jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongliang Zhang | Tae‐Ho Kim | Jadin Swarts | Zefeng Yu | Ranran Su | Longfei Liu | William S. Howland | G. Lucadamo | Adrien Couet
Source: Corrosion Science (article, 2022-09-13)
Field: Materials Science — Materials Chemistry

Nano-porosity effects on corrosion rate of Zr alloys using nanoscale microscopy coupled to machine learning Materials science ; Corrosion ; Oxide ; Porosity ; Texture (cosmology) ; Suboxide ; Substrate (aquarium) ; Chemical engineering ; Metallurgy ; Composite material Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: Lin Chen | Xiaotian Li | Sicong Ma | Y. Hu | Cheng Shang | Zhi‐Pan Liu
Source: ACS Catalysis (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Highly Selective Low-Temperature Acetylene Semihydrogenation Guided by Multiscale Machine Learning Catalytic hydrogenation is the key measure to remove traces of acetylene in ethylene in the petroleum industry. Herein we report a highly selective and stable nanocatalyst, Pd1Ag3 supported on rutile-TiO2 (r-TiO2) annealed at unusually high temperatures (>750 °C), which can purify ethylene mixed with 1% of acetylene at 97.2% selectivity and 100% acetylene conversion below 100 °C. The selectivity is more than 10% higher than that in our previous work. This advance is achieved by a rational catalyst search featuring machine learning to correlate catalyst synthesis conditions with the catalyst performance and a large-scale machine-learning atomic simulation for disclosing composite atomic structures at high temperatures. We show that Pd1Ag3 alloy crystal nanoparticles form until 727 °C and the alloy nanoparticles grow epitaxially on r-TiO2(110) via its {111} facets. The maximum exposure of the alloy {111} surface is the key to the highest selectivity among the different supports tested, as confirmed by high-resolution characterization experiments and microkinetics simulations. Our results demonstrate the power of multiscale machine-learning tools in guiding the catalyst design and clarifying the atomic nature in complex heterogeneous catalysis. Acetylene ; Selectivity ; Catalysis ; Materials science ; Nanoparticle ; Chemical engineering ; Alloy ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jason M. Stevens | Jun Li | Eric M. Simmons | Steven R. Wisniewski | Stacey DiSomma | Kenneth J. Fraunhoffer | Peng Geng | Bo Hao | Erika W. Jackson
Source: Organometallics (article, 2022-06-03)
Field: Chemistry — Inorganic Chemistry

Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning An expansive data set containing 33 substrates, 36 unique monophosphine ligands, and two solvents was produced for the NiCl2·6H2O catalyzed aryl (pseudo)halide borylation with tetrahydroxydiboron for a total of 1632 reactions. Exploratory data analysis revealed excellent reaction performance with simple triarylphosphines (P(p-F-Ph)3 and P(p-Anis)3) and mixed aryl-alkyl phosphines (PPh2Cy), in addition to the previously established high performance with Cy-JohnPhos. The data were used to train machine learning models that predicted out of sample reaction performance with a root-mean-square error of 18.4. The important features extracted from the models identified three phosphine parameters that offered reliable reactivity thresholds for identifying optimal ligand performance. The predictive models showed reasonable performance for predicting reaction yields employing ligands not included in model training, while the important feature boundaries accurately classified the performance of 10 of the 12 external ligands examined. Chemistry ; Borylation ; Aryl ; Catalysis ; Alkyl ; Ligand (biochemistry) ; Phosphine ; Reactivity (psychology) ; Halide ; Expansive ; Training set ; Combinatorial chemistry ; Organic chemistry ; Artificial intelligence ; Computer science ; Thermodynamics Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry
Authors: Pallavi Vanjari | Reddi Kamesh | K. Yamuna Rani
Source: Materials Today Proceedings (article, 2022-11-26)
Field: Materials Science — Materials Chemistry

Machine learning models representing catalytic activity for direct catalytic CO2 hydrogenation to methanol Catalysis ; Methanol ; Support vector machine ; Selectivity ; Artificial neural network ; Regression analysis ; Linear regression ; Regression ; Random forest ; Kriging ; Calcination ; Computer science ; Biological system ; Chemistry ; Machine learning ; Mathematics ; Statistics ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Carbon Dioxide Capture Technologies
Authors: Hajime Kimizuka | Bo Thomsen | Motoyuki Shiga
Source: Journal of Physics Energy (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Artificial neural network-based path integral simulations of hydrogen isotope diffusion in palladium Abstract The contribution of nuclear quantum effects (NQEs) to the kinetics and dynamics of interstitial H isotopes in face-centered cubic Pd was intensively investigated using several path-integral techniques, along with a newly developed machine-learning interatomic potential based on artificial neural networks for Pd–H alloys. The diffusion coefficients ( D ) of protium, deuterium, and tritium in Pd were predicted over a wide temperature range (50–1500 K) based on quantum transition-state theory (QTST) combined with path-integral molecular-dynamics simulations. The importance of NQEs even at high temperatures was illustrated in terms of the characteristic temperature dependence of the activation free energies for H-isotope migration in Pd. This illuminates the overall picture of anomalous D crossovers among the three H isotopes in Pd. In addition, the D of protium in Pd was directly computed using two approximate quantum-dynamics methods based on Feynman’s path-integral theory, i.e. centroid molecular dynamics (CMD) and ring-polymer molecular dynamics (RPMD), in the temperature range 370–1500 K. The D values obtained from the CMD and RPMD simulations were very similar and agreed better with the reported experimental values than the QTST results in this temperature range. Our machine learning-based path-integral calculations elucidate the underlying quantum nature of the ‘reversed S’-type nonlinear behavior of D for the three H isotopes in Pd on the Arrhenius plots. Path integral formulation ; Kinetic isotope effect ; Molecular dynamics ; Deuterium ; Diffusion ; Quantum ; Atmospheric temperature range ; Chemistry ; Range (aeronautics) ; Isotope ; Feynman diagram ; Thermodynamics ; Statistical physics ; Physics ; Atomic physics ; Materials science ; Computational chemistry ; Quantum mechanics Nuclear Materials and Properties ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Geun Ho Gu | Miriam Lee | Yousung Jung | Dionisios G. Vlachos
Source: Nature Communications (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10<sup>8</sup> potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally. Transition metal ; Space (punctuation) ; Nanotechnology ; Chemistry ; Transition (genetics) ; Chemical physics ; Materials science ; Computer science ; Catalysis ; Biochemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Praneeth S Ramesh | Tarak K. Patra
Source: Soft Matter (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Polymer sequence design <i>via</i> molecular simulation-based active learning Molecular-scale interactions and chemical structures offer an enormous opportunity to tune material properties. However, designing materials from their molecular scale is a grand challenge owing to the practical limitations in exploring astronomically large design spaces using traditional experimental or computational methods. Advancements in data science and machine learning have produced a host of tools and techniques that can address this problem and facilitate the efficient exploration of large search spaces. In this work, a blended approach integrating physics-based methods, machine learning techniques and uncertainty quantification is implemented to effectively screen a macromolecular sequence space and design target structures. Here, we survey and assess the efficacy of data-driven methods within the framework of active learning for a challenging design problem, <i>viz.</i>, sequence optimization of a copolymer. We report the impact of surrogate models, kernels, and initial conditions on the convergence of the active learning method for the sequence design problem. This work establishes optimal strategies and hyperparameters for efficient inverse design of polymer sequences <i>via</i> active learning. Computer science ; Sequence (biology) ; Active learning (machine learning) ; Hyperparameter ; Scale (ratio) ; Convergence (economics) ; Machine learning ; Chemical space ; Sequence space ; Space (punctuation) ; Artificial intelligence ; Surrogate model ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Christoph Schattauer | Milica Todorović | Kunal Ghosh | Patrick Rinke | Florian Libisch
Source: npj Computational Materials (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Machine learning sparse tight-binding parameters for defects Abstract We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them for our further investigations. We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure, local density of states, transport and level spacing simulations for two common defects in single layer graphene. Our machine learning approach achieves results comparable to maximally localized Wannier functions (i.e., DFT accuracy) without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time. It is general and can be applied to a wide range of other materials, enabling accurate large-scale simulations of material properties in the presence of different defects. Tight binding ; Range (aeronautics) ; Electronic structure ; Computer science ; Perceptron ; Artificial neural network ; Wannier function ; Artificial intelligence ; Algorithm ; Machine learning ; Statistical physics ; Materials science ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Samantha Durdy | Michael W. Gaultois | Vladimir V. Gusev | Danushka Bollegala | Matthew J. Rosseinsky
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties Kernelised LOCO-CV can measure the extrapolatory power of an algorithm. Random projections are a versatile benchmark for composition featurisation. Machine learning ; Artificial intelligence ; Computer science ; Metric (unit) ; Kernel (algebra) ; Predictive power ; Process (computing) ; Cluster (spacecraft) ; Skew ; Function (biology) ; Range (aeronautics) ; Representation (politics) ; Data mining ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Robbert Rietkerk | A. Heine | Werner Riedel
Source: International Journal of Impact Engineering (article, 2022-12-05)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning model for prediction of long-rod penetration depth in a semi-infinite target Artificial neural network ; Machine learning ; Artificial intelligence ; Computer science ; Experimental data ; Generalization error ; Generalization ; Penetration (warfare) ; Regularization (linguistics) ; Penetration depth ; Mathematics ; Physics ; Mathematical analysis ; Optics High-Velocity Impact and Material Behavior ; Force Microscopy Techniques and Applications ; Non-Destructive Testing Techniques
Authors: Dilshana Shanavas Rasheeda | Alberto Martín Santa Daría | Benjamin Schröder | Edit Mátyus | Jörg Behler
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark In recent years, machine learning potentials (MLP) for atomistic simulations have attracted a lot of attention in chemistry and materials science. Many new approaches have been developed with the primary aim to transfer the accuracy of electronic structure calculations to large condensed systems containing thousands of atoms. In spite of these advances, the reliability of modern MLPs in reproducing the subtle details of the multi-dimensional potential-energy surface is still difficult to assess for such systems. On the other hand, moderately sized systems enabling the application of tools for thorough and systematic quality-control are nowadays rarely investigated. In this work we use benchmark-quality harmonic and anharmonic vibrational frequencies as a sensitive probe for the validation of high-dimensional neural network potentials. For the case of the formic acid dimer, a frequently studied model system for which stringent spectroscopic data became recently available, we show that high-quality frequencies can be obtained from state-of-the-art calculations in excellent agreement with coupled cluster theory and experimental data. Anharmonicity ; Benchmark (surveying) ; Artificial neural network ; Potential energy surface ; Computer science ; Formic acid ; Reliability (semiconductor) ; Statistical physics ; Materials science ; Chemistry ; Artificial intelligence ; Physics ; Ab initio ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Katarina Batalović | Jana Radaković | Bojana Paskaš Mamula | Bojana Kuzmanović | Mirjana Medić Ilić
Source: Advanced Theory and Simulations (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Predicting the Heat of Hydride Formation by Graph Neural Network ‐ Exploring the Structure–Property Relation for Metal Hydrides Abstract Theoretical tools or structure–property relations that enable the prediction of metal hydrides are of enormous interest in developing new hydrogen storage materials. Density functional theory (DFT) is one such approach that provides accurate hydride formation energies, which, if complemented with vibrational zero‐point energy and other contributions, provides accurate hydride formation enthalpies. However, this approach is time consuming and, therefore, often avoided, hindering the modeling of experimental behavior. The recent implementation of graph neural networks (GNN) in materials science enables fast prediction of crystal formation energy with a DFT accuracy. Starting from the MatErials Graph Network (MEGNet), transfer learning is applied to develop a model for predicting hydride formation enthalpy based on the crystal structure of the starting intermetallic. Excellent accuracy is achieved for Mg‐containing alloys, allowing the screening of the Mg─Ni─M ternary intermetallics. In addition, data containing matching experimental properties and crystal structure of metal hydrides are provided, enabling future development. Intermetallic ; Hydride ; Ternary operation ; Artificial neural network ; Materials science ; Crystal structure prediction ; Crystal structure ; Hydrogen storage ; Enthalpy ; Thermodynamics ; Metal ; Density functional theory ; Computational chemistry ; Computer science ; Chemistry ; Artificial intelligence ; Metallurgy ; Physics ; Crystallography ; Alloy Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Maicon Pierre Lourenço | Jiří Hostaš | Lizandra Barrios Herrera | Patrizia Calaminici | Andreas M. Köster | Alain Tchagang | Dennis R. Salahub
Source: Journal of Computational Chemistry (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

<scp>GAMaterial</scp>—A genetic‐algorithm software for material design and discovery Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti<sub>6</sub> O<sub>12</sub> cluster, doping Al in Si<sub>11</sub> (4Al@Si<sub>11</sub> ) and Na<sub>10</sub> supported on graphene (Na<sub>10</sub> @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo<sub>8</sub> C<sub>4</sub> cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial. Cluster (spacecraft) ; Dopant ; Software ; Genetic algorithm ; Computer science ; Graphene ; Algorithm ; Computational science ; Doping ; Nanotechnology ; Materials science ; Biological system ; Machine learning Machine Learning in Materials Science ; 2D Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Krishna Chaitanya Pitike | Wahyu Setyawan
Source: Journal of Nuclear Materials (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Accurate Fe–He machine learning potential for studying He effects in BCC-Fe Bubble ; Molecular dynamics ; Cluster (spacecraft) ; Density functional theory ; Helium ; Materials science ; Atom (system on chip) ; Lattice constant ; Neutron ; Atomic physics ; Molecular physics ; Thermodynamics ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Nuclear physics ; Computer science ; Mechanics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Chen Qu | Anthony J. Kearsley | Barry I. Schneider | Walid Keyrouz | Thomas C. Allison
Source: Journal of Molecular Graphics and Modelling (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

Graph convolutional neural network applied to the prediction of normal boiling point NIST ; Sketch ; Computer science ; Boiling point ; Graph ; Artificial neural network ; Artificial intelligence ; Convolutional neural network ; Data set ; Machine learning ; Set (abstract data type) ; Data mining ; Algorithm ; Theoretical computer science ; Thermodynamics ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Chi‐Hua Yu | Changyan Wu | Markus J. Buehler
Source: Computational Materials Science (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Deep learning based design of porous graphene for enhanced mechanical resilience Graphene ; Materials science ; Artificial intelligence ; Deep learning ; Multiscale modeling ; Computer science ; Fracture (geology) ; Brittleness ; Generalizability theory ; Molecular dynamics ; Resilience (materials science) ; Nanotechnology ; Ultimate tensile strength ; Machine learning ; Composite material Graphene research and applications ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Ling-Heng Luo | Sida Huang | Cheng Shang | Zhi‐Pan Liu
Source: ACS Catalysis (article, 2022-05-11)
Field: Materials Science — Materials Chemistry

Resolving Activation Entropy of CO Oxidation under the Solid–Gas and Solid–Liquid Conditions from Machine Learning Simulation The free-energy barrier and its temperature dependence and the activation entropy of chemical reactions are key quantities to reaction kinetics but are very challenging to predict accurately, particularly for reactions in complex environments. Herein, by combining global neural network potential with umbrella sampling molecular dynamics (MD), we are able to resolve the activation entropy of CO oxidation on Pt(111), a prototype heterogeneous catalytic reaction, with two different mechanisms (CO reacting with the adsorbed atomic O, CO + O, and with the adsorbed OH, CO + OH) in the absence and presence of an aqueous solution. We show that (i) CO + OH reaction is always kinetically more preferable than CO + O reaction despite that the presence of an aqueous solution increases significantly the barrier of CO + OH reaction, and (ii) the activation entropy, although with the absolute value being below 35 J·mol–1·K–1, equivalent to 0.1 eV at 300 K, contributes differently to the free-energy barrier depending on the reaction conditions: it is negative under the solid–gas conditions but turns out to be positive under the solid–liquid conditions. The configuration entropy dominates the activation entropy, to which the classic harmonic approximation to account for the vibrational entropy is a poor approximation. Our results are of significance for understanding and predicting the free-energy barrier of catalytic recombination reactions in general. Chemistry ; Catalysis ; Aqueous solution ; Adsorption ; Activation energy ; Entropy of activation ; Thermodynamics ; Entropy (arrow of time) ; Kinetics ; Reaction mechanism ; Chemical kinetics ; Physical chemistry ; Reaction rate constant ; Organic chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science