jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dinor Nagar | Nikita Vladimirov | Christian T. Farrar | Or Perlman
Source: arXiv (Cornell University) (preprint, 2023-05-30)
Field: Materials Science — Materials Chemistry

Dynamic and Rapid Deep Synthesis of Molecular MRI Signals Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch-McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 78%-99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson's r$&gt;$0.99, p$&lt;$0.0001, normalized root mean square error $&lt;$3%). Magnetization transfer ; Magnetic resonance imaging ; Computer science ; Ground truth ; Biological system ; In silico ; Artificial intelligence ; Data acquisition ; Nuclear magnetic resonance ; Pattern recognition (psychology) ; Physics ; Chemistry ; Biology Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies ; Advanced MRI Techniques and Applications
Authors: Zijuan Luo | Jiale Peng | Yue Mu | Li Sun | Zhihua Zhu | Zhen Liu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Design of Cr-Pnp Catalysts for Ethylene Tri-/Tetramerization Assisted by a Data-Driven Approach Ethylene ; Catalysis ; Chemistry ; Chemical engineering ; Computer science ; Combinatorial chemistry ; Materials science ; Engineering ; Biochemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Zeolite Catalysis and Synthesis
Authors: Hyun Park | Boyuan Yu | Juhae Park | Ge Sun | Emad Tajkhorshid | Juan Pablo | Ludwig Schneider
Source: arXiv (Cornell University) (preprint, 2023-08-31)
Field: Materials Science — Materials Chemistry

Prediction of Diblock Copolymer Morphology via Machine Learning A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes. Mesoscopic physics ; Computer science ; Particle (ecology) ; Computation ; Scaling ; Time evolution ; Materials science ; Statistical physics ; Biological system ; Artificial intelligence ; Algorithm ; Physics ; Geometry ; Mathematics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Yerin Jang | Choong H. Kim | Ara Go
Source: arXiv (Cornell University) (preprint, 2023-02-26)
Field: Materials Science — Materials Chemistry

Classification of magnetic order from electronic structure by using machine learning Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO$_3$. Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model's performance. We improved the model's performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning. Artificial intelligence ; Antiferromagnetism ; Excitation ; Machine learning ; Hamiltonian (control theory) ; Magnetism ; Physics ; Computer science ; Statistical physics ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Peilun Han | Xiaoqian Shen | Boxiong Shen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Simulation Study on Nox Reduction Efficiency in Scr Catalysts Utilizing a Modern C3-Cnn Algorithm NOx ; Reduction (mathematics) ; Selective catalytic reduction ; Computer science ; Algorithm ; Catalysis ; Environmental science ; Automotive engineering ; Chemistry ; Engineering ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Iron and Steelmaking Processes
Authors: Roman Remme | Tobias Kaczun | Maximilian Scheurer | Andreas Dreuw | Fred A. Hamprecht
Source: arXiv (Cornell University) (preprint, 2023-05-08)
Field: Materials Science — Materials Chemistry

KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory Orbital-free density functional theory (OF-DFT) holds the promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy as a functional of the electron density only. We here set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn-Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU; and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of the nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths; and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy. Density functional theory ; Computer science ; Orbital-free density functional theory ; Statistical physics ; Context (archaeology) ; Energy functional ; Topology (electrical circuits) ; Artificial intelligence ; Algorithm ; Time-dependent density functional theory ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Pascal Pernot
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science. It is now well understood that average calibration is insufficient, and most studies implement additional methods testing the conditional calibration with respect to uncertainty, i.e. consistency. Consistency is assessed mostly by so-called reliability diagrams. There exists however another way beyond average calibration, which is conditional calibration with respect to input features, i.e. adaptivity. In practice, adaptivity is the main concern of the final users of a ML-UQ method, seeking for the reliability of predictions and uncertainties for any point in features space. This article aims to show that consistency and adaptivity are complementary validation targets, and that a good consistency does not imply a good adaptivity. Adapted validation methods are proposed and illustrated on a representative example. Consistency (knowledge bases) ; Calibration ; Reliability (semiconductor) ; Computer science ; Point (geometry) ; Uncertainty quantification ; Machine learning ; Focus (optics) ; Weak consistency ; Data mining ; Artificial intelligence ; Mathematics ; Strong consistency ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Yu Zheng | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Fast and accurate modeling of thermoset fracture by active learning quantum-chemical bond scission A molecular understanding of thermoset fracture is crucial for enhancing performance and durability across applications. However, achieving accurate atomistic modeling of thermoset fracture remains computationally prohibitive due to the high cost associated with quantum mechanical methods for describing bond breaking. In this work, we introduce an active learning (AL) framework for our recently developed machine-learning based adaptable bond topology (MLABT) model that uses datasets generated via density functional theory (DFT) calculations that are both minimalistic and informative. Employing MLABT integrated with AL and DFT, we explore fracture behavior in highly crosslinked thermosets, assessing the variations in fracture behavior induced by system temperature, temperature fluctuations, strain rate, cooling rate, and degree of crosslinking. Notably, we discover that while fracture is minimally affected by temperature, it is strongly influenced by strain rate, suggesting the absence of the time-temperature superposition in thermoset plasticity. Furthermore, while the structural disparities introduced by different network annealing rates influence the elastic properties, they are inconsequential for thermoset fracture. In contrast, network topology emerges as the dominant determinant of fracture, influencing both the ultimate strain and stress. The integration of MLABT with the AL framework paves the way for efficient and DFT-accurate modeling of thermoset fracture, providing an affordable and accurate approach for calculating polymer network fracture across chemical space. Thermosetting polymer ; Materials science ; Fracture (geology) ; Superposition principle ; Composite material ; Computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Huiwen Zhu | Zeyu Guo | Dawei Lan | Shuai Liu | Jiahui Yu | Min Liu | Jianwen Zhang | Xiang Luo | Tao Wu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning-Accelerated Paradigm for the Rapid Screening of Efficient Dual-Atom Electrocatalysts In Co2 Reduction Dual (grammatical number) ; Reduction (mathematics) ; Atom (system on chip) ; Computer science ; Artificial intelligence ; Nanotechnology ; Chemistry ; Materials science ; Embedded system ; Mathematics ; Philosophy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Neural Networks and Applications
Authors: Wenye Ji | Jin Woo Chang | He‐Xiu Xu | Jian Gao | Simon Gröblacher | Paul Urbach | A. J. L. Adam
Source: arXiv (Cornell University) (preprint, 2023-07-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Advances in Metasurface Design and Quantum Optics Applications with Machine Learning, Physics-Informed Neural Networks, and Topology Optimization Methods As a two-dimensional planar material with low depth profile, a metasurface can generate non-classical phase distributions for the transmitted and reflected electromagnetic waves at its interface. Thus, it offers more flexibility to control the wave front. A traditional metasurface design process mainly adopts the forward prediction algorithm, such as Finite Difference Time Domain, combined with manual parameter optimization. However, such methods are time-consuming, and it is difficult to keep the practical meta-atom spectrum being consistent with the ideal one. In addition, since the periodic boundary condition is used in the meta-atom design process, while the aperiodic condition is used in the array simulation, the coupling between neighboring meta-atoms leads to inevitable inaccuracy. In this review, representative intelligent methods for metasurface design are introduced and discussed, including machine learning, physics-information neural network, and topology optimization method. We elaborate on the principle of each approach, analyze their advantages and limitations, and discuss their potential applications. We also summarise recent advances in enabled metasurfaces for quantum optics applications. In short, this paper highlights a promising direction for intelligent metasurface designs and applications for future quantum optics research and serves as an up-to-date reference for researchers in the metasurface and metamaterial fields. Metamaterial ; Aperiodic graph ; Topology optimization ; Topology (electrical circuits) ; Computer science ; Physics ; Artificial neural network ; Quantum ; Coupling (piping) ; Quantum optics ; Artificial intelligence ; Quantum mechanics ; Finite element method ; Mathematics ; Mechanical engineering ; Electrical engineering ; Engineering Metamaterials and Metasurfaces Applications ; Millimeter-Wave Propagation and Modeling ; Antenna Design and Analysis
Authors: Sukriti Manna | Henry Chan | Avishek Ghosh | Tamoghna Chakraborti | Subramanian K. R. S. Sankaranarayanan
Source: ChemRxiv (preprint, 2023-07-17)
Field: Materials Science — Materials Chemistry

Understanding and Control of Zener Pinning via Phase Field and Ensemble Learning Zener pinning refers to the dispersion of fine particles which influences grain size distribution \textit{via} movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution \textit{via} Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control \textit{via} Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables. Zener diode ; Grain size ; Materials science ; Zener pinning ; Phase (matter) ; Microstructure ; Field (mathematics) ; Machine learning ; Statistical physics ; Artificial intelligence ; Condensed matter physics ; Algorithm ; Computer science ; Physics ; Mathematics ; Flux pinning ; Superconductivity ; Composite material ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Source:  (article, 2023-03-13)
Field: Materials Science — Materials Chemistry

Keynotes Artificial Intelligence (AI) is a rapidly advancing field.Novel machine learning methods combined with reasoning and search techniques have led us to reach new milestones: from computer vision, machine translation, and Go world-champion level play, to self-driving cars.These everexpanding AI capabilities open exciting avenues for advances in new domains.I will discuss our AI research for advancing scientific discovery for a sustainable future.In particular, I will talk about our research in a new interdisciplinary field, Computational Sustainability, which aims to develop computational models and methods to help balance the environmental, economic, and societal needs for a sustainable future.I will provide examples of computational sustainability problems, ranging from biodiversity conservation to multi-criteria strategic planning of hydropower dams in the Amazon basin and materials discovery for renewable energy materials.I will also highlight our work on AI to accelerate the discovery of new solar fuels materials.In this work, we propose an approach called Deep Reasoning Networks (DRNets), which requires only modest amounts of (unlabeled) data, in sharp contrast to standard deep learning approaches.DRNets reach super-human performance for crystalstructure phase mapping, a core, long-standing challenge in materials science, enabling the discovery of solar-fuels materials.DRNets provide a general framework for integrating deep learning and reasoning for tackling challenging problems.Finally, I will highlight cross-cutting computational themes and challenges for AI. Computer science Machine Learning in Materials Science ; Economic and Technological Innovation ; Big Data and Digital Economy
Authors: T. Li
Source: arXiv (Cornell University) (preprint, 2023-09-21)
Field: Materials Science — Materials Chemistry

Micromechanics-Informed Parametric Deep Material Network for Physics Behavior Prediction of Heterogeneous Materials with a Varying Morphology Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behaviors can be successfully approximated by such physics-based neural-network like architecture. In this work, a novel micromechanics-informed parametric DMN (MIpDMN) architecture is proposed for multiscale materials with a varying microstructure characterized by several parameters. A single-layer feedforward neural network is used to account for the dependence of DMN fitting parameters on the microstructural ones. Micromechanical constraints are prescribed both on the architecture and the outputs of this new neural network. The proposed MIpDMN is also recast in a multiple physics setting, where physical properties other than the mechanical ones can also be predicted. In the numerical simulations conducted on three parameterized microstructures, MIpDMN demonstrates satisfying generalization capabilities when morphology varies. The effective behaviors of such parametric multiscale materials can thus be predicted and encoded by MIpDMN with high accuracy and efficiency. Micromechanics ; Parametric statistics ; Parameterized complexity ; Generalization ; Artificial neural network ; Network architecture ; Biological system ; Materials science ; Computer science ; Nonlinear system ; Microstructure ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Composite material ; Mathematical analysis Machine Learning in Materials Science ; Neural Networks and Applications ; Model Reduction and Neural Networks
Authors: Hannah Toru Shay | Brett A. McGuire
Source:  (article, 2023-06-20)
Field: Chemistry — Spectroscopy

BEYOND THE MACHINE: AUTOMATING SPECTRA GENERATION AND ANALYSIS FROM MACHINE LEARNING RESULTS Almost 300 molecules have been detected in the interstellar medium, with an exponential explosion in recent years thanks to rapid innovation in technology and technique.However, detections alone only offer tantalizing hints to future chemistry; we generally have been unable to make reliable predictions of the chemical species that will be subsequently detected.Prior work in the McGuire group was able to use Machine Learning approaches to accurately reproduce the inventories of 87 known species in TMC-1.The model went on to make over 1500 predictions of potential targets.Not only is this more than can efficiently be pursued, but these predictions provided only column densities and did not account for other key factors of detectability such as how many spectral lines fall within the range of our astronomical observations and the intensities of those lines.My work takes the results of this machine learning model, and others like it, and further narrows the list of candidates for detection by applying detectability metrics.Through automation, this workflow leads to extended usability of ML inventory predictions. Workflow ; Usability ; Computer science ; Machine learning ; Automation ; Key (lock) ; Artificial intelligence ; Range (aeronautics) ; Work (physics) ; Aerospace engineering ; Human–computer interaction ; Database ; Engineering ; Mechanical engineering ; Operating system Analytical Chemistry and Chromatography