jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cuauhtémoc Núñez Valencia | William Bang Lomholdt | Matthew Helmi Leth Larsen | Thomas W. Hansen | Jakob Schiøtz
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Beam induced heating in electron microscopy modeled with machine learning interatomic potentials We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and eqivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces. This can be used both to improve the networks during the training phase, and to validate the performance when simulating systems too big to be described by DFT. The energy deposited into the nanoparticle by the electron beam is estimated by measuring the mean free path of the electrons and the average energy loss, both are done with Electron Energy Loss Spectroscopy (EELS) within the microscope. In combination, this allows us to predict the heating incurred by a nanoparticle as a function of its size, its shape, the support material, and the electron beam energy and intensity. Electron ; Materials science ; Electron microscope ; Cathode ray ; Nanoparticle ; Atomic physics ; Mean free path ; Molecular physics ; Computational physics ; Optics ; Nanotechnology ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Martin Liebetrau | Yvonne Dorenkamp | Oliver Bünermann | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2023-12-15)
Field: Materials Science — Materials Chemistry

Hydrogen Atom Scattering at the Al$_2$O$_3$(0001) Surface: A Combined Experimental and Theoretical Study Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields -- from heterogeneous catalysis to corrosion. In this work, we present a joint experimental and theoretical effort to gain insights into the atomistic details of hydrogen atom scattering at the $α$-Al$_2$O$_3$(0001) surface. Surprisingly, this system has been hardly studied to date, although hydrogen atoms as well as $α$-Al$_2$O$_3$ are omnipresent in catalysis as reactive species and support oxide, respectively. We address this system by performing hydrogen atom beam scattering experiments and molecular dynamics (MD) simulations based on a high-dimensional machine learning potential trained to density functional theory data. Using this combination of methods we are able to probe the properties of the multidimensional potential energy surface governing the scattering process. Specifically, we compare the angular distribution and the kinetic energy loss of the scattered atoms obtained in experiment with a large number of MD trajectories, which, moreover, allow to identify the underlying impact sites at the surface. Scattering ; Atom (system on chip) ; Hydrogen ; Hydrogen atom ; Atomic physics ; Molecular dynamics ; Oxide ; Chemical physics ; Density functional theory ; Surface (topology) ; Work (physics) ; Catalysis ; Kinetic energy ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics ; Computer science ; Metallurgy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Adrian Gordon | Jason D. Goodpaster
Source: ChemRxiv (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

Accurate Rate Calculations for Hydrogen Atom Abstraction from Methane by Hydroxyl Radical: A Combination of RPMD, Machine Learning Potentials, and Active Learning We employ an active learning scheme to train a neural network potential which is then utilized in RPMD simulations to calculate the thermal rate coefficients for a hydrogen atom abstraction from methane by hydroxyl radical. We use a active learning approach to determine which geometries from umbrella sampling trajectories, performed using our neural network potential, should be included in our CCSD(T) training data to improve the neural network potential. After sufficient accuracy of the neural network potential is achieved, we use this potential energy surface in RPMD trajectories to calculate the reaction rate using the Bennet-Chandler method. We find this approach to be exceptionally accurate, with errors in the rate less than 20% at 200K and decreasing to 10% at 1000K. Next, we examine the limit of how much data was required to obtain highly accurate neural network potentials and find that only 7,000 CCSD(T) are required to preserve the accuracy of the network. Finally, we examine a transfer learning approach for this reaction, and find that transfer learning does not provide sufficient accuracy for rate calculations. Abstraction ; Hydrogen atom abstraction ; Artificial neural network ; Potential energy surface ; Computer science ; Chemistry ; Hydrogen atom ; Atom (system on chip) ; Transfer of learning ; Hydrogen ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Carson Koppel | Brandon Wilfong | Allana G. Iwanicki | Elizabeth Hedrick | Tanya R. Berry | Tyrel M. McQueen
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

Machine-guided Design of Oxidation Resistant Superconductors for Quantum Information Applications Decoherence in superconducting qubits has long been attributed to two level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by superconducting tantalum, resulting in a tripling of transmon qubit lifetimes (T1). One of these surface variables, the identity, thickness, and quality of the native surface oxide, is thought to play a major role as tantalum only has one oxide whereas niobium has several. Here we report the development of a thermodynamic metric to rank materials based on their potential to form a well-defined, thin, surface oxide. We first compute this metric for known binary and ternary metal alloys using data available from Materials Project, and experimentally validate the strengths and limits of this metric through preparation and controlled oxidation of 8 known metal alloys. Then we train a convolutional neural network to predict the value of this metric from atomic composition and atomic properties. This allows us to compute the metric for materials that are not present in materials project, including a large selection of known superconductors, and, when combined with Tc, allow us to identify new candidate superconductors for quantum information science (QISE) applications. We test the oxidation resistance of a pair of these predictions experimentally. Our results are expected to lay the foundation for tailored and rapid selection of improved superconductors for QISE. Superconductivity ; Qubit ; Niobium ; Metric (unit) ; Oxide ; Ternary operation ; Transmon ; Materials science ; Quantum decoherence ; Quantum ; Quantum computer ; Topology (electrical circuits) ; Quantum information ; Condensed matter physics ; Physics ; Computer science ; Statistical physics ; Nanotechnology ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Lucas Lang | Henrique Musseli Cezar | Ludwik Adamowicz | Thomas Bondo Pedersen
Source: ChemRxiv (preprint, 2023-10-11)
Field: Chemistry — Physical and Theoretical Chemistry

A Quantum Definition of Molecular Structure Molecular structure, a key concept of chemistry, has remained elusive from the perspective of all-particle quantum mechanics despite many efforts. Viewing molecular structure as a manifestation of strong statistical correlation between nuclear positions, we propose a practical method based on Markov chain Monte Carlo sampling and unsupervised machine learning. Application to the D3+ molecule unambiguously shows that it possesses an equilateral triangular structure. These results provide a major step forward in our understanding of molecular structure from fundamental quantum principles. Equilateral triangle ; Statistical physics ; Computer science ; Key (lock) ; Markov chain ; Sampling (signal processing) ; Quantum mechanics ; Physics ; Mathematics ; Machine learning ; Geometry History and advancements in chemistry ; Molecular spectroscopy and chirality ; Chemical Thermodynamics and Molecular Structure
Authors: Zonghuan Liu | Anna Zhao | Yunhao Zhang | Chuo Li | Tianhe Wang | Jing Xu
Source:  (article, 2023-12-18)
Field: Chemistry — Analytical Chemistry

Study on identification method for Artemisia argyi floss Mugwort floss, valued in traditional Chinese medicine, varies in therapeutic properties and market price based on origin and production year. Traditional identification methods, due to their destructiveness and low accuracy, often confuse mugwort floss with A.stolonifera and cause a testing waste. Hyperspectral Imaging, a non-contact technique, offers potential for rapid identification of such medicinal materials. In this paper, we explore hyperspectral data to differentiate mugwort and A.stolonifera using deep learning and neural networks. Using a massive hyperspectral dataset from mugwort and wormwood from two regions across four years, we analyzed performance using metrics like Accuracy, Specificity, and F1 Score. The self-attention-based Backpropagation Neural Network model showed the most promising results for accurate classification. This approach has potential future applications in various fields using Hyperspectral data Mugwort ; Hyperspectral imaging ; Artificial intelligence ; Identification (biology) ; Pattern recognition (psychology) ; Computer science ; Artificial neural network ; Backpropagation ; Machine learning ; Medicine ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Traditional Chinese Medicine Analysis
Authors: Herman Rull | Markus Fischer | Stefan Kühn
Source: arXiv (Cornell University) (preprint, 2023-04-06)
Field: Chemistry — Spectroscopy

NMR shift prediction from small data quantities Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model which is able to achieve good results with comparatively low amounts of data. We show this by predicting 19F and 13C NMR chemical shifts of small molecules in specific solvents. Chemical shift ; Computer science ; Carbon-13 NMR ; Fluorine-19 NMR ; Machine learning ; Chemistry ; Artificial intelligence ; Data mining ; Nuclear magnetic resonance spectroscopy ; Physical chemistry ; Organic chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Carson Koppel | Brandon Wilfong | Allana G. Iwanicki | Elizabeth Hedrick | Tanya Berry | Tyrel M. McQueen
Source: Inorganics (article, 2023-03-11)
Field: Materials Science — Materials Chemistry

Machine-Guided Design of Oxidation-Resistant Superconductors for Quantum Information Applications Decoherence in superconducting qubits has long been attributed to two-level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by superconducting tantalum, resulting in a tripling of transmon qubit lifetimes (T1). The identity, thickness, and quality of the native surface oxide, is thought to play a major role, as tantalum only has one oxide whereas niobium has several. Here we report the development of a thermodynamic metric to rank materials based on their potential to form a well-defined, thin, surface oxide. We first computed this metric for known binary and ternary metal alloys using data available from the Materials Project and experimentally validated the strengths and limits of this metric through the preparation and controlled oxidation of eight known metal alloys. Then we trained a convolutional neural network to predict the value of this metric from atomic composition and atomic properties. This allowed us to compute the metric for materials that are not present in the Materials Project, including a large selection of known superconductors, and, when combined with Tc, allowed us to identify new candidate superconductors for quantum information science and engineering (QISE) applications. We tested the oxidation resistance of a pair of these predictions experimentally. Our results are expected to lay the foundation for the tailored and rapid selection of improved superconductors for QISE. Superconductivity ; Niobium ; Qubit ; Metric (unit) ; Quantum decoherence ; Oxide ; Materials science ; Ternary operation ; Quantum computer ; Transmon ; Quantum ; Quantum information ; Topology (electrical circuits) ; Condensed matter physics ; Physics ; Computer science ; Nanotechnology ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Michael Craig | Felix Kleuker | Michal Bajdich | Max García‐Melchor
Source: ChemRxiv (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

FEFOS: A method to derive oxide formation energies from oxidation states Herein we report a method to extract formation energies from oxidation states, which we call FEFOS. This new scheme predicts the formation energies of binary oxides through analysing unary oxide formation energies as a function of their oxidation states. Taking averages of fitted quadratic equations that represent how elements respond to oxidation and reduction, the weights of these averages are determined by constraing the compound to be neutral. The performance of FEFOS results in mean absolute errors of ca. 0.10 eV/atom when tested against Materials Project data for oxides with general formulas A1–zBzO, A1–zBzO1.5, and A1–zBzO2 with specific coordinations. Our FEFOS method not only allows for the prediction of binary oxide formation energies with low variance and high interpretability, but also compares well with state-of-the-art deep learning methods without being biased by training data and the need for large resources to compute it. Finally, we discuss the potential applications of the FEFOS method in tackling the problem of inverse design for catalysis. Oxide ; Binary number ; Unary operation ; Inverse ; Interpretability ; Atom (system on chip) ; Quadratic equation ; Chemistry ; Materials science ; Mathematics ; Computer science ; Discrete mathematics ; Arithmetic ; Machine learning Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Cordelia Sealy
Source: Materials Today (article, 2023-05-01)
Field: Materials Science — Biomaterials

Star-shaped plankton inspire complex structures Star (game theory) ; Plankton ; Oceanography ; Physics ; Geology ; Astrophysics Calcium Carbonate Crystallization and Inhibition ; Paleontology and Stratigraphy of Fossils ; Marine Biology and Ecology Research
Authors: Behnam Ahmadikia | Adolph L. Beyerlein | Jonathan M. Hestroffer | M. Arul Kumar | Irene J. Beyerlein
Source: Research Square (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength. Microstructure ; Slip (aerodynamics) ; Materials science ; Lüders band ; Plasticity ; Titanium alloy ; Deformation bands ; Composite material ; Alloy ; Physics ; Thermodynamics Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Mechanical stress and fatigue analysis
Authors: Penghui Sun | jiajia wang | Pei Liu | Zhilin Dong
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Tablet Identification Using Ensemble Convolutional Neural Networks and Vibrational Spectroscopy Convolutional neural network ; Identification (biology) ; Spectroscopy ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Astronomy ; Biology Spectroscopy and Chemometric Analyses
Authors: Chunxiu Wang | Jiancheng Yin | Tao Shen | Yingli Liu
Source:  (article, 2023-07-30)
Field: Materials Science — Materials Chemistry

A Weighted Graph-based Method with Mapping of Material Composition to Target Performance Materials informatics is a method that combines statistics, machine learning techniques, and high-throughput computing to promote materials discovery and design. In this study, we propose a novel framework based on weighted graph for mapping material compositions to properties. The framework utilizes distributed representations of elemental compositions and incorporates soft attention mechanisms to capture the importance of different elements in the materials. By leveraging attention mechanism and residual network, the model can effectively extract features and generate fixed-length representations for materials, enabling efficient information aggregation. Experimental results on diverse datasets from the Material Project database demonstrate the effectiveness of our proposed method, achieving less errors compared to other methods that do not consider the material structure. This data-driven deep learning method showcases the potential of materials informatics in providing accurate predictions for various material properties. Computer science ; Graph ; Composition (language) ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Daiki Koge | Naoaki Ono | Shigehiko Kanaya
Source: arXiv (Cornell University) (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Pre-training of Molecular GNNs via Conditional Boltzmann Generator Learning representations of molecular structures using deep learning is a fundamental problem in molecular property prediction tasks. Molecules inherently exist in the real world as three-dimensional structures; furthermore, they are not static but in continuous motion in the 3D Euclidean space, forming a potential energy surface. Therefore, it is desirable to generate multiple conformations in advance and extract molecular representations using a 4D-QSAR model that incorporates multiple conformations. However, this approach is impractical for drug and material discovery tasks because of the computational cost of obtaining multiple conformations. To address this issue, we propose a pre-training method for molecular GNNs using an existing dataset of molecular conformations to generate a latent vector universal to multiple conformations from a 2D molecular graph. Our method, called Boltzmann GNN, is formulated by maximizing the conditional marginal likelihood of a conditional generative model for conformations generation. We show that our model has a better prediction performance for molecular properties than existing pre-training methods using molecular graphs and three-dimensional molecular structures. Computer science ; Chemical space ; Artificial intelligence ; Molecular dynamics ; Molecular descriptor ; Generator (circuit theory) ; Euclidean space ; Molecular graph ; Generative grammar ; Boltzmann machine ; Machine learning ; Theoretical computer science ; Graph ; Quantitative structure–activity relationship ; Drug discovery ; Artificial neural network ; Computational chemistry ; Chemistry ; Mathematics ; Bioinformatics ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Nguyen Hai Chau
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Machine learning guided prediction of the yield strength and hardness of multi-principal element alloys [version 1; peer review: 1 approved with reservations] Finite element method ; Yield (engineering) ; Element (criminal law) ; Artificial neural network Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metal Alloys Wear and Properties