jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Menglin Sun | Bin Jin | Xiaolong Yang | Shenzhen Xu
Source: arXiv (Cornell University) (preprint, 2024-07-17)
Field: Chemistry — Electrochemistry

A Machine-Learning Accelerated Grand Canonical Sampling Framework for Nuclear Quantum Effects in Constant Potential Electrochemistry Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. However, consideration of the quantum nature of transferring protons under an exact grand canonical (GC) constant potential condition is a great challenge for theoretical electrocatalysis. Here, we develop a unified computational framework to explicitly treat nuclear quantum effects (NQEs) by a sufficient GC sampling, further assisted by a machine learning force field adapted for electrochemical conditions. Our work demonstrates a non-negligible impact of NQEs on PCET simulations for hydrogen evolution reaction at room temperature, and provides a physical picture that wave-like quantum characteristic of the transferring protons facilitates the particles to tunnel through classical barriers in PCET paths, leading to a remarkable activation energy reduction compared to classical simulations. Moreover, the physical insight of NQEs may reshape our fundamental understanding of other types of PCET reactions in broader scenarios of energy conversion processes. Proton ; Quantum tunnelling ; Electron ; Electron transfer ; Proton-coupled electron transfer ; Hydrogen ; Atomic physics ; Chemical physics ; Physics ; Materials science ; Chemistry ; Photochemistry ; Nuclear physics ; Condensed matter physics ; Quantum mechanics Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Valerii Chuiko | Paul W. Ayers
Source: The Journal of Chemical Physics (article, 2024-11-05)
Field: Chemistry — Physical and Theoretical Chemistry

Inferring the existence of hydrogen bonds directly from statistical analysis of molecular dynamics trajectories As a demonstration of how fundamental chemical concepts can be gleaned from data using machine learning methods, we demonstrate the automated detection of hydrogen bonds by statistical analysis of molecular dynamics trajectories. In particular, we infer the existence and nature of electrostatically driven noncovalent interactions by examining the relative probability of supramolecular configurations with and without electrostatic interactions. Then, using Laplacian eigenmaps clustering, we identify hydrogen bonding motifs in hydrogen fluoride, water, and methanol. The hydrogen bonding motifs that we identify support traditional geometric criteria. Hydrogen bond ; Non-covalent interactions ; Molecular dynamics ; Supramolecular chemistry ; Cluster analysis ; Laplace operator ; Chemical physics ; Statistical analysis ; Molecule ; Statistical physics ; Hydrogen ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Crystallography and molecular interactions ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Baoliang Liu | Wenxin Wei | Jiangang Sun
Source:  (article, 2024-11-08)
Field: Materials Science — Ceramics and Composites

Deep Learning-Based Prediction of Crack Extension in C<sub>f</sub>/ZrB<sub>2</sub>-SiC Composites This paper proposes a crack extension prediction model that includes predicting the shape and length of cracks, based on a variety of deep learning technologies and optimization methods, using a large amount of data obtained from traditional numerical simulation experiments in the early stage. The model is implemented by first qualitatively predicting the distribution and damage morphology of cracks from a macroscopic perspective through a generative adversarial network, then quantitatively analyzing the specific dimensions of crack extension from a microscopic level through an artificial neural network, and finally comparing the predicted results with the simulation experiment results to further verify the effectiveness of the above methods. A finite element model is established for the microstructural sample plate of ultra-high temperature ceramic composite materials. A data sample set for crack extension prediction is constructed. A crack morphology extension prediction model is built by using an improved generative adversarial network to predict the distribution of cracks. The model's ability to learn important features is enhanced by adding an attention mechanism module to the traditional CDCGAN, thereby improving the quality of the generated crack morphology images. Materials science ; Composite material ; Extension (predicate logic) ; Computer science ; Programming language Advanced ceramic materials synthesis ; Advanced materials and composites ; Aluminum Alloys Composites Properties
Authors: Dipak Ashok Zope
Source:  (article, 2024-01-11)
Field: Materials Science — Materials Chemistry

Condensed Matter Physics And The Search For Room-Temperature Superconductors Superconductivity remains one of the most profound topics in condensed matter physics, offering pathways to revolutionize modern technology through zero-resistance electrical transport and perfect diamagnetism. The persistent challenge lies in achieving superconductivity at ambient conditions a goal that continues to drive both experimental and theoretical innovation. This study employs a data-driven approach to investigate the physicochemical parameters that govern superconducting transition temperatures. Using a large, curated dataset of more than twenty thousand known superconductors, a comprehensive analysis was conducted through statistical exploration and machine-learning modeling. Ensemble learning techniques, including Random Forest and Gradient Boosting regressors, were implemented to capture nonlinear dependencies between atomic descriptors and critical temperature. The models achieved strong predictive accuracy and interpretability, identifying mean atomic mass, valence electron count, and electronegativity as dominant features influencing superconducting behavior. The results align with fundamental physical theory, reinforcing that lighter elements and optimal electron configurations favor higher transition temperatures. The study highlights the effectiveness of integrating condensed-matter physics with machine learning for materials discovery. While limited by the absence of structural and pressure-dependent parameters, the framework provides a scalable route for screening and predicting new superconductors. These insights contribute to ongoing efforts toward realizing room-temperature superconductivity, marking a significant step toward data-informed materials innovation. Superconductivity ; Electronegativity ; Leverage (statistics) ; Gradient boosting ; Valence (chemistry) ; Boosting (machine learning) ; Scalability Machine Learning in Materials Science ; Quantum many-body systems ; Artificial Intelligence in Healthcare and Education
Authors: Oleh Hordiichuk | Y. Okis | A. Kuznietsov | A. Yanchak | Jean de Montmollin | C.A. Fuller | Michael Wörle | Dmitry Chernyshov | M. Kovalenko
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-08-26)
Field: Chemistry — Inorganic Chemistry

Hybrid materials of metal halide nano-sized clusters orderly confined in single-crystal metal–organic reticular materials Constraining spatial dimensionality of electronically extended systems by tailoring crystal structure, size and shape of inorganic compounds at nanoscale often endows the resulting materials with extraordinary optoelectronic, magnetic and catalytic properties [1,2].A recent prominence of inorganic and hybrid -organic-inorganic -lead halide three-and twodimensional perovskites as exceptional materials for optoelectronic applications has stimulated studies of other metal halide compounds leading to discoveries of materials with diverse and novel functionalities [2][3][4][5].We envisioned an extension of the structural space accessible for metal halides, in terms of atomic connectivity and electronic dimensionality, by utilising crystalline porous templates as hosts for metal halides crystallization.Atomically defined periodically ordered metal halide nano-sized clusters and their properties can thus be engineered well beyond the structural motives known from the conventional free-space crystallization.We have achieved successful results with metal-organic frameworks and polyhedra as the templates and various main-group and transition metal halides, having obtained unique nano-sized clusters within the pores.We demonstrate the possibility of obtaining different structures by changing parameters of the reticular templates and crystallization conditions.The structures were determined with atomic resolution using single-crystal X-ray diffraction methods.In the cases of partial occupancy of the pores by clusters, the pronounced structured X-ray diffuse scattering revealed presence of short-range correlations between empty and occupied cavities.We managed to characterize the local structures using a recently developed deep learning method by treating the systems as a substitutional disorder and factorizing the diffuse scattering intensities into formfactor and short-range correlation components [6].Characterization and synthetic control of arrangement of such optically active clusters on a local scale opens opportunities to study and engineer collective phenomena.We anticipate that such confined molecularly templated crystallization will evolve into an attractive materials design strategy. Halide ; Metal ; Reticular connective tissue ; Nano- ; Materials science ; Nanotechnology ; Single crystal ; Crystal (programming language) ; Chemistry ; Composite material ; Inorganic chemistry ; Crystallography ; Metallurgy Inorganic Chemistry and Materials ; Nanocluster Synthesis and Applications ; Crystal Structures and Properties
Authors: Ashwini Verma | Kavita Joshi
Source: ChemRxiv (preprint, 2024-04-09)
Field: Materials Science — Materials Chemistry

PCTpro: A Machine learning model for rapid prediction of Pressure-Composition-Temperature (PCT) isotherms We present a machine-learning powered Pressure-Composition-Temperature isotherm Predictor (PCTpro) for metal compositions. To train the PCTpro, an experimental database of PCT isotherms (MH-PCT) is built from published literature. The database comprises over 14,000 data points extracted from 237 PCT isotherms, representing 138 distinct compositions. The dataset encom- passes more than 25 elements and spans a broad spectrum of absorption tem- peratures (263-653 K) and hydrogen pressures (0.001 to 40 MPa). The feature set includes weighted average of elemental properties, hydriding properties, and experimental parameters like absorption temperature and hydrogen pressure. The comprehensive feature set equips PCTpro to predict the PCT isotherms for a given composition. The model is validated on a wide range of alloy fam- ilies and its predictions are consistent with experimental results. The model also captures temperature-dependent variations in plateau pressure, enabling determination of enthalpy and entropy of hydride formation through Van’t Hoff plots. We also demonstrate that training PCTpro on a subset of relevant data provides improved PCT isotherm prediction. Hence, PCTpro can be used as an ML tool for guiding PCT experiments, offering PCT isotherm predictions and valuable thermodynamic insights into materials suitable for solid-state hydrogen storage. Composition (language) ; Thermodynamics ; Artificial intelligence ; Materials science ; Computer science ; Physics ; Philosophy Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Thermal and Kinetic Analysis
Authors: Nora Izzati binti Mohd Razip | Mega Novita | Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Creation of a Two-Step Prediction Model of 4f-5d Transition Energies of Ce<sup>3+</sup> in Garnet-Type Oxides Based on the Combination of First-Principles Calculations and Machine Learning This study presents a novel approach which combines first-principles calculations with machine learning techniques to predict the 4f-5d transition energy of Ce 3+ ions in garnet-type oxides. This is important in various technological applications, e.g., light-emitting materials, and solid-state lighting devices. However, it is difficult and time-consuming to accurately determine this energy using conventional methods. A linear regression model for the 4f-5d transition energy of Ce 3+ in garnet-type oxides was created by machine learning, using electronic and structural parameters as attributes. The electronic parameters were obtained through first-principles calculations. This model is highly effective in estimating transition energy values for various Ce 3+ -doped garnet-type oxides. The machine learning model improved the accuracy of transition energy calculations. Furthermore, we have developed predictive models that estimate first-principles calculation results based on structural data. Systematic first-principles calculations of fictitious garnet-type oxides with gradually changed structural parameters were performed and used as the training data. The predictive models of the electronic parameters such as the bond order, the crystal field splitting (ε cfs ) and the net charge were used to create a two-step predictive model based solely on structural parameters. Physics ; Computer science Luminescence Properties of Advanced Materials
Authors: N. N. Kiselyova | Victor Dudarev | O. V. Sen’ko | A. A. Dokukin | A. V. Stolyarenko | Yu. O. Kuznetsova
Source: Pattern Recognition and Image Analysis (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Prediction of Physical Properties of Double Halides Using Machine Learning Methods The effectiveness of various machine learning methods in predicting the quantitative properties of inorganic compounds was compared. To assess the accuracy, cross-validation was used in the mode leave-one-out cross-validation. It has been shown that the use of ridge, Bayesian ridge, automatic relevance determination regression, elastic net, and extra trees regressor programs and gradient boosting regressor and hist gradient boosting regressor, based on the methodology of ensembles of machine learning algorithms from the scikit-learn package, as well as simple syndrome regressor and recursive regressor, specially designed to solve problems in the field of inorganic materials science, allows us to obtain the most accurate estimates. Using selected machine learning programs, the melting point at atmospheric pressure of double halides of compositions ABHal3, ABHal4, A2BHal4, A2BHal5, and A3BHal6 (A and B are different elements; Hal is F, Cl, Br, or I) was predicted and the parameters of their crystal lattice under normal conditions were estimated. When estimating melting points, the average absolute errors determined by the cross-validation method were in the range of 29–52 K, depending on the composition of halides and the selected algorithm. Multiple determination coefficient R2 for models used for forecasting was not lower than 0.7. When estimating lattice parameters, the average absolute errors for linear parameters were in the range of 0.0068–0.2120 Å and were 0.1209°–0.1562° for angles. R2 was above 0.6. Computer science ; Artificial intelligence ; Machine learning ; Halide ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Gyu-Jang Sim | Myoung‐Gyu Lee | Marat I. Latypov
Source: arXiv (Cornell University) (preprint, 2024-06-12)
Field: Materials Science — Materials Chemistry

FIP-GNN: Graph neural networks for scalable prediction of grain-level fatigue indicator parameters High-cycle fatigue is a critical performance metric of structural alloys for many applications. The high cost, time, and labor involved in experimental fatigue testing call for efficient and accurate computer models of fatigue life. We present FIP-GNN -- a graph neural network for polycrystals that (i) predicts fatigue indicator parameters as grain-level inelastic responses to cyclic loading quantifying the local driving force for crack initiation and (ii) generalizes these predictions to large microstructure volume elements with grain populations well beyond those used in training. These advances can make significant contributions to statistically rigorous and computationally efficient modeling of high-cycle fatigue -- a long-standing challenge in the field. Scalability ; Artificial neural network ; Computer science ; Graph ; Data mining ; Artificial intelligence ; Theoretical computer science ; Database Metal Alloys Wear and Properties ; Fatigue and fracture mechanics ; Metallurgy and Material Forming
Authors: William Harbutt Dawson | Katsuhisa Ozaki | Jens Domke | Takahito Nakajima
Source: arXiv (Cornell University) (preprint, 2024-07-18)
Field: Chemistry — Physical and Theoretical Chemistry

Reducing Numerical Precision Requirements in Quantum Chemistry Calculations The abundant demand for deep learning compute resources has created a renaissance in low precision hardware. Going forward, it will be essential for simulation software to run on this new generation of machines without sacrificing scientific fidelity. In this paper, we examine the precision requirements of a representative kernel from quantum chemistry calculations: calculation of the single particle density matrix from a given mean field Hamiltonian (i.e. Hartree-Fock or Density Functional Theory) represented in an LCAO basis. We find that double precision affords an unnecessarily high level of precision, leading to optimization opportunities. We show how an approximation built from an error-free matrix multiplication transformation can be used to potentially accelerate this kernel on future hardware. Our results provide a road map for adapting quantum chemistry software for the next generation of High Performance Computing platforms. Quantum chemistry ; Quantum ; Computer science ; Chemistry ; Statistical physics ; Physics ; Quantum mechanics ; Reaction mechanism ; Organic chemistry Various Chemistry Research Topics ; Molecular spectroscopy and chirality ; Atmospheric Ozone and Climate
Authors: Yarub Al-Douri
Source: Piri Reis University Institutional Repository (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Quantum dots: Synthesis, characterization, and optical investigations The focus of this book is synthesis and optical studies of self-assembled quantum dots (QDs) with particular consideration given to the capability to control nanoelectronics properties via structural modifications. Furthermore, it is devoted to new nanotechnology tools and technological procedures useful for the developed community of science, society, improved economy and best future. The newest addition is exploring the application of artificial intelligence (AI) techniques in synthesis and optimization of quantum dots. AI-driven design utilizes machine learning algorithms to predict the properties of synthesis condition-, composition- and size-based quantum dots. AI can achieve optimal conditions for desired optical and electronic characteristics. But the processing optimizes the synthesis of QDs. AI is recommended to improve functionality and uniformity of QDs. AI predict optical properties that bases on particle shape and size, enabling rapid screening of potential quantum dot configurations. Key Features: Provides a comprehensive overview of a wide range of materials and related properties. Includes the fundamentals, characterization, and applications. Considers the capability to control nanoelectronics properties. Presents advances and advantages. Fundamental, characterisation and application will be shared to impetus all to understand it. Nanoelectronics ; Quantum dot ; Quantum ; Key (lock) ; Quantum computer ; Control (management) Quantum Dots Synthesis And Properties ; Chemical and Physical Properties of Materials ; Carbon and Quantum Dots Applications
Authors: Fatemeh Akbari
Source: Medical Physics (article, 2024-10-12)
Field: Materials Science — Materials Chemistry

Prediction of electron‐solid interaction parameters using machine learning The developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges. Random forest ; Mean squared error ; Mean absolute percentage error ; Computer science ; Machine learning ; Ensemble learning ; Support vector machine ; Artificial intelligence ; Data mining ; Statistics ; Mathematics ; Artificial neural network Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Sijia S. Dong | Bhumika Jayee | Gustavo Mondragon | Felipe Curtolo | Nithin Chintala | Zheyu Zhang
Source: Biophysical Journal (article, 2024-02-01)
Field: Chemistry — Organic Chemistry

Understanding the ensemble effect in photoenzyme photophysics and reactivity via first principles simulations and machine learning Photoexcitation ; Chemistry ; Intramolecular force ; Flavin group ; Reactivity (psychology) ; Mutagenesis ; Cofactor ; Computational chemistry ; Density functional theory ; Stereochemistry ; Enzyme ; Physics ; Biochemistry ; Excited state ; Mutation Radical Photochemical Reactions ; Photochemistry and Electron Transfer Studies ; Photoreceptor and optogenetics research