We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The includes 515 compounds, containing 180 different organic cations, 10 metals (Pb, Sn, Bi, Cd, Cu, Fe, Ge, Mn, Pd, Sb) and 3 halogens (I, Br, Cl) known so far will be regularly updated. contains geometrical chemical analysis the structures, which are useful to reveal quantitative structure-property relationships for this...

Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one which choice chemical structure representation. The classical approach rigorous feature engineering in ML typically improves performance predictive model, but at same time, it narrows down scope applicability decreases physical interpretability predicted results. In this study, we present graph convolutional neural networks...

Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard technique to access the performance of thousands candidates, but its accuracy is highly dependent on partial charge assignment method. In this study, we propose machine learning model that can reconcile benefits two main approaches-the high density-derived electrostatic and chemical (DDEC) method scalability...

The unprecedented structural flexibility and diversity of inorganic frameworks layered hybrid halide perovskites (LHHPs) raise a wide range useful optoelectronic properties thus predetermining the extraordinary high interest in this family materials. Nevertheless, influence different types distortions their framework on key physical such as band gaps has not yet been quantitatively identified. We provided systematic study relationships between LHHPs' six main descriptors framework, including...

Crystal structure prediction (CSP) has proven to be an effective route for the discovery of new materials. Nonetheless, ab initio techniques employed CSP metal-organic frameworks (MOFs) cannot scaled a high-throughput mode. Here, we propose data-driven method addressing current needs computational MOF discovery. Specifically, coarse-grained neural networks were implemented predict underlying net topology. The models showed satisfactory performance, which was next enhanced via limitation...

Machine learning-based methods are widely used today in chemical tasks, particularly drug design. Graph Convolutional Neural Networks (GCNNs) compete with one another predicting properties, achieving errors comparable those of experimental measurements. However, the increasing complexity data entry structures and trend toward utilizing three-dimensional molecular geometries rarely grounded a thorough search for accurate conformations input. In this study, we examined stability...

The presented multimodal transformer networks quantitatively reproduce experimental proton conductivity and the underlying conduction mechanism provide predictive uncertainty estimates.

The conformational mobility of organic molecules, defined as the variability practically accessible conformers, plays a critical role in determining electronic, chemical, and physical properties within computational methods. At the...

This article presents a general approach to solving the urgent practical problem of separation 4f-(lanthanides, Ln3+) and 5f-elements (actinides, An3+) very similar in properties based on DFT quantum-chemical supercomputer simulation Ln3+ An3+ complexes with polydentate nitrogen-containing heterocyclic ligands. The method allows calculate geometry parameters ligands metal ligand binding energies accuracy, permitting direct comparison calculation results experimental data, estimate...

Asceding interest of the scientific community in layered hybrid halide perovskites (LHHPs) as materials for innovative photovoltaic and optoelectronic applications led to unprecedented expansion this family compounds, reaching now several hundred refined structures. Despite unique structural diversity LHHPs, traditional approaches describing their structures, such dividing into Dion-Jacobson (DJ) or Ruddlesden-Popper (RP) phases mostt structures are ambiguous unquantifiable. Here, we...

In the current research, we conducted a comparative study of Ac3+ complex with H4DOTA and H4BATA. The stability constants [AcBATA]- [AcDOTA]- complexes were studied directly by extraction methods. We discovered that thermodynamic properties are superior to those [AcDOTA]-. Moreover, fast kinetics H4BATA complexation during radiolabeling experiment was observed already at room temperature. placed inside macrocyclic cavity complex, preventing release cation. According DFT studies, two possible...

While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning materials science through extensive benchmarking. particular, a set diverse neural networks is trained for given supervised task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as 28%...

New computational framework has extended an inverse materials design over all the possible stoichiometric compounds.

Here we present experimental confirmation of the theoretical calculation organic ligands' radiolytic degradation.

Ligand <bold>H4BATA</bold> forms highly stable complex with bismuth(<sc>iii</sc>) in 1–2 min at room temperature.

Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis an impressive range applications in various fields from gas storage to biomedicine. Diverse properties arise variation building units─metal centers linkers─in almost infinite chemical space. Such substantially complicates experimental design promotes use computational methods. In particular, most successful artificial intelligence algorithms for predicting reticular...

Actinide chemistry often lies beyond the applicability domain of majority modern theoretical tools due to high computational costs, relativistic effects, or just absence actinide data for semiempirical method fitting. On other hand, radioactivity pushes usage methods instead experimental ones. Here, we would like present a novel relPBE functional as an actinide-fitted version PBE0 functional.

The tunable structure of metal–organic frameworks (MOFs) is an ideal platform to achieve contradictory requirements for proton exchange membranes, a key component fuel cells. However, the rational design proton-conducting MOFs remains challenge owing intricate structure-property relationships that govern target performance. In present study, modeling quantities available hundreds scaled up many thousands entities using supervised machine learning. experimental dataset was curated train...

Radiolytic stability is one of the main requirements substances that are used in chemistry nuclear cycle or radiopharmaceutical chemistry. Herein, we proposed an approach for prediction radiolytic by estimation molecular reactivity. The DFT calculations atom-wise reactivity descriptor were made a number organic molecules. theoretical simulations validated experimental data. We irradiated molecules gamma-radiation and studied products radiolysis changes concentration HPLC-MS analysis....