jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Bin How | Sanggyu Chong | Federico Grasselli | Kevin K. Huguenin-Dumittan | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

Adaptive energy reference for machine-learning models of the electronic density of states The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of the training process, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights into the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance. Energy (signal processing) ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Paula Mercurio | Ди Лю
Source: Journal of Materials Informatics (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Clustering molecular energy landscapes by adaptive network embedding In order to efficiently explore the chemical space of all possible small molecules, a common approach is to compress the dimension of the system to facilitate downstream machine learning tasks. Towards this end, we present a data-driven approach for clustering potential energy landscapes of molecular structures by applying recently developed Network Embedding techniques to obtain latent variables defined through the embedding function. To scale up the method, we also incorporate an entropy sensitive adaptive scheme for hierarchical sampling of the energy landscape, based on Metadynamics and Transition Path Theory. Taking into account the kinetic information implied by the energy landscape of a system, we can interpret dynamical node-node relationships in reduced dimensions. We demonstrate the framework through Lennard-Jones clusters and a human DNA sequence. Cluster analysis ; Embedding ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Arthur Hardiagon | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2024-04-29)
Field: Chemistry — Inorganic Chemistry

Multi-Scale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal and Adsorption Behavior Nanoporous frameworks are a large and diverse family of materials, with a key role in various industrial processes and applications such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both its physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multi-scale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption. For the study of the mechanical behavior of nanoporous materials, the last few years have seen a rapid acceleration of research. For example, computational resources have been pooled to created public large-scale databases of elastic constants: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models. The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such large scale. Tentative databases have been assembled at the DFT level on specific families of materials, like zeolites, but prediction at larger scale currently requires the use of transferable, classical force fields, whose accuracy can be limited. Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties, including thermodynamic quantities and transport properties. Finally, we stress the importance for data-based methods of addressing all sources of uncertainty. The Account concludes with some perspectives about the latest developments and open questions in data-based approaches, and the integration of computational and experimental data together in the materials discovery loop. Nanoporous ; Scale (ratio) ; Adsorption ; Materials science ; Nanotechnology ; Computer science ; Process engineering ; Engineering ; Chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Yi Wang
Source: Highlights in Science Engineering and Technology (article, 2024-03-13)
Field: Chemistry — Analytical Chemistry

Neural Network Model for Surface Raman Spectroscopy and Pesticide Concentration of Spinach At present, there are over 1000 chemical pesticides. The widespread use of these pesticides not only causes serious environmental pollution, but also poses a threat to human health. Currently, there are over 1000 types of chemical pesticides. The widespread use of these pesticides has caused serious environmental pollution. Meanwhile, due to some workers not effectively removing pesticides from food during the picking and processing stages of the farm plants, so the long-term consumption of such food will also pose a threat to human health. An analytical technique called Raman spectroscopy is based on the Raman scattering effect, which was discovered by Indian scientist C.V. Raman. It is used in molecular structure research to get data on molecule vibration and rotation by analyzing scattering spectra at various frequencies from incident light. As a newborn way, the Raman spectroscopy analysis method has higher sensitivity. In this thesis, I put it and the BP neural network together and set up the model, to predict pesticide residues in spinach. Spinach ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Pesticide ; Environmental chemistry ; Surface (topology) ; Spectroscopy ; Artificial neural network ; Environmental science ; Analytical Chemistry (journal) ; Chemistry ; Materials science ; Computer science ; Physics ; Ecology ; Biology ; Optics ; Raman scattering ; Artificial intelligence ; Astronomy ; Mathematics ; Biochemistry Spectroscopy and Chemometric Analyses
Authors: Jiahao Wu | Wei Peng Lu | Jingwen Wu | Yanchuan Guo
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Superior Atomic Descriptor: Extra-Nuclear Electron Configuration Aids in Predicting the Partial Electronic Density of States For Medium-Entropy Spinel Oxides Using Neural Network Model Spinel ; Artificial neural network ; Statistical physics ; Entropy (arrow of time) ; Electronic structure ; Electron ; Condensed matter physics ; Atomic physics ; Materials science ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics ; Metallurgy X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials
Authors: Kashmira Malla | Madhav Prasad Ghimire
Source: Scientific World (article, 2024-06-18)
Field: Chemistry — Inorganic Chemistry

Prediction of lattice parameters of tetragonal oxyhalides AOX Machine learning enables computers to emulate human intelligence for complex data analysis and patternrecognition. This work utilizes machine learning to predict lattice parameters in tetragonal oxyhalide compounds with molecular formula AOX. Four supervised learning methods - random forest regression, gradient boosting regression, support vector regression, and kernel ridge regression - are employed to forecast lattice parameters from features including atomic radii, ionic radii, atomic masses, electronegativities, band gaps, formation energies, and densities. Model accuracy is evaluated using mean absolute error and R2 as regression scoring measures. An analysis of gradient boosting regression determines the predictive capacity of distinct features toward lattice parameters. Comparisons identify kernel ridge regression as optimal for predicting lattice constant a, with the highest R2 of 0.840; whereas gradient boosting shows superior in modeling lattice parameter c with a maximum R2 reaching 0.948. This research demonstrates the successful application of machine learning methodologies for predicting material properties, enabling the estimation of lattice parameters in tetragonal oxyhalides. Tetragonal crystal system ; Lattice (music) ; Materials science ; Crystallography ; Condensed matter physics ; Geology ; Crystal structure ; Chemistry ; Physics Inorganic Chemistry and Materials ; Solid-state spectroscopy and crystallography ; Luminescence Properties of Advanced Materials
Authors: Luca Brugnoli | Maxime Ducamp | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2024-11-01)
Field: Materials Science — Materials Chemistry

Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites Molecular dynamics simulations of zeolites are commonly employed for the characterization of their framework dynamics and response to the application of temperature and pressure. While classical interatomic potentials are commonly used for this task, they offer a description of the interactions in the system with limited accuracy. Density Functional Theory, meanwhile, is accurate but its high computational expense limits its scalability for large systems or long dynamics. Recent advances in machine learning interatomic potentials, trained on computational data obtained at the quantum chemical level, offer a promising alternative combining high accuracy with computational efficiency. In this study, we developed an MLIP specifically for pure silica zeolites, trained on data from high- temperature ab initio MD simulations across various zeolitic topologies. This MLIP was then applied to predict structural properties, thermal expansion, and pressure response of different zeolites, demonstrating its potential for accurate and generalizable in simulations of topologies beyond its initial training set. Thermal ; Interatomic potential ; Artificial neural network ; Materials science ; Thermodynamics ; Computer science ; Chemistry ; Physics ; Molecular dynamics ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Yamato Nakanishi | Hiromasa Kaneko
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Data Analysis of the Yield and Electrical Properties of Proton-Conducting Ceramic Fuel Cells Yield (engineering) ; Ceramic ; Proton ; Materials science ; Fuel cells ; Composite material ; Chemical engineering ; Engineering ; Physics ; Nuclear physics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Najd Altwaijry | Ryan Coffee | Matthias F. Kling
Source: APL Machine Learning (article, 2024-06-01)
Field: Chemistry — Spectroscopy

Waveform retrieval for ultrafast applications based on convolutional neural networks Electric field waveforms of light carry rich information about dynamical events on a broad range of timescales. The insight that can be reached from their analysis, however, depends on the accuracy of retrieval from noisy data. In this article, we present a novel approach for waveform retrieval based on supervised deep learning. We demonstrate the performance of our model by comparison with conventional denoising approaches, including wavelet transform and Wiener filtering. The model leverages the enhanced precision obtained from the nonlinearity of deep learning. The results open a path toward an improved understanding of physical and chemical phenomena in field-resolved spectroscopy. Waveform ; Computer science ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Field (mathematics) ; Noise reduction ; Pattern recognition (psychology) ; Wavelet transform ; Ultrashort pulse ; Wavelet ; Physics ; Telecommunications ; Mathematics ; Optics Spectroscopy and Laser Applications ; Laser-Matter Interactions and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Yuting Chen
Source: Deep Blue (University of Michigan) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Finite Size Effects, Machine Learning DFT Functionals and Intermolecular Interaction Energies from Self-Consistent GW Accurate treatment of electron correlation is crucial for computational and theoretical chemists as it influences reaction mechanisms, structural properties, and various spectroscopic quantities. Traditionally, the chemistry community has relied on Density Functional Theory (DFT) and wavefunction-based methods to address the problem of electron correlation. While relatively successful, these methods have limitations either in computational scalability or in the degree of electron correlation described. Green's functions provide an alternative formalism to study electron correlation. This thesis focuses on the self-consistent GW (scGW) approximation, which has been shown to describe a higher degree of electron correlation at a cost comparable to other more traditional wavefunction and Density Functional Theory methods. In Chapter 2 of this thesis, we show a way to account for finite size effects in periodic systems and demonstrate its applications to band structure diagrams. This work also demonstrates that Fock and Self-Energy matrix quantities are able to be extrapolated to the thermodynamic limit. In Chapter 3, this thesis presents a novel application of using Green's functions to train a DFT exchange-correlation functional that recovers the more strongly correlated scGW result at the cheaper DFT cost. It is found that the machine-learning-trained functional outperformed manually created functionals, showcasing the applicability of this approach. This work also showcases the limitations of machine learning by training just on scGW energies instead of enforcing exact conditions, as we find the lack of exact conditions causes the paramaterization to fail in regimes with fewer data points. These two works both focus on reproducing the accuracy of scGW calculations at lower computational costs. Chapter 4 presents an application of the self-consistent GW approximation to studying interaction energies in high-spin open-shell dimers. These systems have traditionally only been evaluated using DFT and wavefunction methods because it was believed scGW could not resolve the small quantity of interaction energies due to the usage of a numerical grid, and this work demonstrates that scGW is capable of studying these complex systems effectively while also highlighting some problems in the benchmark datasets that arise from using a restricted formalism compared to an unrestricted method. Intermolecular force ; Intermolecular interaction ; Physics ; Statistical physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds
Authors: Ilan Mitnikov | Joseph M. Jacobson
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction Electron density prediction stands as a cornerstone challenge in molecular systems, pivotal for various applications such as understanding molecular interactions and conducting precise quantum mechanical calculations. However, the scaling of density functional theory (DFT) calculations is prohibitively expensive. Machine learning methods provide an alternative, offering efficiency and accuracy. We introduce a novel SE(3)-equivariant architecture, drawing inspiration from Slater-Type Orbitals (STO), to learn representations of molecular electronic structures. Our approach offers an alternative functional form for learned orbital-like molecular representation. We showcase the effectiveness of our method by achieving SOTA prediction accuracy of molecular electron density with 30-70\% improvement over other work on Molecular Dynamics data. Equivariant map ; Representation (politics) ; Electron ; Electron density ; Physics ; Mathematics ; Pure mathematics ; Theoretical physics ; Quantum mechanics ; Political science ; Politics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Crystal Li | Amber Li
Source:  (preprint, 2024-02-24)
Field: Chemistry — Analytical Chemistry

Multi-Spectral Camera System to Improve Food and Material Identification Visible cameras have been widely used in many places for food and material identification, such as food sorting and produce labeling.Visible camera sensors, working in the 400nm-700nm range, can identify food with different colors, shapes, or other visible characteristics that are otherwise unidentifiable to visible cameras.Visible image sensors struggle to identify food and material with similar color, as they are limited to the colors ranges human eyes can sense [1] .In this study, a Sony IMX991 sensor was used to capture both visible and infrared images.It was demonstrated that some materials can be distinguished with either visible or infrared narrow band images [2] .Material identification accuracy can be improved by analyzing both visible and infrared images.A machine learning algorithm can be developed by analyzing all images captured under different conditions, accuracy of food and material identification can be significantly improved with continuous image process algorithm development and machine learning training. Identification (biology) ; Computer vision ; Spectral properties ; Computer science ; Artificial intelligence ; Business ; Biological system ; Environmental science ; Chemistry ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Sanan H. Khan
Source: ASEAN Journal of Psychiatry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Commentary on Predicting Projectile Residual Velocities Using an Advanced Artificial Neural Network Model The paper “Predicting projectile residual velocities using an advanced artificial neural network model” presents an innovative approach to a longstanding challenge in the field of ballistics and material science. The authors have developed an Artificial Neural Network (ANN) model to predict the residual velocity of projectiles after impact, a critical parameter in various applications ranging from civilian protection to military technology. This commentary will discuss the key aspects of the paper, its methodological approach, results, and potential impact on the scientific community. Residual ; Projectile ; Artificial neural network ; Ballistics ; Computer science ; Field (mathematics) ; Artificial intelligence ; Engineering ; Mathematics ; Materials science ; Algorithm High-Velocity Impact and Material Behavior ; Advanced Measurement and Detection Methods ; Electromagnetic Launch and Propulsion Technology
Authors: Jaina Wollowitz | Dana Goerzen | Mijin Kim | Daniel A. Heller
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Nanocarbons Division Award, 2nd Place) Assessment of Biomolecule-Carbon Nanotube Interactions in Machine Perception Nanosensor Arrays Arrays of chemically modified and DNA-wrapped carbon nanotube-based nanosensors are powerful new tools for biomarker-independent diagnostics. When exposed to patient biofluids, an individual nanosensor can simultaneously interact with many different biological molecules, enabling the detection of collective molecular changes, constituting a “spectral fingerprint”, rather than measuring individual biomarkers. Biomolecule-nanosensor interactions result in quantitative changes in sensor emission that are employed by machine learning models as features to differentiate between sample categories, such as healthy versus disease. To determine the underlying molecular interactions responsible for changes in sensor emission, we conducted single-analyte titrations of biomolecules at physiologically relevant concentrations. Each sensor exhibited optical responses to multiple biological molecules, illustrating the ability of the nanosensors to concurrently interact with many biomolecules to detect subtle molecular shifts occurring in different disease states. Nanosensor ; Carbon nanotube ; Nanotechnology ; Division (mathematics) ; Biomolecule ; Materials science ; Perception ; Psychology ; Neuroscience Carbon Nanotubes in Composites ; Neuroscience and Neural Engineering ; Advanced Biosensing Techniques and Applications
Authors: Nisha Kumari | Saroj Kumar Sarangi
Source: Surface Review and Letters (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

ACCELERATED DISCOVERIES OF MECHANICAL PROPERTIES OF BORON NITRIDE NANOSHEETS USING MACHINE LEARNING This research investigates the mechanical properties of Boron Nitride Nanosheets (BNNS) by integrating molecular dynamics (MD) simulation with machine learning (ML) techniques. The work highlights the impact of various factors, including dimension, temperature, strain rate and chirality orientation and their influence on mechanical properties like fracture stress, fracture strain, ultimate tensile strength, and Young’s modulus within the context of nanoelectromechanical systems (NEMS). The novel combination of MD simulations and ML models — specifically decision trees, random forest, support vector machines, and polynomial regressions demonstrates that temperature significantly impacts the Young’s modulus, and strain rate estimates true agreement with theoretical expectations. These results highlight machine learning potential to advance material property predictions providing a new methodology for interpreting mechanical properties at the nanoscale and paving the way for innovative applications in NEMS. Boron nitride ; Boron ; Materials science ; Nanotechnology ; Nitride ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research