jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bhavya Mehta | Vijay Kharche | Sandeep S. Udmale
Source:  (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Accelerating the Search for Stable Full Heusler Compounds through Machine Learning Applications for Heusler compounds are expanding in topological insulators, magnetocaloric, spintronics, and superconductivity areas. These substances are expanding the boundaries of science and offering answers to engineering problems. Our work demonstrates a discovery engine that can predict the crystal structures and chemical characteristics of 1107 Full Heusler compounds by implementing a Machine Learning approach trained with elemental descriptor data. Our approach is 50 times faster than rule-based and diffraction techniques, with a true positive rate of 0.99 for every random combination of elements on more than 1,000,000 candidates. We also compute the formation energies of these novel compounds to filter out 144 highly stable Heuslers that coincide with existing research and density functional theory trends to validate and support our findings. Spintronics ; Density functional theory ; Work (physics) ; Materials science ; Computer science ; Machine learning ; Condensed matter physics ; Ferromagnetism ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Inorganic Chemistry and Materials
Authors: José G. F. Coutinho | Ce Guo | Tim Todman | Wayne Luk
Source:  (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Exploring Machine Learning Adoption in Customisable Processor Design Machine learning has recently been deployed in many areas of processor design with promising results. This paper presents novel capabilities that enable exploration of design flow customisation involving machine learning adoption for customisable processor architectures. We introduce Orbit-ML, an approach that supports customisation of design flow for parametric architecture descriptions to allow experiments with machine learning techniques for optimising processor designs. Orbit-ML facilitates exploring domain-specific design flow with various tools such as design space exploration, source-level transformation, high-level synthesis, and static and dynamic optimisations. Our Orbit-ML prototype illustrates a customised design flow targeting a tunable processor, showing that Bayesian learning can find optimised designs by traversing only 4% of the total design space. Computer science ; Design space exploration ; Design flow ; Computer architecture ; Domain (mathematical analysis) ; Microarchitecture ; Architecture ; Artificial intelligence ; Embedded system Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Software Engineering Research
Authors: R.S. Monzamodeth | J.F. Flores-Alvarez | I. Reyes-Chaparro | Fermín Castillo | B. Campillo | O. Flores
Source: Materials Today Proceedings (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Artificial neural networks for predicting potentiodynamic tests of brass 70-30 Artificial neural network ; Gradient descent ; Polarization (electrochemistry) ; Anode ; Electrolyte ; Brass ; Materials science ; Correlation coefficient ; Hyperbolic function ; Coefficient of determination ; Electrode ; Analytical Chemistry (journal) ; Computer science ; Mathematics ; Chemistry ; Artificial intelligence ; Metallurgy ; Machine learning ; Chromatography ; Mathematical analysis Anodic Oxide Films and Nanostructures ; Corrosion Behavior and Inhibition ; Nanoporous metals and alloys
Authors: Sangni Xun | Fang Liu
Source: ChemRxiv (preprint, 2023-10-31)
Field: Materials Science — Materials Chemistry

Comparison of Machine-Learning and Classical ForceFields in Simulating the Solvation of Small OrganicMolecules in Acetonitrile Machine learning force fields (MLFFs) have emerged as a new method for molecular simulation that combines the accuracy of ab initio approaches with the computational efficiency of classical force fields. However, the performance of MLFFs in describing the solvation configuration has yet to be explored. Here, we compare and contrast the performance of ANI-1ccx MLFF, the GAFF classical force field, and the ab initio molecular dynamics (AIMD) in simulating nine organic solutes in acetonitrile solvents. We examine the solvent-solute interaction described by these methods from four aspects: the solute conformation landscape, the solvation shell structure, the structure and dynamics of the O-H⋯N hydrogen bond, and the dynamics of the first solvation shell. For solute conformation description, ANI-1ccx and GAFF both yield minima that agree with density functional theory optimization for rigid solutes. However, their results diverge for flexible solutes. For solvation shell structure description, ANI-1ccx agrees better with AIMD on the location of the first solvent shell than GAFF does. For the description of the O-H⋯N hydrogen bond formed between acetonitrile and the solute, ANI-1ccx generates stronger hydrogen bonds with shorter bond lengths, wider bond angles, and longer hydrogen bond lifetimes, agreeing better with DFT-optimized structure. ANI-1ccx also describes a more frequent exchange of acetonitrile molecules in and out of the first solvation shell than GAFF. Our study demonstrates the potential benefits of utilizing MLFF for simulating solution-phase dynamics and generating solvation configurations. Solvation ; Solvation shell ; Hydrogen bond ; Force field (fiction) ; Chemistry ; Acetonitrile ; Molecular dynamics ; Implicit solvation ; Ab initio ; Molecule ; Computational chemistry ; Chemical physics ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Various Chemistry Research Topics
Authors: Romain Botella | Andrey A. Kistanov
Source: The Journal of Physical Chemistry Letters (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning. This work is paving the way for using simulated vibrational spectra as a tool to identify materials with defined stoichiometry, enabling the separation of genuine vibrational features of pure phases from morphological and defect-induced signals. Quantization (signal processing) ; Molecular vibration ; Kernel (algebra) ; Molecular physics ; Statistical physics ; Ab initio ; Phonon ; Spectroscopy ; Materials science ; Computer science ; Infrared spectroscopy ; Computational physics ; Algorithm ; Chemistry ; Physics ; Mathematics ; Raman spectroscopy ; Optics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Ju Huang | Seung‐Jae Shin | Kasper Tolborg | Alex M. Ganose | Gabriel Krenzer | Aron Walsh
Source: ChemRxiv (preprint, 2023-03-24)
Field: Chemistry — Inorganic Chemistry

Room-temperature stacking disorder in layered covalent-organic frameworks from machine-learning force fields The local structures of layered covalent-organic frameworks (COFs) deviate from the average crystal structures assigned from X-ray diffraction experiments. For two prototype COFs of Tp-Azo and DAAQ-TFP, density functional theory calculations have shown that the eclipsed structure is not an energy minimum and that the internal energy is lowered for an inclined stacking arrangement. Here we explore the structural disorder of these frameworks at 300 K through molecular dynamics (MD) simulations using an on-the-fly machine learning force field (MLFF). We find that an initially eclipsed stacking mode spontaneously distorts to form a zigzag configuration that lowers the free energy of the crystal. The simulated diffraction patterns show good agreement with experimental observations. The dynamic disorder from the MLFF MD trajectories is found to persist in mesoscale MD simulations of 155 thousand atoms, giving further confidence in our conclusions. Our simulations show that the stacking behaviour of layered COFs is more complicated than previously understood. Stacking ; Zigzag ; Molecular dynamics ; Force field (fiction) ; Covalent bond ; Materials science ; Crystal (programming language) ; Diffraction ; Density functional theory ; Crystallography ; Chemical physics ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry ; Computer science ; Optics ; Nuclear magnetic resonance ; Geometry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Jinlong Zhang | Cornelis F. De Hoop | Qinglin Wu
Source: BioResources (article, 2023-11-02)
Field: Chemistry — Spectroscopy

Environmentally friendly, low thermal conductivity, fire retarding, mechanically robust cellulose nanofibril aerogels and their use for early fire alarm sensors in thermally insulating sustainable building applications As a way to reduce carbon emissions, manufacturing an environmentally friendly and biodegradable cellulose aerogel material with low thermal conductivity, excellent mechanical, and flame retarding property to replace conventional foams is of significant interest in thermally insulating building applications. Primary questions to be addressed include how to design fire retarding and mechanically robust wood derived cellulose nanofibril aerogels as alternatives of expanded polystyrene and rigid polyurethane foams; how to develop aerogel materials in industrial-level manufacturing; and whether it is possible to further develop its early fire alarm sensors with ultra-low temperature sensitive limit and long signal durability by experimental and machine learning artificial intelligence approaches for thermally insulating sustainable building applications. Materials science ; Aerogel ; Environmentally friendly ; Thermal conductivity ; Cellulose ; Composite material ; Durability ; Polystyrene ; Polymer ; Chemical engineering ; Engineering Aerogels and thermal insulation ; Advanced Cellulose Research Studies ; High voltage insulation and dielectric phenomena
Source: Angewandte Chemie (article, 2023-08-30)
Field: Chemistry — Physical and Theoretical Chemistry

Lukáš Grajciar Graphical Abstract “The most exciting thing about my research is that it works! Our machine learning models are general and robust enough to ‘discover’ new chemistry that we have not thought about beforehand, but which often makes a lot of sense a posteriori … I celebrate success by starting to think about what to do next.” Find out more about Lukáš Grajciar in his Introducing … Profile. A priori and a posteriori ; Epistemology ; Computer science ; Chemistry ; Data science ; Philosophy History and advancements in chemistry ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Marjolein de Jager | Frank Smallenburg | Laura Filion
Source: arXiv (Cornell University) (preprint, 2023-06-09)
Field: Materials Science — Materials Chemistry

In search of a precursor for crystal nucleation of hard and charged colloids The interplay between crystal nucleation and the structure of the metastable fluid has been a topic of significant debate over recent years. In particular, it has been suggested that even in simple model systems such as hard or charged colloids, crystal nucleation might be foreshadowed by significant fluctuations in local structure around the location where the first nucleus arises. We investigate this using computer simulations of spontaneous nucleation events in both hard and charged colloidal particles. To detect local structural variations, we use both standard and unsupervised machine learning methods capable of finding hidden structures in the metastable fluid phase. We track numerous nucleation events for the face-centered cubic and body-centered cubic crystal on a local level, and demonstrate that all signs of crystallinity emerge simultaneously from the very start of the nucleation process. We thus conclude that there is no precursor for the nucleation of charged colloids. Nucleation ; Metastability ; Crystal (programming language) ; Crystallinity ; Chemical physics ; Colloidal particle ; Colloidal crystal ; Materials science ; Colloid ; Physics ; Crystallography ; Chemistry ; Thermodynamics ; Physical chemistry ; Computer science ; Quantum mechanics Material Dynamics and Properties
Authors: Neeraj Nepal | Madhav Prasad Ghimire
Source: Journal of Nepal Physical Society (article, 2023-12-31)
Field: Materials Science — Materials Chemistry

Predicting Band Gap of Transition Metal Trihalides using Machine Learning Machine Learning (ML) is a tool that finds pattern in data sets for analysis and prediction and is very useful in various sectors such as materials discovery, predicting material properties, medical science, and data science etc. From predicting lattice parameters, stability, band gap, and many other material properties with low cost and good accuracy, ML is becoming popular among material scientists. Recently, a new compound in a two-dimensional (2D) realm (transition metal trihalides) is getting immense interest due to its intriguing properties such as magnetic order in lower dimensions. In this work, we have selected transition metal trihalides to predict band gaps using ML. For the dataset, we have used some compound and elemental properties extracted from the materials project database and periodic table for the features set, and band gaps within the range of 0-3 eV for target variables. We performed the least absolute shrinkage and selection operator (LASSO) for feature selection and selected five features from a set of 24 features. After training and testing our data in four types of ML algorithms (support vector regression (SVR), kernel ridge regression (KRR), gradient boosted regression tree (GBRT), and random forest regression (RFR)), the GBRT model is found to be best with the lowest mean absolute error (MAE) &lt; 0.27 eV and root mean squared error (RMSE) &lt; 0.34 eV. Transition metal ; Materials science ; Transition (genetics) ; Engineering physics ; Optoelectronics ; Nanotechnology ; Chemistry ; Physics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Thermoelectric Materials and Devices
Authors: Benedikt Prifling | Matthias Neumann | Simon Hein | Timo Danner | Emanuel Heider | Alice Hoffmann | Philipp Rieder | André Hilger | Markus Osenberg | Ingo Manke | Margret Wohlfahrt‐Mehrens | Arnulf Latz | Volker Schmidt
Source: Energy Technology (article, 2023-05-01)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative Comparison of Different Approaches for Reconstructing the Carbon‐Binder Domain from Tomographic Image Data of Cathodes in Lithium‐Ion Batteries and Its Influence on Electrochemical Properties The cover shows the 3D microstructure of a cathode in a lithium-ion battery, which is crucial for the resulting electrochemical performance. In particular, the lateral faces show two different three-phase reconstructions obtained by a closing approach based on EDX data and by a neural network trained with correlative microscopy. The background shows the fluorine mapping of an EDX image, which provides insight into the spatial distribution of the binder. More details can be found in article number 2200784, Benedikt Prifling and co-workers. Materials science ; Electrochemistry ; Cathode ; Microstructure ; Lithium (medication) ; Ion ; Lithium-ion battery ; Battery (electricity) ; Carbon fibers ; Electrode ; Chemical engineering ; Composite material ; Chemistry ; Electrical engineering ; Composite number ; Engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Digital Image Processing Techniques
Authors: Matt Brown | Jonathan M. Skelton | Paul L. A. Popelier
Source: ChemRxiv (preprint, 2023-06-13)
Field: Chemistry — Physical and Theoretical Chemistry

Application of the FFLUX Force Field to Molecular Crystals:A Study of Formamide In this work we present the first application of the quantum chemical topology force field, FFLUX, to the solid state. FFLUX utilises Gaussian process regression machine learning models trained on data from the interacting quantum atoms partitioning scheme to predict atomic energies and flexible multipole moments that change with geometry. Here, the ambient (α) and high-pressure (β) polymorphs of formamide are used as test systems and optimised using FFLUX. Optimising the structures with increasing multipolar ranks indicates that the lattice parameters of the α-phase differ by less than 5% to the experimental structure when multipole moments up to the quadrupole moment are used. These differences are found to be in line with dispersion-corrected density functional theory. Lattice dynamics calculations are also found to be possible using FFLUX, yielding harmonic phonon spectra comparable to dispersion-corrected DFT while enabling larger supercells to be considered than is typically possible with first-principles calculations. These promising results indicate that FFLUX can be used to accurately determine properties of molecular solids that are difficult to access using DFT, including the structural dynamics, free energies and properties at finite temperature. Multipole expansion ; Quadrupole ; Density functional theory ; Force field (fiction) ; Phonon ; Molecular dynamics ; Lattice (music) ; Formamide ; Physics ; Condensed matter physics ; Chemistry ; Quantum mechanics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Jiawei Wu | P. Hu | Haifeng Wang
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Aqueous growth of titania subnanoparticles: an understanding of the ultrasmall visible-light-absorbing unit of (TiO<sub>2</sub>)<sub>8</sub>(H<sub>2</sub>O)<sub>16</sub> An atomistic understanding of the initial hydrothermal growth of titania remains crucial for the development of nanosized materials, where the presence of water strongly affects the particle growth in comparison to the vapor-phase growth. Herein, we explore the structural evolution of aqueous titania from its salt precursors and determine the nanoparticle configurations in the practical environment by invoking <i>ab initio</i> molecular dynamic simulations and a machine-learning accelerated structural search. Thermodynamically, Ti(OH)<sub>4</sub>·2H<sub>2</sub>O serving as the hydrated monomer undergoes planar-to-tubular-to-spherical multistage growth in the Ti(OH)<sub>4</sub>/H<sub>2</sub>O hydrothermal system, in which large-sized (TiO<sub>2</sub>)<sub><i>n</i></sub>(H<sub>2</sub>O)<sub><i>m</i></sub> particles (<i>n</i> = 1-20) are generated <i>via</i> the olation/oxolation reaction. Importantly, in a mixture of particles of different sizes, we identify that (TiO<sub>2</sub>)<sub>8</sub>(H<sub>2</sub>O)<sub>16</sub> is one of the most abundant species in solution with peculiar metastability and exhibits extraordinary visible-light absorption ability, which may be the smallest aqueous titania subnanoparticle in the form of suspension and worth exploring for photocatalytic applications. Hydrothermal circulation ; Aqueous solution ; Monomer ; Materials science ; Planar ; Unit (ring theory) ; Crystallography ; Chemistry ; Chemical engineering ; Physical chemistry ; Polymer ; Mathematics ; Computer science ; Composite material Polyoxometalates: Synthesis and Applications ; Catalysis and Oxidation Reactions ; TiO2 Photocatalysis and Solar Cells
Authors: Ki Sung Jung | Tarek Echekki | Jacqueline H. Chen | Mohammad Affan Khalil
Source: arXiv (Cornell University) (preprint, 2023-12-01)
Field: Chemistry — Spectroscopy

Transfer learning for predicting source terms of principal component transport in chemically reactive flow The objective of this study is to evaluate whether the number of requisite training samples can be reduced with the use of various transfer learning models for predicting, for example, the chemical source terms of the data-driven reduced-order model that represents the homogeneous ignition process of a hydrogen/air mixture. Principal component analysis is applied to reduce the dimensionality of the hydrogen/air mixture in composition space. Artificial neural networks (ANNs) are used to tabulate the reaction rates of principal components, and subsequently, a system of ordinary differential equations is solved. As the number of training samples decreases at the target task (i.e.,for T0 &gt; 1000 K and various phi), the reduced-order model fails to predict the ignition evolution of a hydrogen/air mixture. Three transfer learning strategies are then applied to the training of the ANN model with a sparse dataset. The performance of the reduced-order model with a sparse dataset is found to be remarkably enhanced if the training of the ANN model is restricted by a regularization term that controls the degree of knowledge transfer from source to target tasks. To this end, a novel transfer learning method is introduced, parameter control via partial initialization and regularization (PaPIR), whereby the amount of knowledge transferred is systemically adjusted for the initialization and regularization of the ANN model in the target task. It is found that an additional performance gain can be achieved by changing the initialization scheme of the ANN model in the target task when the task similarity between source and target tasks is relatively low. Initialization ; Principal component analysis ; Computer science ; Transfer of learning ; Artificial neural network ; Regularization (linguistics) ; Curse of dimensionality ; Artificial intelligence ; Machine learning Spectroscopy and Laser Applications ; Advanced Combustion Engine Technologies ; Fault Detection and Control Systems
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-06-13)
Field: Chemistry — Inorganic Chemistry

Gas Separation Selectivity Prediction Based on Finely Designed Descriptors Adsorption-based techniques for gas separation using nanoporous materials are widely used and hold a promising future, but systematic identification of the best-performing materials for a given application is still an open problem. For that task, we need to estimate selectivity at different operating conditions (temperature, pressure) on a large set of nanoporous structures. To this aim, we have developed a machine learning-assisted screening process based on a fast grid calculation of interaction energies, in addition to newly designed geometrical descriptors to predict ambient-pressure selectivity. As a proof of concept, we tested our methodology for the separation of a 20-80 xenon/krypton mixture at 298 K and 1 atm in the nanoporous materials of the CoRE MOF 2019 database. Based on a standard train/test split of the dataset, our model is promising with an RMSE of 2.5 on the ambient-pressure selectivity values of the test set and 0.06 on their base-10 logarithm. This method can thence be used to pre-select the best performing materials for a more thorough investigation. Nanoporous ; Logarithm ; Selectivity ; Test set ; Computer science ; Set (abstract data type) ; Process (computing) ; Machine learning ; Materials science ; Chemistry ; Mathematics ; Nanotechnology Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Catalytic Processes in Materials Science