jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rodrigo Hormázabal | Jeong Won Kang | Kiho Park | Dae Ryook Yang
Source: Journal of Chemical Information and Modeling (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks In this study, a framework for the prediction of thermophysical properties based on transfer learning from existing estimation models is explored. The predictive capabilities of conventional group-contribution methods and traditional machine-learning approaches rely heavily on the availability of experimental datasets and their uncertainty. Through the use of a pretraining scheme, which leverages the knowledge established by other estimation methods, improved prediction models for thermophysical properties can be obtained after fine-tuning networks with more accurate experimental data. As our experiments show, for the case of critical properties of compounds, this pipeline not only improves the performance of the models on commonly found organic structures but can also help these models generalize to less explored areas of chemical space, where experimental data is scarce, such as inorganics and heavier organic compounds. Transfer learning from estimation models data also allows for graph-based deep learning models to create more flexible molecular features over a bigger chemical space, which leads to improved predictive capabilities and can give insights into the relationship between molecular structures and thermophysical properties. The generated molecular features can discriminate behavior discrepancy between isomers without the need of additional parameters. Also, this approach shows better robustness to outliers in experimental datasets. Computer science ; Transfer of learning ; Robustness (evolution) ; Outlier ; Chemical space ; Machine learning ; Artificial intelligence ; Graph ; Experimental data ; Scratch ; Pipeline (software) ; Deep learning ; Data mining ; Theoretical computer science ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alejandro A. Franco | Amanda S. Barnard
Source: Batteries & Supercaps (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Electrochemical Energy Storage Accelerating battery research: This special collection is devoted to the field of Artificial Intelligence, including Machine Learning, applied to electrochemical energy storage systems. Electrochemical energy storage ; Energy storage ; Computer science ; Battery (electricity) ; Electrochemical energy conversion ; Field (mathematics) ; Artificial intelligence ; Electrochemistry ; Supercapacitor ; Chemistry ; Physics ; Thermodynamics ; Power (physics) ; Electrode ; Mathematics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Misato Akiyoshi | Koki Ikemoto | Hiroyuki Isobe
Source: Chemistry - An Asian Journal (article, 2022-11-25)
Field: Materials Science — Materials Chemistry

Tier‐grown Expansion of Design‐of‐Experiments Parameter Spaces for Synthesis of a Nanometer‐scale Macrocycle A method to find optimum synthetic conditions was devised by combining a data-driven empirical model with a traditional mechanistic model. In this method, an experimental parameter space was empirically obtained by Design-of-Experiments optimizations with machine-learning supplements and was strategically expanded by examination of the mechanistic model of the reaction paths. An extra tier grown on the original 3×3×3 parameter space succeeded in allocating an optimum reaction condition in the expanded 3×3×4 parameter space. The method was specifically devised for the synthesis of a macrocycle, [n]cyclo-meta-phenylenes ([n]CMP), and the largest congener with n=12 was synthesized and fully characterized for the first time. Crystallographic and photophysical analyses revealed favorable features of [12]CMP for the material applications. Nanometre ; Parameter space ; Space (punctuation) ; Scale (ratio) ; Reaction conditions ; Congener ; Computer science ; Materials science ; Algorithm ; Biological system ; Mathematics ; Nanotechnology ; Chemistry ; Geometry ; Physics ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Paolo Restuccia | E. Ahmad | N. M. Harrison
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A transferable prediction model of molecular adsorption on metals based on adsorbate and substrate properties Surface adsorption is one of the fundamental processes in numerous fields, including catalysis, the environment, energy and medicine. The development of an adsorption model which provides an effective prediction of binding energy in minutes has been a long term goal in surface and interface science. The solution has been elusive as identifying the intrinsic determinants of the adsorption energy for various compositions, structures and environments is non-trivial. We introduce a new and flexible model for predicting adsorption energies to metal substrates. The model is based on easily computed, intrinsic properties of the substrate and adsorbate, which are the same for all the considered systems. It is parameterised using machine learning based on first-principles calculations of probe molecules (<i>e.g.</i>, H<sub>2</sub>O, CO<sub>2</sub>, O<sub>2</sub>, N<sub>2</sub>) adsorbed to a range of pure metal substrates. The model predicts the computed dissociative adsorption energy to metal surfaces with a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV for the large database of molecular adsorption energies provided by Catalysis-Hub.org which have a range of 15 eV. As the model is based on pre-computed quantities it provides near-instantaneous estimates of adsorption energies and it is sufficiently accurate to eliminate around 90% of candidates in screening study of new adsorbates. The model, therefore, significantly enhances current efforts to identify new molecular coatings in many applied research fields. Adsorption ; Range (aeronautics) ; Substrate (aquarium) ; Binding energy ; Catalysis ; Molecule ; Materials science ; Metal ; Chemical physics ; Chemistry ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Atomic physics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Matteo Cobelli | Paddy Cahalane | Stefano Sanvito
Source: Physical review. B./Physical review. B (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

Local inversion of the chemical environment representations Machine-learning generative methods for material design are constructed by representing a given chemical structure, either a solid or a molecule, over appropriate atomic features, generally called structural descriptors. These must be fully descriptive of the system, must facilitate the training process, and must be invertible, so that one can extract the atomic configurations corresponding to the output of the model. In general, this last requirement is not automatically satisfied by the most efficient structural descriptors, namely the representation is not directly invertible. Such a drawback severely limits our freedom of choice in selecting the most appropriate descriptors for the problem, and thus our flexibility to construct generative models. In this paper, we present an optimization method capable of inverting the local many-body descriptors of the chemical environment, back to a Cartesian representation. This is not a global inversion scheme, but it allows one to find the Cartesian representation of variations of known structures, such as those produced by molecular distortions. The algorithm, which is implemented together with the bispectrum representation, is demonstrated for a number of molecules with different bonding structures and atomic species. The scheme presented here thus represents a convenient approach to the local inversion of structural descriptors, enabling the construction of structural generative models. Computer science ; Invertible matrix ; Inversion (geology) ; Representation (politics) ; Generative grammar ; Generative model ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Indranil Roy | Hamdy Abouelella | Rajnikant V. Umretiya | Subhrajit Roychowdhury | Bojun Feng | Sandipp Krishnan Ravi | Sayan Ghosh | Raúl B. Rebak | Daniel M Ruscitto | Vipul Gupta | Andrew Hoffman
Source: Research Square (preprint, 2022-08-08)
Field: Materials Science — Materials Chemistry

Understanding Impacts of Chemistry on Oxidation of FeCrAl Alloys Abstract Traditionally, FeCrAl alloys played an important role in high-temperature applications due to their ability to form a passive Al oxide film at temperatures above ~800°C. Recently, FeCrAl alloys became of interest for the application of accident tolerant nuclear fuel cladding. This study covers work done at GE Research for better understanding the role of Al, Cr, and Mo in oxidation kinetics and thermodynamics. Several models and commercial prototype alloys have been tested in hydrothermal corrosion autoclave loops, at low temperature steam exposure (~400°C), high temperature steam exposure (~1000°C or higher), and high temperature air exposures. The results provide insights on how chromium and aluminum play a significant role in both high temperature and low temperature oxidation of FeCrAl. Additionally, machine learning tools are used to gain further insights on both predicting future optimized chemistries for balancing the properties of hydrothermal corrosion, low and high temperature steam oxidation, and thermal aging (which is exacerbated due to radiation in a nuclear reactor environment). GE plans to use this framework to further optimize the FeCrAl alloy system for use in nuclear reactor environments. High-temperature corrosion ; Corrosion ; Cladding (metalworking) ; Autoclave ; Hydrothermal circulation ; Alloy ; Metallurgy ; Nuclear engineering ; Oxide ; Materials science ; Nuclear reactor ; Chemical engineering ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Gyoung S. Na | Hyun Woo Kim
Source: Chemical Communications (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Contrastive representation learning of inorganic materials to overcome lack of training datasets Data representation forms a feature space where forms data distribution that is one of the key factors determining the prediction accuracy of machine learning (ML). In particular, the data representation is crucial to handle small and biased training datasets, which is the main challenge of ML in chemical applications. In this paper, we propose a data-agnostic representation method that automatically and universally generates a vector-shaped and target-specified representation of crystal structures. By employing the new materials representation of the proposed method, the prediction capabilities of ML algorithms were highly improved on small training datasets and transfer learning tasks. Moreover, the prediction accuracies of ML algorithms were improved by 28.89-30.87% in extrapolation problems to predict the physical properties of the materials in unknown material groups. The source code of EMRL is publicly available at https://github.com/ngs00/emrl/tree/master/EMRL. Representation (politics) ; Extrapolation ; Computer science ; Feature (linguistics) ; Code (set theory) ; Machine learning ; Training set ; Feature vector ; Artificial intelligence ; Feature learning ; External Data Representation ; Key (lock) ; Tree (set theory) ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: James A. Stewart
Source: Computational Materials Science (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition Thin film ; Materials science ; Microstructure ; Physical vapor deposition ; Chemical vapor deposition ; Deposition (geology) ; Combustion chemical vapor deposition ; Mesoscale meteorology ; Fabrication ; Nanotechnology ; Phase (matter) ; Crystallite ; Carbon film ; Composite material ; Metallurgy ; Chemistry ; Geology Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Fluid Dynamics and Thin Films
Authors: Seongjin Park | Jeong-Woo Son | Jaeyeon Yu | Jongwon Lee
Source: Sensors (article, 2022-04-21)
Field: Chemistry — Spectroscopy

Standoff Detection and Identification of Liquid Chemicals on a Reflective Substrate Using a Wavelength-Tunable Quantum Cascade Laser Standoff chemical detection and identification techniques are necessary for ensuring safe exposure to dangerous substances. Molecular fingerprints of unknown chemicals can be measured using wavelength-tunable quantum cascade lasers operating in long-wavelength infrared. In this work, we present a method that can identify liquid chemicals on a reflective substrate via diffuse reflection spectra measurement from 50 cm away and multiple nonlinear regression analysis. Experimental measurements and numerical analyses were conducted for different chemical surface densities and angles of light incidence using diethyl phthalate (DEP) and dimethyl methylphosphonate (DMMP). Candidate substances can be classified using a deep learning model to reduce analysis time. Quantum cascade laser ; Cascade ; Wavelength ; Laser ; Materials science ; Substrate (aquarium) ; Optoelectronics ; Reflection (computer programming) ; Optics ; Dimethyl methylphosphonate ; Infrared ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Computer science ; Organic chemistry ; Physics Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Ling Lan | Chia-Hao Liu | Qiang Du | Simon J. L. Billinge
Source: Journal of Applied Crystallography (article, 2022-05-16)
Field: Materials Science — Materials Chemistry

Robustness test of the <i>spacegroupMining</i> model for determining space groups from atomic pair distribution function data Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this work, the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters r max , Q max , Q damp and atomic displacement parameters. Robustness (evolution) ; Convolutional neural network ; Experimental data ; Algorithm ; Function (biology) ; Data set ; Distribution function ; Computer science ; Distribution (mathematics) ; Test data ; Statistical physics ; Mathematics ; Biological system ; Physics ; Artificial intelligence ; Statistics ; Mathematical analysis ; Chemistry ; Thermodynamics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Fernando Meneses | David F. Wise | Daniela Pagliero | Pablo R. Zangara | Siddharth Dhomkar | Carlos A. Meriles
Source: Physical Review Applied (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Toward Deep-Learning-Assisted Spectrally Resolved Imaging of Magnetic Noise Recent progress in the application of color centers to nanoscale spin sensing makes the combined use of noise spectroscopy and scanning probe imaging an attractive route for the characterization of arbitrary material systems. Unfortunately, the traditional approach to characterizing environmental magnetic field fluctuations from the measured probe signal typically requires the experimenter's input, thus complicating the implementation of automated imaging protocols based on spectrally resolved noise. Here, we probe the response of color centers in diamond in the presence of externally engineered random magnetic signals and implement a deep neural network to methodically extract information on their associated spectral densities. Building on a long sequence of successive measurements under different types of stimuli, we show that our network manages to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field with good fidelity under a broad set of conditions and with only a minimal measured data set, even in the presence of substantial experimental noise. These proof-of-principle results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging. Noise (video) ; Fidelity ; Computer science ; Artificial neural network ; High fidelity ; Set (abstract data type) ; Artificial intelligence ; SIGNAL (programming language) ; Physics ; Acoustics ; Telecommunications Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides
Authors: Ritaban Dutta | Ling Chen | D. Renshaw | Daniel Liang
Source: PLoS ONE (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Artificial intelligence automates the characterization of reversibly actuating planar-flow-casted NiTi shape memory alloy foil Nickel-Titanium (NiTi) shape memory alloys (SMAs) are smart materials able to recover their original shape under thermal stimulus. Near-net-shape NiTi SMA foils of 2 meters in length and width of 30 mm have been successfully produced by a planar flow casting facility at CSIRO, opening possibilities of wider applications of SMA foils. The study also focuses on establishing a fully automated experimental system for the characterisation of their reversible actuation, significantly improving SMA foils adaptation into real applications. Artificial Intelligence involving Computer Vision and Machine Learning based methods were successfully employed in the development of the automation SMA characterization process. The study finds that an Extreme Gradient Boosting (XGBoost) Regression model based predictive system experimented with over 175,000 video samples could achieve 99% overall prediction accuracy. Generalisation capability of the proposed system makes a significant contribution towards the efficient optimisation of the material design to produce high quality 30 mm SMA foils. Shape-memory alloy ; Nickel titanium ; SMA* ; Materials science ; FOIL method ; Planar ; Smart material ; Computer science ; Artificial intelligence ; Actuator ; Automation ; Mechanical engineering ; Composite material ; Algorithm ; Computer graphics (images) ; Engineering Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing
Authors: Abdul Hamid Ganie | Irfan Ur Rahman | Muhammad Sulaiman | Kamsing Nonlaopon
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Solution of Nonlinear Reaction-Diffusion Model in Porous Catalysts Arising in Micro-Vessel and Soft Tissue Using a Metaheuristic The steady-state reactive transport model (RTM) is a generalization of the nonlinear reaction-diffusion model in porous catalysts. The RTM is expressed as a non-linear ordinary differential equation of second-order with boundary conditions. Artificial neural network (ANN), Particle swarm optimization (PSO), and hybrid of PSO-SQP (Sequential Quadratic Programming) are used to obtain accurate, approximate solutions to the non-linear RTM. The proposed technique is applied to three different cases of non-linear RTM. The properties of the nonlinear reactive transport model in porous catalysts are investigated by considering various cases based on variation in the half-saturation concentration “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> ” and the characteristic reaction rate “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> .” The stability, reliability, and exactness of the proposed technique are established through comparison with the outcomes of the standard numerical procedure with the RK4 method and along with the different performance indices, which are Root-Mean-Square Error (RMSE), (TIC), Absolute Error (AE), and Mean Absolute Deviation (MAD). Catalysis ; Diffusion ; Materials science ; Porosity ; Nonlinear system ; Reaction–diffusion system ; Biological system ; Chemical engineering ; Porous medium ; Computer science ; Biomedical engineering ; Thermodynamics ; Composite material ; Chemistry ; Engineering ; Organic chemistry ; Physics Dendrimers and Hyperbranched Polymers ; Lattice Boltzmann Simulation Studies ; Fluid Dynamics Simulations and Interactions
Authors: Satadeep Bhattacharjee | Seung‐Cheol Lee
Source: Journal of Magnetism and Magnetic Materials (article, 2022-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A general rule for predicting the magnetic moment of Cobalt-based Heusler compounds using compressed sensing and density functional theory Density functional theory ; Magnetic moment ; Heusler compound ; Valence electron ; Materials science ; Electron ; Valence (chemistry) ; Electronic structure ; Doping ; Transition metal ; Condensed matter physics ; Physics ; Quantum mechanics ; Chemistry Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Wei-Qi Yan | Rui-Jia Zhou | Lijun Jing | Yueqiang Cao | Jinghong Zhou | Zhi‐Jun Sui | Wei Li | De Chen | Xinggui Zhou | Yi‐An Zhu
Source: Catalysis Science & Technology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Computer-aided bimetallic catalyst screening for ester selective hydrogenation Bimetallic catalyst screening for ester selective hydrogenation has been performed by combining microkinetic analysis and machine learning methods. Bimetallic strip ; Catalysis ; Chemistry ; Combinatorial chemistry ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tobias Strohmann | Pere Barriobero‐Vila | Joachim Gussone | David Melching | Andreas Stark | Norbert Schell | Guillermo Requena
Source: Scripta Materialia (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Can unsupervised machine learning boost the on-site analysis of in situ synchrotron diffraction data? Autoencoder ; Materials science ; Diffraction ; Transformation (genetics) ; Synchrotron ; In situ ; Phase (matter) ; Unsupervised learning ; Synchrotron radiation ; Alloy ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Biological system ; Metallurgy ; Optics ; Deep learning ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties