jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moritz Thürlemann | Sereina Riniker
Source: PubMed (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

Hybrid Classical/Machine-Learning Force Fields for the Accurate Description of Molecular Condensed-Phase Systems Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come with inherent approximations and are dependent on the quality and quantity of training data. The rise of machine learning (ML) force fields (FFs) exacerbates limitations related to training data even further, especially for condensed-phase systems for which the generation of large and high-quality training datasets is difficult. Here, we propose a hybrid ML/classical FF model that is parametrized exclusively on high-quality <i>ab initio</i> data of dimers and monomers in vacuum but is transferable to condensed-phase systems. The proposed hybrid model combines our previous ML-parametrized classical model with ML corrections for situations where classical approximations break down, thus combining the robustness and efficiency of classical FFs with the flexibility of ML. Extensive validation on benchmarking datasets and experimental condensed-phase data, including organic liquids and small-molecule crystal structures, showcases how the proposed approach may promote FF development and unlock the full potential of classical FFs. Robustness (evolution) ; Computer science ; Flexibility (engineering) ; Benchmarking ; Ab initio ; Experimental data ; Training set ; Quality (philosophy) ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Vlada V. Petrova | Anton V. Domnin | Yuri B. Porozov | Pavel O. Kuliaev | Yaroslav V. Solovev
Source: Journal of Computational Chemistry (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds. Computer science ; Cluster analysis ; Machine learning ; Artificial intelligence ; Classifier (UML) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Yvonne Thielmann | Thorsten Luft | Norbert Zint | Juergen Koepke
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-03-16)
Field: Materials Science — Materials Chemistry

Crystal search – feasibility study of a real-time deep learning process for crystallization well images To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images. Python (programming language) ; Computer science ; Crystallization ; Artificial intelligence ; Deep learning ; Workstation ; Frame rate ; Real-time computing ; Operating system ; Engineering Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Detlef W. M. Hofmann | L. N. Kuleshova
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

A general force field by machine learning on experimental crystal structures. Calculations of intermolecular Gibbs energy with<i>FlexCryst</i> Machine learning was employed on the experimental crystal structures of the Cambridge Structural Database (CSD) to derive an intermolecular force field for all available types of atoms (general force field). The obtained pairwise interatomic potentials of the general force field allow for the fast and accurate calculation of intermolecular Gibbs energy. The approach is based on three postulates regarding Gibbs energy: the lattice energy must be below zero, the crystal structure must be a local minimum, and, if available, the experimental and the calculated lattice energy must coincide. The parametrized general force field was then validated regarding these three conditions. First, the experimental lattice energy was compared with the calculated energies. The observed errors were found to be in the order of experimental errors. Second, Gibbs lattice energy was calculated for all structures available in the CSD. Their energy values were found to be below zero in 99.86% of the cases. Finally, 500 random structures were minimized, and the change in density and energy was examined. The mean error in the case of density was below 4.06%, and for energy it was below 5.7%. The obtained general force field calculated Gibbs lattice energies of 259 041 known crystal structures within a few hours. Since Gibbs energy defines the reaction energy, the calculated energy can be used to predict chemical-physical properties of crystals, for instance, the formation of co-crystals, polymorph stability and solubility. Gibbs free energy ; Lattice energy ; Intermolecular force ; Force field (fiction) ; Lattice (music) ; Thermodynamics ; Crystal structure ; Crystal (programming language) ; Chemistry ; Statistical physics ; Crystallography ; Physics ; Molecule ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Nour Mahmoud Eldabah | Amin Shoukry | Wael Khair‐Eldeen | Sengo Kobayashi | Lamiaa Z. Mohamed
Source: Key engineering materials (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Neural Network Regressor for Designing Biomedical Low Elastic Modulus Ti-Zr-Nb-Mo Medium Entropy Alloys The excellent biocompatibility of Ti and Zr alloys makes them the best candidates for orthopedic implantations. The design of high Ti and Zr-containing alloys that show low Young's modulus for implant manufacturing is the objective of this work. Here, a feed-forward-back propagation neural network was used to speed up the design process and optimize alloy composition. The β-typeTi 45 -Zr 39 -Nb 12 -Mo 4 alloy is designed and showed promising properties. The alloy showed a low elastic modulus of 78 GPa and a high yield strength of 891 MPa resulting in a high elastic admissible strain that made it suitable for orthopedic applications. Materials science ; Elastic modulus ; Alloy ; Biocompatibility ; Modulus ; Composite material ; Titanium alloy ; Artificial neural network ; Young's modulus ; Metallurgy ; Computer science ; Machine learning Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; High Entropy Alloys Studies
Authors: Amer Marwan El-Samman | Incé A. Husain | Mai Huynh | Stefano De Castro | Brooke Morton | Guillaume Acke | Stijn De Baerdemacker
Source: ChemRxiv (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

Global interpretability and geometry of graph convolu- tional neural networks for chemistry in terms of chemical moieties Graph convolutional neural nets, such as SchNet, [Schütt et al, Journal of Chemical Physics, 2018, 148, 241722], provide accurate predictions of chemical quantities without invoking any direct physical or chemical principles. These methods learn a hidden statistical representation of molecular systems in an end-to-end fashion; from xyz coordinates to molecular properties with many hidden layers in between. This naturally leads to the interpretability question: what underlying chemical model determines the algorithm’s accurate decision-making? To answer this question, we analyze the hidden layer activations of QM9-trained SchNet, also known as “embedding vectors” with dimension- reduction, linear discriminant analysis and Euclidean-distance measures. The result is a quantifiable geometry of the model’s decision making that identifies chemical moieties and has a low parametric space of ∼ 5 important parameters from the fully-trained 128-parameter embedding. The geometry of the embedding space organizes these moieties with sharp linear boundaries that can classify each chemical environment within < 5 × 10−4 error. Euclidean distance between embedding vectors can be used to demonstrate a versatile molecular similarity measure, outperforming other popular hand- crafted representations such as Smooth Overlap of Atomic Positions (SOAP). We also reveal that the embedding vectors can be used to extract observables that are related to chemical environments such as pKa and NMR. The work is in line with the recent push for explainable AI and gives insights into the depth of modern statistical representations of chemistry, such as graph convolutional neural nets, in this rapidly evolving technology. Embedding ; Interpretability ; Chemical space ; Convolutional neural network ; Artificial intelligence ; Euclidean space ; Computer science ; Parametric statistics ; Dimensionality reduction ; Pattern recognition (psychology) ; Theoretical computer science ; Algorithm ; Mathematics ; Chemistry ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Shirish Joshi | Sanjeev Kumar Singh | Santosh Dubey
Source: International Journal for Computational Methods in Engineering Science and Mechanics (article, 2023-03-08)
Field: Materials Science — Materials Chemistry

Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires Metallic nanowires are now extensively used in several nanoscale devices and applications. To further enhance their efficient usage, the estimation and prediction of thermal and mechanical properties of these nanowires is very important. Performing experimental studies on the objects of such a small dimension is quite challenging. Molecular dynamics simulation technique can easily simulate and perform virtual experimentation on the objects of nanoscale dimensions. In the present work, silver nanowires of known dimension simulated and a uniaxial stress has been implemented using the Molecular dynamics approach. The stress-strain data generated by MD simulation, has been utilized to train, test and validate different machine learning models. These machine-learning models offer a reasonably good amount of predictability of the tensile characteristics of the silver nanowire at any temperature. Nanowire ; Molecular dynamics ; Predictability ; Materials science ; Nanoscopic scale ; Dimension (graph theory) ; Stress (linguistics) ; Nanotechnology ; Work (physics) ; Nanoelectronics ; Computer science ; Characterization (materials science) ; Thermal ; Artificial intelligence ; Mechanical engineering ; Engineering ; Physics ; Mathematics ; Chemistry ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Thermal properties of materials
Authors: Nobuya Tsuji | Pavel Sidorov | Chendan Zhu | Yuuya Nagata | Timur Gimadiev | Alexandre Varnek | Benjamin List
Source: Angewandte Chemie (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors** Abstract Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time‐ and cost‐efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine‐tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis. Enantioselective synthesis ; Catalysis ; Tetrahydropyran ; Binary number ; Quantum chemical ; Fragment (logic) ; Fingerprint (computing) ; Computer science ; Chemistry ; Combinatorial chemistry ; Artificial intelligence ; Molecule ; Organic chemistry ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Debarati Dey | Pradipta Roy | Debashis De
Source: Journal of Molecular Modeling (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Machine learning and DFT-based combined framework for predicting transmission spectra of quantum-confined bio-molecular nanotube Quantum ; Computer science ; Hamiltonian (control theory) ; Spectral line ; Transmission (telecommunications) ; Ab initio ; Statistical physics ; Machine learning ; Artificial intelligence ; Materials science ; Algorithm ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization ; Telecommunications Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Molecular Junctions and Nanostructures
Authors: Kirill S. Erokhin | Evgeniy O. Pentsak | Vyacheslav R. Sorokin | Yury V. Agaev | Roman G. Zaytsev | В. И. Исаева | Valentine P. Ananikov
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Dynamic behavior of metal nanoparticles in MOF materials: analysis with electron microscopy and deep learning Electron microscopy is a key characterization technique for nanoscale systems, and electron microscopy images are typically recorded and analyzed in terms of the morphology of the objects under study in static mode. The emerging current trend is to analyze the dynamic behavior at the nanoscale observed during electron microscopy measurements. In this work, the study of the stability of MOF structures with different compositions and topologies under conditions of an electron microscope experiment revealed an unusual dynamic behavior of M NPs formed due to the electron-beam-induced transformation of specific frameworks. The transition to the liquid phase led to spatial movement, rapid sintering, and an increase in the M NPs size within seconds. In the case of copper nanoparticles, instantaneous sublimation was observed. The dynamic behavior of Co NPs was analyzed with a computational framework combining deep learning and classic computer vision techniques. The present study for the first time revealed unique information about the stability of a variety of MOFs under an electron beam and the dynamic behavior of the formed M NPs. The formation of Fe, Ni, Cu, and Co NPs was observed from a molecular framework with a specific subsequent behavior - a stable form for Fe, excessive dynamics for Co, and sublimation/condensation for Cu. Two important outcomes of the present study should be mentioned: (i) electron microscopy investigations of MOF samples should be made with care, as decomposition under an electron beam may lead to incorrect results and the appearance of "phantom" nanoparticles; and (ii) MOFs represent an excellent model for fundamental studies of molecular-to-nano transitions <i>in situ</i> in video mode, including a number of dynamic transformations. Sublimation (psychology) ; Materials science ; Nanoparticle ; Scanning electron microscope ; Nanotechnology ; Electron microscope ; Nanoscopic scale ; Chemical physics ; Chemical engineering ; Chemistry ; Optics ; Composite material ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Ashish Srivastava | Binayak Nahak
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2023-08-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantifying the impact of different material properties on Barkhausen noise generation from machined steel using machine learning techniques Barkhausen effect ; Overfitting ; Materials science ; Residual stress ; Surface roughness ; Root mean square ; Noise (video) ; Machine learning ; Material properties ; Artificial intelligence ; Computer science ; Composite material ; Artificial neural network ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Power Transformer Diagnostics and Insulation
Authors: Deepti Rekha Sahoo | Trinath Biswal
Source: Polymer Composites (article, 2023-05-03)
Field: Materials Science — Polymers and Plastics

Complex catalyzed synthesis and prediction of properties of poly(<scp>acrylonitrile‐co‐acrylamide</scp>)/crab shell powder composites by using artificial neural network Abstract In a nitrogen atmosphere, a novel composite, Poly(Acrylonitrile‐co‐Acrylamide)/crab shell powder, was synthesized by combining two monomers, acrylonitrile (AN) and acrylamide (Aam), with varying weight percentages of crab shell powder (CSP) as bio‐filler. The synthesized composite Poly (AN‐co‐Aam)/CSP, copolymer Poly (AN‐co‐Aam), and CSP were characterized using Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), thermogravimetric (TGA) analysis, derivative thermogravimetric (DTG) analysis, and a scanning electron microscope (SEM). The mechanical properties, like tensile, flexural, and impact strengths, were studied. The hardness, biodegradability, and water absorbency properties have been studied using appropriate methodologies. The SEM images and FTIR data confirm the appropriate synthesis of the composite material and the uniform distribution of CSP. The mechanical strength increases with the rise in concentration of CSP up to 20 wt. % and then gradually decreases. The thermal analysis reveals that the composite exhibits adequate thermal stability. The artificial neural network (ANN) model is used for the prediction of mechanical properties, which shows adequate results with the experimental results. The training and test datasets use a correlation factor associated with this analysis that is higher than 0.9, which confirms that the expected values of the mechanical performance are an excellent comparison to the experimental value. Thermogravimetric analysis ; Materials science ; Acrylonitrile ; Fourier transform infrared spectroscopy ; Composite material ; Flexural strength ; Composite number ; Thermal stability ; Ultimate tensile strength ; Scanning electron microscope ; Acrylamide ; Copolymer ; Chemical engineering ; Polymer Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties
Authors: Liguo Xu | Shuangxi Shi | Bin Kong | Deng Luo | Xiaoyong Zhang | Kechao Zhou
Source: Materials (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

Static Globularization Behavior and Artificial Neural Network Modeling during Post-Annealing of Wedge-Shaped Hot-Rolled Ti-55511 Alloy The globularization of the lamellar α phase by thermomechanical processing and subsequent annealing contributes to achieving the well-balanced strength and plasticity of titanium alloys. A high-throughput experimental method, wedge-shaped hot-rolling, was designed to obtain samples with gradient true strain distribution of 0~1.10. The samples with gradient strain distribution were annealed to obtain the gradient distribution of globularized α phase, which could rapidly assess the globularization fraction of α phase under different conditions. The static globularization behavior under various parameters was systematically studied. The applied prestrain provided the necessary driving force for static globularization during annealing. The substructure evolution and the boundary splitting occurred mainly at the early stage of annealing. The termination migration and the Ostwald ripening were dominant in the prolonged annealing. A backpropagation artificial neural network (BP-ANN) model for static globularization was developed, which coupled the factors of prestrain, annealing temperature, and annealing time. The average absolute relative errors (AARE) for the training and validation set are 3.17% and 3.22%, respectively. Further sensitivity analysis of the factors shows that the order of relative importance for static globularization is annealing temperature, prestrain and annealing time. The developed BP-ANN can precisely predict the static globularization kinetic curves without overfitting. Annealing (glass) ; Materials science ; Ostwald ripening ; Plasticity ; Composite material ; Nanotechnology Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels