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Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

Mood disorders affect the daily lives of millions of people worldwide. The search for more efficient therapies for mood disorders remains an active field of research. In silico approaches can accelerate the search for inhibitors against protein targets related to mood disorders. Here, we developed the first model perturbation-theory machine learning model based on a multiplayer perceptron network (PTML-MLP) for the simultaneous prediction and design of virtual dual-target inhibitors against two proteins associated with mood disorders, namely norepinephrine and serotonin transporters (NET and SERT, respectively). The PTML-MLP model had an accuracy of around 80%. From a chemical point of view, the PTML-MLP model could accurately identify both single- and dual-target inhibitors present in the dataset used to build it. Through the application of the fragment-based topological design (FBTD) approach, the molecular descriptors (multi-label graph-based indices) present in the PTML-MLP model were physicochemically and structurally interpreted. Such interpretations enabled (a) the extraction of different molecular fragments with a positive influence on the enhancement of the dual-target activity and (b) the design of four new drug-like molecules by assembling (fusing and/or connecting) several suitable molecular fragments. The designed molecules were predicted by the PTML-MLP model to exhibit dual-target activity against the NET and SERT proteins. These predictions, together with the estimated druglikeness suggest that the designed molecules could be new promising chemotypes to be considered for future synthesis and biological experimentation in the context of treatments for mood disorders. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures. [ABSTRACT FROM AUTHOR]

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar. This article is categorized under:Structure and Mechanism > Reaction Mechanisms and CatalysisComputer and Information Science > Visualization [ABSTRACT FROM AUTHOR]

Summary: Artificial neural networks (ANNs) methods have recently been used for modeling and predicting catalysis, which has been conventionally described using reaction kinetics. ANNs‐based techniques for analyzing complex catalytic systems have shown a high level of accuracy and dependability. In contrast, there is still a lack of strategies to select an appropriate ANNs algorithm according to size and quality of data, the complexity of the reaction, and catalyst characteristics. In this study, four different ANNs were proposed for the performance prediction of Pt‐based catalysts in water gas shift reaction: multilayer perceptron, long short‐term memory, recurrent neural network, and gated recurrent unit. By identifying the optimal ANN structure such as training/testing dataset ratio, topology, and epochs, the capability of the ANNs is comparatively evaluated in terms of prediction accuracy, computational load, and the quantity of required data for model training. As a case study, the effect of types and contents of promoters and supports in Pt‐based catalysts on CO conversion was analyzed. As a result, it was revealed that the multilayer perceptron model shows better performance than the others with the highest accuracy (mean square error = 0.0068) and lowest computation time. In addition, it was identified using the multiplayer perceptron model that Pt/Ca/TiO2 of 10 wt% Ca is the most favorable catalyst by achieving CO conversion over 90% at an operating temperature of 300°C. [ABSTRACT FROM AUTHOR]