPR approach.

The molecular diffusivity of 378 pure components into air was predicted using genetic algorithm-based multivariate linear regression (GA-MLR) and feed forward neural networks (FFNN). GA-MLR was used to select the molecular descriptors, as inputs for FFNN. The correlation coefficient (R2) of obtained multivariate linear seven-descriptor model by GA-MLR is 0.