jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ali Rajabpour | Bohayra Mortazavi | Pedram Mirchi | Julien El Hajj | Yangyu Guo | Xiaoying Zhuang | Samy Mérabia
Source: arXiv (Cornell University) (preprint, 2024-07-22)
Field: Materials Science — Materials Chemistry

Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using machine learning (ML) interatomic potentials trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results show a closer agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC by a factor of about 3. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of the ML interatomic potential to improve the accuracy and computational efficiency of thermal transport simulations in complex material systems. Silicon ; Diamond ; Thermal ; Conductance ; Interatomic potential ; Materials science ; Estimation ; Thermal conductivity ; Condensed matter physics ; Computer science ; Engineering physics ; Optoelectronics ; Composite material ; Chemistry ; Physics ; Thermodynamics ; Engineering ; Computational chemistry ; Molecular dynamics Thermal properties of materials ; Diamond and Carbon-based Materials Research ; Surface and Thin Film Phenomena
Authors: Philipp del Hougne
Source:  (article, 2024-06-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Frugal open-loop coherent wave control in metasurface-programmable complex media The control of wave scattering in unknown complex media (e.g., chaotic cavities) that can be reconfigured by a programmable metasurface is a seemingly daunting task: the mapping from metasurface configuration to scattering matrix is non-linear (due to non-locality), and there is no a priori knowledge of geometry or material composition of the unknown complex medium. Yet, this mapping must be known to enable open-loop wave control. Training a deep neural network to approximate the mapping is a potential solution. However, we show here that formulating a physical model is surprisingly simple and offers 10X advantages over the use of deep learning in terms of the achieved accuracy, the model compactness (number of model parameters) and the number of required calibration measurements. Surprisingly, the physical model can be trained based on phase-insensitive measurements which drastically alleviates the measurement complexity. This unlocks previously unimagined regimes of phaseless coherent wave control combining wavefront shaping and metasurface tuning, e.g., to tune a complex system to coherent perfect absorption (CPA) at a desired frequency and to identify the CPA wavefront without ever measuring phase. This remarkable phase-retrieval ability can be traced back to the mathematical structure of the physical model that imposes strong constraints on the possible model parameter values. The constraints are so strong that one can even estimate an entire scattering matrix (up to some sign ambiguities) by only inputting/outputting waves through a subset of the ports while tuning the loads terminating the remaining ports. Loop (graph theory) ; Computer science ; Physics ; Control (management) ; Artificial intelligence ; Mathematics Metamaterials and Metasurfaces Applications ; Underwater Vehicles and Communication Systems ; Optical Wireless Communication Technologies
Authors: Hao Zhou | Janak Tiwari | Tianli Feng
Source: arXiv (Cornell University) (preprint, 2024-01-10)
Field: Materials Science — Materials Chemistry

Understanding the flat thermal conductivity of La2Zr2O7 at ultrahigh temperatures Many crystals, such as lanthanum zirconate (La2Zr2O7), exhibit a flat temperature dependence of thermal conductivity at elevated temperatures. This phenomenon has recently been attributed to the inter-band phonon tunneling (or diffuson) contribution using different formalisms. However, the contributions of finite-temperature corrections (e.g., higher-order phonon-scattering, phonon renormalization, and phonon scattering cross-section softening effects) remain unclear. In this work, we predict and compare the thermal conductivity of La2Zr2O7 using three distinct first-principles methods. The first method is Green-Kubo molecular dynamics (MD) based on temperature-dependent machine learning interatomic potentials (MLIPs) trained from ab initio MD simulations, which successfully predict the flat trend at ultra-high temperatures. The second method is the Peierls Boltzmann transport equation (BTE), within the phonon particle framework, using phonon lifetime that includes all the finite-temperature corrections. Four-phonon scattering is found large but is cancelled by the phonon scattering cross-section softening effect. As a result, BTE with temperature corrections does not reproduce the flat thermal conductivity. The third method is Wigner formalism, which includes both phonon particle and wave contributions, which successfully reproduce the flat thermal conductivity. Diffuson and phonon contribute about 67% and 27% of thermal conductivity at 1800 K, respectively. The radiation contribution to thermal conductivity is around 6%. The scaling laws of the phonon, diffuson, radiation, and total thermal conductivity are found to be ~T-0.97, ~T0.43, ~T2.01, and ~T-0.40, respectively. This work clarifies the thermal transport mechanisms in La2Zr2O7 at ultra-high temperatures from different aspects. Thermal conductivity ; Phonon ; Condensed matter physics ; Scattering ; Phonon scattering ; Boltzmann equation ; Materials science ; Thermal conduction ; Physics ; Thermodynamics ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; High-pressure geophysics and materials
Authors: Ewa M. Nowara | Pedro O. Pinheiro | Sai Pooja Mahajan | Omar Abdulkareem Mahmood | Andrew M. Watkins | Saeed Saremi | Michael Maser
Source: arXiv (Cornell University) (preprint, 2024-07-03)
Field: Materials Science — Materials Chemistry

NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scientific discovery, but it remains challenging to generate large numbers of high quality samples efficiently. 3D-voxel-based methods have recently shown great promise for generating high quality samples de novo from random noise (Pinheiro et al., 2023). However, sampling in 3D-voxel space is computationally expensive and use in library generation is prohibitively slow. Here, we instead perform neural empirical Bayes sampling (Saremi &amp; Hyvarinen, 2019) in the learned latent space of a vector-quantized variational autoencoder. NEBULA generates large molecular libraries nearly an order of magnitude faster than existing methods without sacrificing sample quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as demonstrated on two public datasets and multiple recently released drugs. We expect the approach herein to be highly enabling for machine learning-based drug discovery. The code is available at https://github.com/prescient-design/nebula Bayes' theorem ; Computer science ; Artificial intelligence ; Bayesian probability Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: John Findley | Janice A. Steckel
Source:  (article, 2024-08-06)
Field: Chemistry — Inorganic Chemistry

Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to screen MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties. Density functional theory ; Metal-organic framework ; Flexibility (engineering) ; Adsorption ; Force field (fiction) ; Sorbent ; Work (physics) ; Quantum chemical ; Materials science ; Dispersion (optics) ; Field (mathematics) ; Computer science ; Molecule ; Mechanical engineering ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Engineering ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Carbon Dioxide Capture Technologies
Authors: Thomas Radford | Peter R. Wiecha | Alberto Politi | Otto L. Muskens
Source: Quantum 2.0 Conference and Exhibition (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Neural Adjoint, Inverse Design for Arbitrary Unitary Matrix Recreation in Coupled Waveguide Arrays Using Programmable Phase Change Materials We present a deep learning adjoint network, demonstrating its use for the inverse design of refractive index perturbation patterns required to map an arbitrary unitary matrix onto a waveguide array using programmable phase change materials. Unitary state ; Inverse ; Refractive index ; Waveguide ; Unitary matrix ; Artificial neural network ; Matrix (chemical analysis) ; Perturbation (astronomy) ; Inverse problem ; Computer science ; Mathematics ; Optics ; Mathematical analysis ; Physics ; Materials science ; Artificial intelligence ; Quantum mechanics ; Geometry Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Shanghui Sun
Source: Molecular & cellular biomechanics (article, 2024-07-23)
Field: Chemistry — Physical and Theoretical Chemistry

Navigating the biomechanical landscape: Enhanced methods for drug analysis In drug analysis and detection, chemiluminescence is a standard technical method based on the idea that the concentration of observed substances in the chemical detection system and the chemical luminescence intensity establish a linear quantitative relationship under certain conditions. Bio-mechanical landscape (BML), which uses bioengineered systems to simulate essential elements of the human tumour microenvironment, is already enhancing cancer biology and aiding clinical translation. The majority of illicit substances have dangerous cardiovascular side effects that might range from an irregular heartbeat to a heart attack. Cardiovascular disease, including blocked veins and bacterial infections of the blood arteries and heart valves, may also result from injecting illicit narcotics. The challenging characteristics of such a cardiovascular disease using drug analysis are heart failure, low blood pressure and headaches. Hence, in this research, drug analysis has improved wearable body sensor network-enabled artificial neural network (WBSN-ANN) technologies for cardiovascular disease. Medicine and drug development because they provide information on patient status in the clinical context and vital information on pharmacodynamics activity, effectiveness, and safety throughout the research process. The process results in a research-intensive organization finding a novel chemical or biological compound and developing it into a medicine that patients can legally utilize for drug discovery and development. Drug ; Computer science ; Medicine ; Computational biology ; Biology ; Pharmacology thermodynamics and calorimetric analyses ; Cancer and biochemical research ; Cellular Mechanics and Interactions
Authors: M. Warrier | Utkarsh Bhardwaj
Source: arXiv (Cornell University) (preprint, 2024-02-01)
Field: Materials Science — Materials Chemistry

Statistical Study of the Defect Cluster Morphology in the Primary Damage of Tungsten from Collision Cascades from Five Inter-atomic potentials The size and morphology of defect clusters formed during primary damage play a crucial role in the subsequent microstructural evolution of irradiated materials. Molecular dynamics (MD) simulations of collision cascades in tungsten (W) were performed using five interatomic potentials (IAPs): the quantum-accurate machine-learned Spectral Neighbor Analysis Potential (W-SNAP), the machine learning-based tabGAP potential, and three embedded-atom method (EAM) potentials. A total of 3,500 MD simulations were conducted with primary knock-on atoms (PKAs) at energies of 5, 10, 20, 50, 75, 100, and 150 keV. PKAs were launched in 100 random directions at each energy to ensure statistical validity. Analysis was performed using CSaransh , a web-based tool for large-scale collision cascade databases, to quantify: (i) the number of defects (isolated and clustered), (ii) defect cluster morphologies, (iii) defect cluster size distributions and (iv) the number of sub-cascades formed. We show that the difference in the formation energy of self interstitial atom dumbells along the &lt;1 1 0&gt; and &lt;1 1 1&gt; directions critically influence defect cluster morphology. Our results indicate that IAP stiffness and interaction range independently do not affect defect count. However, these parameters combined with defect formation energies, threshold displacement energies, and other factors significantly influence defect production. Cluster (spacecraft) ; Collision ; Morphology (biology) ; Physics ; Atomic physics ; Computer science ; Geology ; Computer security Microstructure and mechanical properties ; Advanced materials and composites ; Semiconductor materials and interfaces
Authors: Joselma Pedrosa da Silva | Pedro dos Santos Panero | Ana Paula Folmer Corrêa | Marcos José Salgado Vital | Fabiana Granja | Francisco dos Santos Panero
Source: Caderno Pedagógico (article, 2024-10-29)
Field: Chemistry — Analytical Chemistry

Non-destructive and rapid identification of yeasts by nearinfrared spectroscopy and machine learning This study aimed to apply near-infrared (NIR) spectroscopy combined with machine learning techniques to identify yeast strains rapidly and practically, comparing the results with traditional molecular identification methods. Yeasts were isolated from the digestive tracts of aquatic mining insects collected in the extreme north of the Western Amazon (Roraima), Brazil, and preserved through cryopreservation and mineral oil methods. Molecular identification involved PCR amplification and sequencing of ribosomal DNA regions. NIR spectroscopy, coupled with multivariate analysis and machine learning algorithms such as principal component analysis (PCA), hierarchical cluster analysis (HCA), k-nearest neighbor (KNN), and soft independent modeling by class analogy (SIMCA), was used to analyze and classify the yeast samples, accurately identified yeast strains at the genus and species levels, achieving 100% accuracy in both the calibration and validation sets. The results indicate that this method provides a rapid, non-destructive, and environmentally friendly alternative to traditional molecular techniques, making it suitable for real-time, in situ analysis with minimal sample manipulation. Identification (biology) ; Spectroscopy ; Computer science ; Artificial intelligence ; Physics ; Biology ; Astronomy Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Noval Ariyanto | Harun Al Azies | Muhamad Akrom
Source: International Journal of Advances in Data and Information Systems (article, 2024-11-03)
Field: Materials Science — Materials Chemistry

Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds Corrosion is a major challenge affecting various industrial sectors, leading to increased operational costs and decreased equipment efficiency. The use of organic corrosion inhibitors is one of the promising solutions. This study applies an ensemble algorithm with a stacking method to estimate pyridazine-derived compounds corrosion inhibition efficiency. This study utilized various molecular characteristics of pyridazine compounds as inputs to predict inhibition efficiency values. After evaluating several boosting models, the stacking technique was chosen as it showed the best results. Stacking Model 6, which combines XGB, LGBM, and CatBoost as the base model with Random Forest as the meta-model, produced the most accurate prediction with an RMSE of 0.055. These findings indicate that machine learning approaches can effectively and efficiently predict corrosion inhibitor performance. This method offers a faster and more economical alternative to conventional experimental methods. Pyridazine ; Stacking ; Corrosion ; Ensemble learning ; Computer science ; Corrosion inhibitor ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Metallurgy ; Medicinal chemistry Corrosion Behavior and Inhibition
Authors: Dor Verbin | Pratul P. Srinivasan | Peter Hedman | Ben Mildenhall | Benjamin Attal | Richard Szeliski | Jonathan T. Barron
Source: arXiv (Cornell University) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. Moreover, these techniques rely on large computationally-expensive neural networks to model outgoing radiance, which severely limits optimization and rendering speed. We address these issues with an approach based on ray tracing: instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts reflection rays from these points and traces them through the NeRF representation to render feature vectors which are decoded into color using a small inexpensive network. We demonstrate that our model outperforms prior methods for view synthesis of scenes containing shiny objects, and that it is the only existing NeRF method that can synthesize photorealistic specular appearance and reflections in real-world scenes, while requiring comparable optimization time to current state-of-the-art view synthesis models. Casting ; Materials science ; Psychology ; Composite material Machine Learning in Materials Science
Authors: Roland Lindh
Source: The Journal of Physical Chemistry A (article, 2024-04-25)
Field: Chemistry — Physical and Theoretical Chemistry

A Brief Scientific Autobiography of Roland Lindh ADVERTISEMENT RETURN TO ISSUEPREVSpecial Issue Prefac...Special Issue PrefaceNEXTA Brief Scientific Autobiography of Roland LindhRoland Lindh*Roland LindhDepartment of Chemistry−BMC, Uppsala University, P.O. Box 576, SE-75123 Uppsala, SwedenUppsala Center for Computational Chemistry (UC3), Uppsala University, P.O. Box 576, SE-75123 Uppsala, Sweden*[email protected]More by Roland Lindhhttps://orcid.org/0000-0001-7567-8295Cite this: J. Phys. Chem. A 2024, 128, 16, 3049–3050Publication Date (Web):April 25, 2024Publication History Received6 March 2024Published online25 April 2024Published inissue 25 April 2024https://pubs.acs.org/doi/10.1021/acs.jpca.4c01500https://doi.org/10.1021/acs.jpca.4c01500introductionACS PublicationsCopyright © Published 2024 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views299Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSupporting Info (3)»Supporting Information Supporting Information SUBJECTS:Chemical engineering and industrial chemistry,Machine learning,Quantum mechanics,Students,Theoretical and computational chemistry Get e-Alerts Biography ; Philosophy ; Psychoanalysis ; Art ; Art history ; Psychology History and advancements in chemistry