jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuexing Han | Ruiqi Li | Shen Yang | Qiaochuan Chen | Bing Wang | Yi Liu
Source: Scientific Reports (article, 2022-07-28)
Field: Materials Science — Materials Chemistry

Center-environment feature models for materials image segmentation based on machine learning Materials properties depend not only on their compositions but also their microstructures under various processing conditions. So far, the analyses of complex microstructure images rely mostly on human experience, lack of automatic quantitative characterization methods. Machine learning provides an emerging vital tool to identify various complex materials phases in an intelligent manner. In this work, we propose a "center-environment segmentation" (CES) feature model for image segmentation based on machine learning method with environment features and the annotation input of domain knowledge. The CES model introduces the information of neighbourhood as the features of a given pixel, reflecting the relationships between the studied pixel and its surrounding environment. Then, an iterative integrated machine learning method is adopted to train and correct the image segmentation model. The CES model was successfully applied to segment seven different material images with complex texture ranging from steels to woods. The overall performance of the CES method in determining boundary contours is better than many conventional methods in the case study of the segmentation of steel image. This work shows that the iterative introduction of domain knowledge and environment features improve the accuracy of machine learning based image segmentation for various complex materials microstructures. Computer science ; Artificial intelligence ; Segmentation ; Image segmentation ; Pixel ; Feature (linguistics) ; Segmentation-based object categorization ; Machine learning ; Computer vision ; Pattern recognition (psychology) ; Scale-space segmentation ; Image texture ; Image processing ; Domain (mathematical analysis) ; Image (mathematics) ; Mathematics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Mineral Processing and Grinding
Authors: Ying He | Guohong Liu | Chengjun Li | Xiliang Yan
Source: Reviews of Environmental Contamination and Toxicology (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Reaching the Full Potential of Machine Learning in Mitigating Environmental Impacts of Functional Materials Computer science ; Sustainability ; Functional design ; Risk analysis (engineering) ; Set (abstract data type) ; Sustainable development ; Biochemical engineering ; Machine learning ; Artificial intelligence ; Engineering ; Software engineering ; Ecology Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Ali Rajabpour | Bohayra Mortazavi
Source: Condensed Matter (article, 2022-11-15)
Field: Materials Science — Materials Chemistry

Substrate Effect on the Thermal Expansion of 2D Materials: An Investigation by Machine Learning Interatomic Potentials The thermal expansion coefficient (TEC) of suspended two-dimensional (2D) nanomaterials is usually negative due to their ability for large out-of-plane deflection as the temperature increases. The presence of a substrate can nonetheless restrict the flexibility of 2D materials and significantly change their dimensional change by temperature. In this short communication, the thermal expansion coefficients of suspended and supported four popular 2D structures of graphene, phagraphene, C3N and BC3 monolayers is systematically investigated. For this purpose, we conduct molecular dynamics simulation, in which the atomic interactions are defined by highly accurate machine learning interatomic potentials. The obtained results show that by increasing the strength of the van der Waals interactions between the monolayer and the substrate, from 2 meV to 8 meV, the TEC for graphene and phagraphene increases from a negative value to a positive one; while the negative value for the C3N and BC3 structures is still retained. Analysis of molecular dynamics trajectories reveals that the substrate can significantly reduce the formation of out-of-plane wrinkles and consequently affect the value of TEC. The obtained results provide useful vision on the role of substrate on the complex thermal expansion responses of 2D materials. TEC ; Thermal expansion ; Graphene ; Monolayer ; Substrate (aquarium) ; Materials science ; Molecular dynamics ; van der Waals force ; Negative thermal expansion ; Chemical physics ; Thermal ; Nanomaterials ; Nanotechnology ; Chemistry ; Composite material ; Computational chemistry ; Thermodynamics ; Physics Thermal properties of materials ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Shuo Zhang | Yang Liu | Lei Xie
Source: arXiv (Cornell University) (preprint, 2022-06-06)
Field: Materials Science — Materials Chemistry

Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. However, most existing GNNs suffer from the limitations of insufficient modeling of diverse interactions, computational expensive operations, and ignorance of vectorial values. Here, we tackle these limitations by proposing a novel GNN model, Physics-aware Multiplex Graph Neural Network (PaxNet), to efficiently and accurately learn the representations of 3D molecules for both small organic compounds and macromolecule complexes. PaxNet separates the modeling of local and non-local interactions inspired by molecular mechanics, and reduces the expensive angle-related computations. Besides scalar properties, PaxNet can also predict vectorial properties by learning an associated vector for each atom. To evaluate the performance of PaxNet, we compare it with state-of-the-art baselines in two tasks. On small molecule dataset for predicting quantum chemical properties, PaxNet reduces the prediction error by 15% and uses 73% less memory than the best baseline. On macromolecule dataset for predicting protein-ligand binding affinities, PaxNet outperforms the best baseline while reducing the memory consumption by 33% and the inference time by 85%. Thus, PaxNet provides a universal, robust and accurate method for large-scale machine learning of molecules. Our code is available at https://github.com/zetayue/Physics-aware-Multiplex-GNN. Computer science ; Inference ; Graph ; Artificial intelligence ; Binding affinities ; Artificial neural network ; Machine learning ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Karina Núñez | Cristian Velasco-Merino | María Asensio | Julia Guerrero | J. C. Merino
Source: Polymers (article, 2022-09-05)
Field: Materials Science — Biomaterials

Rheological Method for Determining the Molecular Weight of Collagen Gels by Using a Machine Learning Technique This article presents, for the first time, the results of applying the rheological technique to measure the molecular weights (Mw) and their distributions (MwD) of highly hierarchical biomolecules, such as non-hydrolyzed collagen gels. Due to the high viscosity of the studied gels, the effect of the concentrations on the rheological tests was investigated. In addition, because these materials are highly sensitive to denaturation and degradation under mechanical stress and temperatures close to 40 °C, when frequency sweeps were applied, a mathematical adjustment of the data by machine learning techniques (artificial intelligence tools) was designed and implemented. Using the proposed method, collagen fibers of Mw close to 600 kDa were identified. To validate the proposed method, lower Mw species were obtained and characterized by both the proposed rheological method and traditional measurement techniques, such as chromatography and electrophoresis. The results of the tests confirmed the validity of the proposed method. It is a simple technique for obtaining more microstructural information on these biomolecules and, in turn, facilitating the design of new structural biomaterials with greater added value. Rheology ; Biomolecule ; Materials science ; Viscosity ; Biological system ; Stress (linguistics) ; Measure (data warehouse) ; Computer science ; Chromatography ; Nanotechnology ; Composite material ; Chemistry ; Data mining Collagen: Extraction and Characterization ; Engineering Technology and Methodologies ; Advanced Scientific Research Methods
Authors: Ni Wang | Hao Guan | Jin Wang | Jianli Zhou | Wenjie Gao | Wenxiang Jiang | Yuefei Zhang | Ze Zhang
Source: Materials Today Communications (article, 2022-11-17)
Field: Materials Science — Metals and Alloys

A deep learning-based approach for segmentation and identification of δ phase for Inconel 718 alloy with different compression deformation Segmentation ; Phase (matter) ; Materials science ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Inconel ; Computer science ; Artificial neural network ; Machine learning ; Alloy ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Additive Manufacturing Materials and Processes ; Welding Techniques and Residual Stresses
Authors: R.M.C. Pinto | J.H. Belo | João P. Araújo | D.J. Silva
Source: Journal of Magnetism and Magnetic Materials (article, 2022-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the magnetic measurements of first- and second-order phase transition magnetocaloric materials with artificial neural networks Magnetic refrigeration ; Thermomagnetic convection ; Materials science ; Magnetic field ; Artificial neural network ; Phase transition ; Magnetization ; Condensed matter physics ; Characterization (materials science) ; Ferrofluid ; Entropy (arrow of time) ; Statistical physics ; Thermodynamics ; Computer science ; Artificial intelligence ; Physics ; Nanotechnology Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Advanced Thermoelectric Materials and Devices
Authors: Atefeh Niknezhad | Nima Nabian | Mohammad Soleimani Lashkenari | Mohsen Ghorbani
Source: Process Safety and Environmental Protection (article, 2022-02-09)
Field: Materials Science — Polymers and Plastics

PANI-ZnO/Pt–Ru electrocatalyst for methanol oxidation: Synthesis, characterization, electrocatalytic performance and artificial neural network modeling Electrocatalyst ; Chronoamperometry ; Cyclic voltammetry ; Materials science ; Linear sweep voltammetry ; Nanocomposite ; Chemical engineering ; Fourier transform infrared spectroscopy ; Platinum ; Methanol ; Catalysis ; Ruthenium ; Inorganic chemistry ; Electrochemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Electrode ; Physical chemistry Conducting polymers and applications ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Shweta Jindal | Po‐Jen Hsu | Huu Trong Phan | Pei‐Kang Tsou | Jer‐Lai Kuo
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning Exploring the structure and properties of molecular clusters with accuracy using the <i>ab initio</i> methods is a resource intensive task due to the increasing cost of the <i>ab initio</i> methods and the number of distinct conformers as the size increases. The energy landscape of methanol clusters has been previously explored using computationally efficient empirical models to collect a database of structurally distinct minima, followed by re-optimization using <i>ab initio</i> methods. In this work, we propose a new method that utilizes the database of stable conformers and borrow the fragmentation concept of many-body-expansion (MBE) methods in <i>ab initio</i> methods to train a deep-learning machine learning (ML) model using SchNet. Picking 684 local minima of (CH<sub>3</sub>OH)<sub>5</sub> to (CH<sub>3</sub>OH)<sub>8</sub> from the existing database, we can generate ∼51 000 data points of one-body, two-body, three-body and four-body molecular systems to train an ML model to reach a mean absolute error (MAE) of 3.19 kJ mol<sup>-1</sup> (in energy) and 2.48 kJ mol<sup>-1</sup> Å<sup>-1</sup> (in forces) tested against <i>ab initio</i> calculations up to (CH<sub>3</sub>OH)<sub>14</sub>. This ML model is then used to create a database of low energy isomers of (CH<sub>3</sub>OH)<sub><i>n</i></sub> (<i>n</i> = 15-20). The proposed scheme can be applied to other hydrogen bonded molecular clusters with an accuracy of first-principles methods and computational speed of empirical force-fields. Maxima and minima ; Ab initio ; Energy landscape ; Potential energy ; Ab initio quantum chemistry methods ; Statistical physics ; Conformational isomerism ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Atomic physics ; Molecule ; Mathematics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Aditya lawerence Toppo | Swasti Dhagat | Satya Eswari Jujjavarapu
Source: Preparative Biochemistry & Biotechnology (article, 2022-04-20)
Field: Materials Science — Materials Chemistry

Comparative study of response surface methodology and artificial neural network for optimization of process parameters for synthesis of gold nanoparticles by <i>Desmostachya bipinnata</i> extract Green synthesis of nanoparticles has gained attention due to its eco-friendly and sustainable approach to synthesize nanoparticles at a reduced cost. Artificial neural network (ANN) and response surface model (RSM) are important to reduce experimental efforts in nanoparticle synthesis. In this work, optimization of gold nanoparticle synthesis by <i>Desmostachya bipinnata</i> extract was performed using the volume of plant extract, concentration of auric chloride, reaction time, pH, and temperature as process parameters, and the output was absorbance. The experimental design was obtained from RSM and the model was optimized further using ANN. Thirty-two experimental runs generated by RSM were performed and the results obtained experimentally were compared with those generated by RSM and ANN. Different algorithms of ANN were tested to obtain the best one. The optimization studies resulted in a maximum response for 20<sup>th</sup> run with 15 ml, 2.5 mM, 45 min, 7, and 40 °C as parameters. Optimized input parameters obtained by RSM were 10 ml, 2 mM, 30 min, 6, and 30 °C. The formation of gold nanoparticles was confirmed by UV spectroscopy, XRD, and SEM. Different algorithms of ANN, such as leven marquardt, scaled conjugate gradient, and bayesian network were used. Leven marquardt algorithm was found to be the most suitable algorithm for the current study. Response surface methodology ; Conjugate gradient method ; Artificial neural network ; Absorbance ; Nanoparticle ; Materials science ; Biological system ; Colloidal gold ; Central composite design ; Algorithm ; Mathematics ; Computer science ; Chemistry ; Nanotechnology ; Chromatography ; Machine learning Nanoparticles: synthesis and applications
Authors: Mikhail V. Polynski | Valentine P. Ananikov
Source: European Journal of Inorganic Chemistry (article, 2022-10-12)
Field: Chemistry — Organic Chemistry

Reductive Elimination or C−C Bond Activation with Model Ni, Pd, Pt Complexes? A High‐Accuracy Comparative Computational Analysis of Reactivity Abstract The development of Pd‐ and Ni‐catalyzed reactions for C−C bond formation is one of the primary driving forces in modern organic synthesis and the fine chemical industry. However, understanding the role of conformational mobility in reaction mechanisms is a long‐standing challenge. We highlight the effect of a multirotamer (multiconformer) system on the effective Gibbs free energy of activation in the key C−C coupling process and promote the use of a simplified version of multiconformer transition state theory that is straightforward to apply. Multivariate regression helped to quantitatively map the effect of coupled organic substituents (their structural and electronic parameters), as well as to determine the relative activity of metals. We provide computational evidence for solvent control of the equilibrium in RE/C−C‐bond activation for some model complexes. We also demonstrate that Ni complexes, being unique in the catalysis of sp 3 ‐sp 3 couplings, can be more challenging for machine learning and computational chemistry. The modeling was performed at an exceptionally high level, DLPNO‐CCSD(T)/CBS//RIJCOSX‐PBE0‐D4/def2‐TZVP. The Conclusions section contains an infographic summarizing the key findings related to the fields of cross‐coupling catalysis, machine learning in catalysis, and computational chemistry. Chemistry ; Catalysis ; Reactivity (psychology) ; Reductive elimination ; Gibbs free energy ; Computational chemistry ; Combinatorial chemistry ; Thermodynamics ; Organic chemistry Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Asmee Prabhu | Tej S. Choksi
Source: Current Opinion in Chemical Engineering (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Data-driven methods to predict the stability metrics of catalytic nanoparticles Nanoparticle ; Bimetallic strip ; Nanostructure ; Stability (learning theory) ; Nanotechnology ; Materials science ; Ab initio ; Computer science ; Chemistry ; Machine learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Junhyoung Gil | Takuji Oda
Source: Journal of Chemical Theory and Computation (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

Accurate and Efficient Calculation of the Solution Enthalpy and Diffusivity of Solutes in Liquid Metals Using Machine Learning Potential Liquid metals (LMs) have various applications in energy systems, such as coolants in advanced nuclear reactors. In addition, room-temperature LMs are attracting attention as flexible components in robotics and electronics and as novel chemical reaction media to form low-dimensional materials. In many of these applications, the capabilities of LMs can be further enhanced if one can better understand and control the chemical reactivity of LMs, which is largely affected by the stability and mobility of solutes in LMs. Here, we propose an automated method using a machine learning moment tensor potential to efficiently calculate the solution enthalpy and diffusivity of solutes in LMs. From several test cases in liquid Na, we demonstrate that the method can achieve an accuracy comparable to that of a direct calculation using first-principles molecular dynamics, while significantly reducing the calculation cost to the order of 1/10 to 1/100. The method is expected to contribute to the advancement of LM chemistry and the development of new LMs. Computer science ; Enthalpy ; Thermal diffusivity ; Coolant ; Chemistry ; Process engineering ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties
Authors: B. Eckert | Stefan Aschauer | P. Holl | Petra Majewski | Thomas Zabel | L. Strüder
Source: IEEE Transactions on Nuclear Science (article, 2022-04-21)
Field: Materials Science — Surfaces, Coatings and Films

Electron Imaging Reconstruction for Pixelated Semiconductor Tracking Detectors in Transmission Electron Microscopes Using the Approach of Convolutional Neural Networks Energetic electrons of several hundred-kilo electron volts (keV) as used in transmission electron microscopy applications do not deposit their energy completely at their point of entry (PoE). Instead, in a detector with a thickness of 450 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> , they produce randomly 3-D tracks often extending over many pixels. The length and the shape of these tracks strongly depend on the primary energy of the electrons. Statistically, an electron deposits most of its energy at the end of its track. The output of a tracking detector system is the pixelated 2-D projection of the energy deposition. The proposed convolutional neural network (CNN) is capable of performing a frame-based event analysis and PoE reconstruction. The resulting modulation transfer function (MTF) is 0.77 at a Nyquist frequency of 0.5 obtained from a slanted edge. It is based on a U-net with a specially designed loss function using the confusion matrix. The input data to the CNN only need to be offset corrected. The output of the CNN is a PoE map containing the probability of a PoE of an electron for each pixel and frame. The architecture of the designed CNN also allows reconstructing different frame sizes without retraining the network. Detector ; Convolutional neural network ; Physics ; Pixel ; Optics ; Computer science ; Semiconductor detector ; Electron ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; CCD and CMOS Imaging Sensors
Authors: Yue Wan | Benben Liao | Chang‐Yu Hsieh | Shengyu Zhang
Source: arXiv (Cornell University) (preprint, 2022-01-29)
Field: Materials Science — Materials Chemistry

Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different levels of dependency on additional chemical knowledge. In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing. Via the proposed local attention head, the model can jointly encode the molecular sequence and graph, and efficiently exchange information between the local reactive region and the global reaction context. Retroformer reaches the new state-of-the-art accuracy for the end-to-end template-free retrosynthesis, and improves over many strong baselines on better molecule and reaction validity. In addition, its generative procedure is highly interpretable and controllable. Overall, Retroformer pushes the limits of the reaction reasoning ability of deep generative models. Retrosynthetic analysis ; Transformer ; Computer science ; Generative grammar ; Cheminformatics ; Artificial intelligence ; Machine translation ; Graph ; Dependency (UML) ; Machine learning ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation