jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Koichiro Adachi | T. SASAKI | Atsuo Arai | Makiko Shimizu | Hiroshi Yamazaki
Source: The Journal of Toxicological Sciences (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Impact of variability of &lt;i&gt;in silico&lt;/i&gt; and &lt;i&gt;in vitro&lt;/i&gt; octanol/water partition coefficients of compounds on the input parameters and results of simplified human physiologically based pharmacokinetic models after virtual oral administrations The octanol/water partition coefficient, P (logP), is a hydrophobicity index and is one of the determining factors of the pharmacokinetics of chemical compounds. LogP values obtained from in silico software, open chemistry databases, and in vitro liquid chromatography retention factors may vary. Some chemicals (boscalid, etoxazole, and permethrin) have up to four-order-magnitude differences in in silico/in vitro P values. This study aimed to evaluate the effects of logP values of these three compounds, along with bisphenol A, 1,2-dibromobenzene, tetrabromobisphenol A, trazodone, and triazolam, on the input parameters and output plasma/hepatic concentration-time profiles of simple physiologically based pharmacokinetic (PBPK) models. Although the blood-to-plasma concentration ratios (~0.9-0.6) were slightly affected by variations in logP values, logarithmic plasma unbound fraction values and liver-to-plasma partition coefficients (K<sub>p,h</sub>) were, respectively, inversely and linearly correlated with logP values (K<sub>p,h</sub> was stable at ~6.7 for logP > 4). LogP was among the input parameters for previously established machine learning systems; consequently, the resulting logarithmic intrinsic clearance values were correlated with logP values in the range 2-8. However, the bioavailability, absorption rate constants, and volumes of distribution were not affected. PBPK-modeled plasma and hepatic maximum concentrations and areas under the concentration-time curves after virtual oral doses were mostly within ~0.5- to ~2-fold ranges, except for substances with low in vitro logP values, e.g., etoxazole and permethrin. These results suggest that in silico logP values are generally suitable for pharmacokinetic modeling; nevertheless, caution is needed for compounds with low in vitro logP values of ~2. In silico ; Octanol ; Partition coefficient ; In vitro ; Partition (number theory) ; Chemistry ; Mathematics ; Biochemistry ; Chromatography ; Combinatorics Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems
Authors: Derek Mendez | James M. Holton | Artem Y. Lyubimov | Sabine Hollatz | Irimpan I. Mathews | Aleksander Cichosz | Vardan Martirosyan | Teo Zeng | Ryan Stofer | Ruobin Liu | Jinhu Song | S.E. McPhillips | Mike Soltis | Aina E. Cohen
Source: Acta Crystallographica Section D Structural Biology (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Deep residual networks for crystallography trained on synthetic data The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis. Computer science ; Artificial neural network ; Diffraction ; Artificial intelligence ; Graphics ; Python (programming language) ; Codebase ; Computer graphics (images) ; Computational science ; Optics ; Source code ; Programming language ; Physics Enzyme Structure and Function ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Surjeet Kumar | Russlan Jaafreh | Subhajit Dutta | Jung Hyeon Yoo | Santiago Pereznieto | Kotiba Hamad | Dae Ho Yoon
Source: Journal of Magnesium and Alloys (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Predictive modeling of critical temperatures in magnesium compounds using transfer learning This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset. Initially, a large source dataset (Bandgap dataset) comprising approximately ∼75k compounds is utilized for pretraining, followed by finetuning with a smaller Critical Temperature (Tc) dataset containing ∼300 compounds. Comparatively, there is a significant improvement in the performance of the transfer learning model over the traditional deep learning (DL) model in predicting Tc. Subsequently, the transfer learning model is applied to predict the properties of approximately 150k compounds. Predictions are validated computationally using density functional theory (DFT) calculations based on lattice dynamics-related theory. Moreover, to demonstrate the extended predictive capability of the transfer learning model for new materials, a pool of virtual compounds derived from prototype crystal structures from the Materials Project (MP) database is generated. Tc predictions are obtained for ∼3600 virtual compounds, which underwent screening for electroneutrality and thermodynamic stability. An Extra Trees-based model is trained to utilize Ehull values to obtain thermodynamically stable materials, employing a dataset containing Ehull values for approximately 150k materials for training. Materials with Ehull values exceeding 5 meV/atom were filtered out, resulting in a refined list of potential Mg-based superconductors. This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity. Transfer of learning ; Superconductivity ; Materials science ; Lattice (music) ; Density functional theory ; Stability (learning theory) ; Atom (system on chip) ; Transfer (computing) ; Machine learning ; Computer science ; Artificial intelligence ; Condensed matter physics ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Inorganic Chemistry and Materials
Authors: Mingzheng Zhang | Qiming Xie | Zhuozheng Wang | Wentao Zhang | Yawen Bo | Zhiying Zhang | Hao Li | Yi Luo | Qihan Gong | Shunning Li | Feng Pan
Source: The Journal of Physical Chemistry Letters (article, 2024-04-26)
Field: Chemistry — Inorganic Chemistry

Data-Driven Discovery of Gas-Selective Organic Linkers in Metal–Organic Frameworks for the Separation of Ethylene and Ethane Metal-organic frameworks (MOFs) are potential candidates for gas-selective adsorbents for the separation of an ethylene/ethane mixture. To accelerate material discovery, high-throughput computational screening is a viable solution. However, classical force fields, which were widely employed in recent studies of MOF adsorbents, have been criticized for their failure to cover complicated interactions such as those involving π electrons. Herein, we demonstrate that machine learning force fields (MLFFs) trained on quantum-chemical reference data can overcome this difficulty. We have constructed a MLFF to accurately predict the adsorption energies of ethylene and ethane on the organic linkers of MOFs and discovered that the π electrons from both the ethylene molecule and the aromatic rings in the linkers could substantially influence the selectivity for gas adsorption. Four kinds of MOF linkers are identified as having promise for the separation of ethylene and ethane, and our results could also offer a new perspective on the design of MOF building blocks for diverse applications. Ethylene ; Adsorption ; Metal-organic framework ; Selectivity ; Molecule ; Selective adsorption ; Materials science ; Gas separation ; Quantum chemical ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Membrane Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Vid Kuralt | Rok Frlan
Source: Antibiotics (article, 2024-03-11)
Field: Chemistry — Organic Chemistry

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents. Computational biology ; Chemical space ; Structural bioinformatics ; In silico ; Drug discovery ; Cheminformatics ; Antimicrobial ; Lipinski's rule of five ; Computer science ; Biology ; Bioinformatics ; Biochemistry ; Protein structure ; Microbiology ; Gene Antimicrobial agents and applications ; Computational Drug Discovery Methods ; Antimicrobial Peptides and Activities
Authors: Houping Cheng | Yan Liu | Yixiang Ruan | Yuchen He | Xinyu Wang | Jun Xuan | Xiaodong Shen | Ning Ye | Yiming Mo
Source: ACS electrochemistry. (article, 2024-10-03)
Field: Chemistry — Organic Chemistry

Pyridine <i>N</i> -Oxide Mediators for Electrochemical Benzylic C(sp <sup>3</sup> )–H Oxygenation The electrooxidation of ubiquitous benzylic C(sp3)–H bonds offers a sustainable pathway to synthesize value-added carbonyl compounds. However, the lack of a versatile electrocatalytic system to accommodate the structural diversity of compounds containing benzylic C–H bonds hinders the practical implementation of electrochemical benzylic C–H oxidation technology. Herein, we designed and evaluated a library of electrochemical hydrogen atom transfer (HAT) mediators, derived from pyridine N-oxides, for the selective oxygenation of benzylic C(sp3)–H bonds. The O–H bond dissociation energy and oxidation potential of pyridine N-oxides can be fine-tuned by modifying the electronic properties of substituent groups on the pyridine ring, thus offering a facile way to adjust HAT characteristics to suit the target benzylic C–H bonds. A predictive neural network model was constructed based on experimental high-throughput screening data to map the quantitative structure-activity relationship (QSAR) for catalyst–substrate pairs. In addition, the practicality of the developed electrocatalytic system was demonstrated with a good substrate scope and scalability to achieve gram-scale synthesis using a flow electrochemical reactor. Pyridine ; Oxygenation ; Electrochemistry ; Chemistry ; Medicinal chemistry ; Electrode ; Physical chemistry ; Anesthesia ; Medicine Radical Photochemical Reactions ; Oxidative Organic Chemistry Reactions ; Catalytic C–H Functionalization Methods
Authors: Alexandra Day | Carolin B. Wahl | Roberto dos Reis | Wei‐keng Liao | Vinayak P. Dravid | Alok Choudhary | Ankit Agrawal
Source:  (article, 2024-10-20)
Field: Materials Science — Materials Chemistry

Automated Nanoparticle Image Processing Pipeline for AI-Driven Materials Characterization Recent innovations have made it possible to produce millions of distinct nanoparticles on a chip. These vast volumes of data are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image preprocessing step before model training can produce significantly higher-performing models in a fraction of the time and make the model more robust to different image noise levels and microscope acquisition settings. The proposed image processing pipeline effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures, resulting in higher performance and significant cost savings. Experiments demonstrate superior performance relative to our baseline, including a 15% improvement in recall and more than a 10% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives in our application, and our best-performing model obtains a precision of 97.3% and weighted F-score of 95.9% on an unseen test set. Additionally, model training time is reduced from 15.5 hours to 32 seconds. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will offer a considerable speedup in the laboratory, allowing researchers to rapidly and accurately analyze much greater volumes of data and accelerate materials discovery. Pipeline (software) ; Characterization (materials science) ; Nanoparticle ; Image processing ; Computer science ; Artificial intelligence ; Nanotechnology ; Materials science ; Image (mathematics) ; Programming language Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Mineral Processing and Grinding
Authors: Srihari M. Kastuar | Christopher Rzepa | Srinivas Rangarajan | Chinedu E. Ekuma
Source: APL Machine Learning (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

A high-throughput and data-driven computational framework for novel quantum materials Two-dimensional layered materials, such as transition metal dichalcogenides (TMDs), possess an intrinsic van der Waals gap at the layer interface, allowing for remarkable tunability of the optoelectronic features via external intercalation of foreign guests such as atoms, ions, or molecules. Herein, we introduce a high-throughput, data-driven computational framework for the design of novel quantum materials derived from intercalating planar conjugated organic molecules into bilayer transition metal dichalcogenides and dioxides. By combining first-principles methods, material informatics, and machine learning, we characterize the energetic and mechanical stability of this new class of materials and identify the fifty (50) most stable hybrid materials from a vast configurational space comprising ∼105 materials, employing intercalation energy as the screening criterion. Throughput ; Computer science ; Quantum ; Computational science ; Physics ; Telecommunications 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Yuan Fang | Rizwana Irfan | Ahmad Almadhor | Mohamed Abbas
Source: Aerospace Science and Technology (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Bending information of nanocomposites-reinforced microplate subjected to transient loading: Introducing physics-informed machine learning algorithm for solving the transient problem Transient (computer programming) ; Microsystem ; Finite element method ; Laplace transform ; Interpolation (computer graphics) ; Algorithm ; Shock absorber ; Bending ; Computer science ; Machine learning ; Mechanical engineering ; Artificial intelligence ; Materials science ; Engineering ; Structural engineering ; Mathematics ; Mathematical analysis Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Acoustic Wave Phenomena Research
Authors: Zhi-Xiong Que | Shu-Zong Li | Bo Huang | Zhixiong Yang | Wei‐Bing Zhang
Source: The Journal of Chemical Physics (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

Ultra-flat bands at large twist angles in group-V twisted bilayer materials Flat bands in 2D twisted materials are key to the realization of correlation-related exotic phenomena. However, a flat band often was achieved in the large system with a very small twist angle, which enormously increases the computational and experimental complexity. In this work, we proposed group-V twisted bilayer materials, including P, As, and Sb in the β phase with large twist angles. The band structure of twisted bilayer materials up to 2524 atoms has been investigated by a deep learning method DeepH, which significantly reduces the computational time. Our results show that the bandgap and the flat bandwidth of twisted bilayer β-P, β-As, and β-Sb reduce gradually with the decreasing of twist angle, and the ultra-flat band with bandwidth approaching 0 eV is achieved. Interestingly, we found that a twist angle of 9.43° is sufficient to achieve the band flatness for β-As comparable to that of twist bilayer graphene at the magic angle of 1.08°. Moreover, we also find that the bandgap reduces with decreasing interlayer distance while the flat band is still preserved, which suggests interlayer distance as an effective routine to tune the bandgap of flat band systems. Our research provides a feasible platform for exploring physical phenomena related to flat bands in twisted layered 2D materials. Twist ; Bilayer ; Bilayer graphene ; Materials science ; Band gap ; Flatness (cosmology) ; Condensed matter physics ; Optics ; Optoelectronics ; Geometry ; Physics ; Nanotechnology ; Graphene ; Mathematics ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Marco Bertani | Alfonso Pedone | Francesco Faglioni | Thibault Charpentier
Source: ChemPhysChem (article, 2024-07-25)
Field: Materials Science — Ceramics and Composites

Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses In this work, we have applied the Kernel Ridge Regression (KRR) method using a Least Square Support Vector Regression (LSSVR) approach for the prediction of the NMR isotropic magnetic shielding (σ<sub>iso</sub>) of active nuclei (<sup>17</sup>O, <sup>23</sup>Na, <sup>25</sup>Mg, and <sup>29</sup>Si) in a series of (Mg, Na)-silicate glasses. The Machine Learning (ML) algorithm has been trained by mapping the local environment of each atom described by the Smooth Overlap of Atomic Position (SOAP) descriptor with isotropic chemical shielding values computed with DFT using the Gauge-Included-Projector-Augmented-Wave (GIPAW) approach. The influence of different training datasets generated through molecular dynamics simulations at various temperatures and with different inter-atomic potentials has been tested and we demonstrate the importance of a wide exploration of the configurational space to enhance the transferability of the ML-regressor. Finally, the trained ML-regressor has been used to simulate the <sup>29</sup>Si MAS NMR spectra of systems containing up to 20000 atoms by averaging hundreds of configurations extracted from classical MD simulations to account for thermal vibrations. This ML approach is a powerful tool for the interpretation of NMR spectra using relatively large systems at a fraction of the computational time required by quantum mechanical calculations which are of high computational cost. Magnesium ; Sodium silicate ; Silicate ; Electromagnetic shielding ; Sodium ; Inorganic chemistry ; Materials science ; Chemistry ; Chemical physics ; Mineralogy ; Chemical engineering ; Organic chemistry ; Metallurgy ; Composite material ; Engineering Glass properties and applications ; Luminescence Properties of Advanced Materials ; Advancements in Battery Materials
Authors: Anton Soria-López | María García-Martí | Juan C. Mejuto
Source: Tenside Surfactants Detergents (article, 2024-12-10)
Field: Chemistry — Analytical Chemistry

Ionic surfactants critical micelle concentration modelling in water/organic solvent mixtures using random forest and support vector machine algorithms Abstract The physicochemical property of surfactants that is widely used to study their behavior is the critical micellar concentration (CMC). The value of this property is specific to each surfactant as it depends on a number of external factors and the chemical composition of the surfactant. This research focused on using two new machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to predict the logarithmic CMC value of 10 ionic surfactants. The same database from the previous study (a total of 258 experimental cases) was used with the same input variables – those defining the mixture of the organic solvent-water: T, molecular weight, molar fraction and log P; and the chemical composition of the surfactant: number of atoms of each element of the surfactant – to develop the predictive models. The best RF and SVM models were then compared with the best ANN model developed in the previous study. According to the results, the normalized models were those that presented the lowest RMSE values in the validation phase. Finally, the two approaches proposed in this research are suitable tools, together with the ANN, for the prediction of CMC and as possible alternative methods to replace expensive experimental laboratory measurements. Chemistry ; Micelle ; Solvent ; Ionic liquid ; Organic solvent ; Support vector machine ; Random forest ; Critical micelle concentration ; Ionic bonding ; Algorithm ; Chemical engineering ; Organic chemistry ; Chromatography ; Aqueous solution ; Machine learning ; Computer science Spectroscopy and Chemometric Analyses ; Surfactants and Colloidal Systems ; Water Quality Monitoring and Analysis