jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mónika Farsang | Neubauer, Sophie A. | Grosu, Radu
Source: arXiv (Cornell University) (preprint, 2024-01-30)
Field: Chemistry — Physical and Theoretical Chemistry

Liquid Resistance Liquid Capacitance Networks We introduce liquid-resistance liquid-capacitance neural networks (LRCs), a neural-ODE model which considerably improve the generalization, accuracy, and biological plausibility of electrical equivalent circuits (EECs), liquid time-constant networks (LTCs), and saturated liquid time-constant networks (STCs), respectively. We also introduce LRC units (LRCUs), as a very efficient and accurate gated RNN-model, which results from solving LRCs with an explicit Euler scheme using just one unfolding. We empirically show and formally prove that the liquid capacitance of LRCs considerably dampens the oscillations of LTCs and STCs, while at the same time dramatically increasing accuracy even for cheap solvers. We experimentally demonstrate that LRCs are a highly competitive alternative to popular neural ODEs and gated RNNs in terms of accuracy, efficiency, and interpretability, on classic time-series benchmarks and a complex autonomous-driving lane-keeping task. Business Various Chemistry Research Topics
Authors: Gaurav Kumar | Komal Parashar | Prashant Sharma | Durgeshwar Pratap Singh | Anurag Shrivastava | Arun Pratap Srivastava | Krishna Srivastava | P. William
Source: Journal of Nano- and Electronic Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Novel AI-Based Methodology for Predicting Superconducting Films' Resistance-Temperature Properties with Nanomaterials Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine) Nanomaterials ; Materials science ; Superconductivity ; Resistance (ecology) ; Nanotechnology ; Condensed matter physics ; Physics ; Biology Thermal properties of materials ; Machine Learning in Materials Science
Authors: Andrés Martínez Mora | Dimitris Polychronopoulos | M. Ughetto | Sebastian Nilsson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-24)
Field: Materials Science — Materials Chemistry

Community-aware explanations in knowledge graphs with XP-GNN ABSTRACT Machine learning applications for the drug discovery pipeline have exponentially increased in the last few years. An example of these applications is the biological Knowledge Graph. These graphs represent biological entities and the relations between them based on existing knowledge. Graph machine learning models such as Graph Neural Networks can be applied on top of knowledge graphs to support the development of novel therapeutics. Nevertheless, Graph Neural Networks present an improved performance at the expense of complexity, becoming difficult to explain their decisions. State-of-the-art explanation algorithms for Graph Neural Networks focus on determining the most relevant subgraphs involved in their decision-making while considering graph elements (nodes and edges) as independent entities and ignoring any communities these graphs could present. We explore in this work the idea that graph community structure in biological Knowledge Graphs could provide a better grasp of the decision-making of Graph Neural Networks. For that purpose, we introduce XP-GNN , a novel explanation technique for Graph Neural Networks in Knowledge Graphs. XP-GNN exploits the communities of nodes or edges in graphs to refine their explanations, inspired by cooperative game theory . We characterize XP-GNN in a basic example and in terms of scalability and stability. In two relevant use cases for the drug discovery pipeline, XP-GNN provides more relevant explanations than previous techniques, being evaluated quantitatively and by domain experts. At the same time, XP-GNN presents limitations on scalability and stability, which we will address. ACM Reference Format Andrés Martínez Mora, Dimitris Polychronopoulos, Michaël Ughetto, and Sebastian Nilsson. 2024. Community-aware explanations in knowledge graphs with XP-GNN. In Proceedings of ACM Conference (Conference’17) . ACM, New York, NY, USA, 21 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn This work has been funded by AstraZeneca AB, Mölndal, Sweden and AstraZeneca Cambridge. Unfortunately, due to proprietary reasons from AstraZeneca AB, the data used in this work cannot be shared . Computer science ; Scalability ; Theoretical computer science ; Graph ; Artificial intelligence ; Biological network ; Exploit ; GRASP ; Machine learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Roman Rausch | Christoph Karrasch
Source: arXiv (Cornell University) (preprint, 2024-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Low-spin ground state of the giant single-molecule magnets {Mn$_{70}$} and {Mn$_{84}$} The single-molecule magnets {Mn$_{70}$} and {Mn$_{84}$} are characterized by a 14-site unit cell with $S=2$ spin sites arranged in a circular geometry. Experimentally, these systems exhibit a magnetic ground state with a notably low total spin $S_{\text{tot}}=5-7$. Up to now, this low-spin ground state has been up difficult to describe theoretically due to the complexity of the quantum Heisenberg model for such a large system. In this work, we fill this gap and demonstrate that the ground state of {Mn$_{70}$} and {Mn$_{84}$} is in fact governed by a small, finite $S_{\text{tot}}$ in quantitative agreement with the experiment. We employ accurate, large-scale SU(2)-symmetric density-matrix renormalization group calculations for a quantum Heisenberg model with previously published exchange parameters obtained by density-functional theory. We do not find a low-spin state for the same parameters and $S=1$ and thus propose that frustrated systems with $S\geq2$ are inherently prone to weak ferromagnetic interactions. This could account for the prevalence of similar low-spin Mn-based single-molecule magnets. Finally, we compute the full magnetization curve and find wide plateaus at 10/14, 11/14, 12/14 and 13/14 of the saturation, which can be traced back to nearly-independent 3-site clusters with broken inter-cluster bonds. Density matrix renormalization group ; Ground state ; Condensed matter physics ; Spin (aerodynamics) ; Ferromagnetism ; Physics ; Density functional theory ; Magnet ; Magnetization ; Cluster (spacecraft) ; Heisenberg model ; Renormalization group ; Quantum mechanics ; Magnetic field Magnetism in coordination complexes ; Magnetic properties of thin films ; Porphyrin and Phthalocyanine Chemistry
Authors: Catherine Tom | Ioanna Chazapi | Erwan Paineau | Ravi Kumar Pujala
Source: International Journal of Biological Macromolecules (article, 2024-09-19)
Field: Materials Science — Polymers and Plastics

Re-entrant phase transitions in Laponite/Gum Arabic nanocomposites Gum arabic ; Arabic ; Nanocomposite ; Phase transition ; Aqueous solution ; Polysaccharide ; Phase (matter) ; Chemical engineering ; Aqueous two-phase system ; Materials science ; Chemistry ; Polymer chemistry ; Composite material ; Organic chemistry ; Thermodynamics ; Physics Polymer Nanocomposites and Properties ; Clay minerals and soil interactions ; Polysaccharides Composition and Applications
Authors: Michele Cotrufo | Sedigheh Esfahani | Dmitriy Korobkin | Andrea Alù
Source: arXiv (Cornell University) (preprint, 2024-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Temporal Signal Processing with Nonlocal Optical Metasurfaces Nonlocal metasurfaces have recently enabled an ultra-compact, low-power and high-speed platform to perform analog image processing. While several computational tasks have been demonstrated based on this platform, most of the previous studies have focused only on spatial operations, such as spatial differentiation and edge detection. Here, we demonstrate that metasurfaces with temporal nonlocalities - that is, with a tailored dispersive response - can be used to implement time-domain signal processing in deeply subwavelength footprints. In particular, we show a passive metasurface performing first-order differentiation of an input signal with high-fidelity and high-efficiency. We also demonstrate that this approach is prone to scalability and cascaded computation. Our work paves the way to a new generation of ultra-compact, passive devices for all-optical computation, with applications in neural networks and neuromorphic computing. SIGNAL (programming language) ; Signal processing ; Computer science ; Physics ; Optics ; Digital signal processing Metamaterials and Metasurfaces Applications ; Indoor and Outdoor Localization Technologies ; Millimeter-Wave Propagation and Modeling
Authors: Dalia Mohammed Jomaa | Abbas Lutfi Hussein | Jamal Jalal Dawood
Source: Engineering and Technology Journal (article, 2024-04-06)
Field: Materials Science — Biomaterials

Optimization of mechanical and biocompatible properties of ZnOs-fiber membranes This study uses electro-spinning techniques to create a poly(vinyl alcohol)-chitosan-zinc oxide nanoparticles (PVA+CS)+ZnO NPs system in three concentrations of ZnO for application as composite nanofibers for wound treatment applications and&nbsp;&nbsp; Employing the Taguchi technique to optimize the mechanical characteristics via a four-level experimental design process (L16). Numerous techniques have been utilized to characterize the nanofibrous scaffolds: contact angle measurement, cytotoxicity testing, proliferation testing, evaluation of antimicrobial activity, Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersion X-ray Spectroscopy (EDX), and a comparative experimental study involving the determination of hardness using the Nanoindentation method for (PVA+CS)+ZnO. The ideal combination for adding ZnO as nanoparticles was found to be (PVA+CS)+0.6 ZnO, with a flow rate of 0.5 mL/hr, an applied voltage of 18 kV, and a needle tip-to-collector distance of 15 cm (position). This resulted in the smallest fiber diameter (48 nm) with a smooth and uniform distribution. As a consequence, (PVA+CS)+ZnO can be regarded as non-toxic in accordance with the criteria. The methyl thiazole tetrazolium (MTT) assay was used to evaluate the cell viability of&nbsp; L929 (cell cultures for skin). It significantly affects cell viability, achieving 50% in less than 24 hours, which means cell growth through 24 hours is necessary for embryonic development, tissue repair, and regulation of cell division in case of wound healing. Exhibiting findings of inhibition zone diameter in antibacterial activity test exceeding 20 mm. According to the results, (PVA+CS)+0.6 ZnO's hydrophilic properties showed a well-connected porous structure and made it easier for nutrients and oxygen to be exchanged, encouraging cellular activity. After evaluating the mechanical characteristics of each specimen, the main determinants of these characteristics are determined using a Taguchi orthogonal array L16 design. The results of this study suggest that (PVA+CS)+0.6 ZnO could be a good biomedical material for skin tissue engineering applications. Biocompatible material ; Membrane ; Fiber ; Materials science ; Composite material ; Chemical engineering ; Polymer science ; Chemistry ; Biomedical engineering ; Engineering ; Biochemistry Electrospun Nanofibers in Biomedical Applications
Authors: Nisreen J. Razak | Mohammed H. Abass | Khairullah M. Awad
Source: Natural and Engineering Sciences (article, 2024-12-29)
Field: Chemistry — Analytical Chemistry

Assessment of Heavy Metals Phytotoxicity on Seed Germination and Seedling Growth of Tomato Plants (Solanum Lycopersicum L.) This study has been designed to examine the effects of heavy metals, specifically cobalt (Co), chromium (Cr), cadmium (Cd), and lead (Pb), on the germination indices and growth of different tomato varieties (Solanum lycopersicum L.). The current findings revealed that all investigated heavy metals had a significant and detrimental impact on both germination indices and growth parameters at laboratory and greenhouse conditions, with Cd and Pb exhibiting the most pronounced effects. In comparison to the control treatment, the germination percentage decreased by 74.66 and 81.56% when exposed to Pb (380 mg.L-) in laboratory and greenhouse conditions, respectively. Additionally, the germination time was extended two-folds the duration under Cd and Pb treatments. Pb exhibited a significant impact on the reduction of both hypocotyl and radicle lengths in emerging seedlings, at decrease levels of 51.40 and 59.02%, respectively, compared to the control treatment. The results also indicated that Cd (16 mg.L-1) had the most pronounced effect on the growth parameters of tomato seedlings cultivated in potting soil under greenhouse conditions, although there was no significant difference observed when compared to Pb. The plant lost more than half of its height, as well as 69% of its shoot FW, 68% of its shoot DW, 70% of its root FW, and 74% of its root DW. Now that we know these things, we need to clean up the heavy metal trash that comes from farms. Taking better care of the land and using methods that lessen the effects of metals is one way to improve plant health and long-term production. Seedling ; Solanum ; Germination ; Phytotoxicity ; Horticulture ; Biology ; Agronomy Heavy Metals in Plants ; Seed Germination and Physiology ; Leaf Properties and Growth Measurement
Authors: Saeful Amin | Rachma Nur Azijah | Febrianti Rizki Gunawan
Source: Jurnal Ilmu Medis Indonesia (article, 2024-09-20)
Field: Chemistry — Organic Chemistry

Eksplorasi Senyawa Alami sebagai Lead Antikanker Payudara dengan Pendekatan In Silico Purpose: This study aimed to explore the potential of natural compounds as lead candidates for breast cancer therapy through a medicinal chemistry framework using in silico methods, with the goal of identifying safer and more effective alternatives to conventional treatments. Methodology: A qualitative library-based research design was employed using a systematic literature review of 15 primary national and international journal articles published between 2020 and 2025. The computational tools used in the study included AutoDock Vina and PyRx for molecular docking and virtual screening, SwissADME and pkCSM for ADMET predictions, and GROMACS for molecular dynamics simulations. Results: Several natural compounds, including berberine, ellagic acid, camptothecin, and kaempferol, exhibited strong binding affinities toward key breast cancer molecular targets, such as estrogen receptor (ER), progesterone receptor (PR), HER2, and PD-L1. These compounds formed stable ligand–receptor complexes and showed favorable pharmacokinetic properties, conforming to Lipinski’s Rule of Five, thereby suggesting their viability as lead candidates for further development. Conclusions: This study highlights the promising potential of selected natural compounds as lead structures for breast cancer drug development. The integration of molecular docking, pharmacokinetic prediction, and dynamic simulation provides a robust platform for early phase drug discovery. Limitations: The primary limitation of this study is that in silico simulations do not fully capture complex biological interactions within the human body, including metabolic pathways and immune system responses. Contribution: his study contributes to the fields of medicinal chemistry and pharmaceutical sciences by offering updated insights into the utility of natural compounds as anti-breast cancer agents. Biology ; In silico ; Genetics ; Gene Inorganic and Organometallic Chemistry ; Extraction and Separation Processes
Authors: Shuai-Ting Bao | Dian Wu | P. Zhang | Liang Wang
Source: arXiv (Cornell University) (preprint, 2024-07-29)
Field: Materials Science — Materials Chemistry

Learning eigenstates of quantum many-body Hamiltonians within the symmetric subspaces using neural network quantum states The exploration of neural network quantum states has become widespread in the studies of complicated quantum many-body systems. However, achieving high precision remains challenging due to the exponential growth of Hilbert space size and the intricate sign structures. Utilizing symmetries of the physical system, we propose a method to evaluate and sample the variational ansatz within a symmetric subspace. This approach isolates different symmetry sectors, reducing the relevant Hilbert space size by a factor approximately proportional to the size of the symmetry group. It is inspired by exact diagonalization techniques and the work of Choo et al. in Phys. Rev. Lett. 121, 167204 (2018). We validate our method using the frustrated spin-1/2 $J_1$-$J_2$ antiferromagnetic Heisenberg chain and compare its performance to the case without symmetrization. The results indicate that our symmetric subspace approach achieves a substantial improvement over the full Hilbert space on optimizing the ansatz, reducing the energy error by orders of magnitude. We also compare the results on degenerate eigenstates with different quantum numbers, highlighting the advantage of operating within a smaller Hilbert subspace. Linear subspace ; Eigenvalues and eigenvectors ; Quantum ; Artificial neural network ; Physics ; Quantum mechanics ; Mathematics ; Computer science ; Pure mathematics ; Artificial intelligence Machine Learning in Materials Science
Authors: Yang Wu | Wenjing Zhao
Source: National Science Open (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

High Thermoelectric Performance Sheds New Light for Low-grade Waste Heat Recovery Recovering waste heat from fuel energy supply is a haunting challenge for greatly reducing the emissions of greenhouse gas CO 2 , and achieving sustainable economy .Importantly, a significant amount of energy is dissipated to environment as low-grade heat (below 250 o C), largely produced in transportation and chemical engineering industry.It accounts for ~30% of the total global energy products but is much more difficult to recycle than high-grade heat (Figure 1 a). 1 So the reuse of low-grade heat is of great importance for the future energy demands and the issue of climate change. 2,3So far, several technologies have been developed to recover low-grade waste heat, such as organic Rankine cycle, water desalination, piezoelectric, and thermoelectric conversion. 4Among them thermoelectric that enables direct and inverse heat-to-electricity conversion is of great interests due to its wide adaptability and long-lasting reliability.However, the low conversion efficiency of thermoelectric power generation limits its potential application in recovering low-grade wasted heat. 5,6e efficiency of thermoelectric power generation is calculated by the unit-less figure of merit, zT=σS 2 T/κ, , where σ, S, κ, and T are the electrical conductivity, Seebeck coefficient, and thermal conductivity of the material, and absolute temperature in kelvin, respectively.To improve the performance, one should expect both a high power factor (σS 2 ), a low thermal conductivity, and material stability with temperature.Unfortunately, the above parameters are entangled, so that it is very difficult to adjust them independently.Traditional methods to decouple this interplay issue is to introduce nanoscale precipitates and regulate the band structure and the Thermoelectric effect ; Waste heat recovery unit ; Materials science ; Environmental science ; Waste management ; Business ; Process engineering ; Engineering ; Mechanical engineering ; Thermodynamics ; Physics ; Heat exchanger Advanced Thermoelectric Materials and Devices
Authors: Ayush Suhane | Matthias Militzer
Source: Journal of Phase Equilibria and Diffusion (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Atomistically Informed Phase Field Modeling of Solid-Solid Phase Transformations Phase (matter) ; Materials science ; Field (mathematics) ; Thermodynamics ; Chemistry ; Physics ; Mathematics ; Organic chemistry Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; nanoparticles nucleation surface interactions
Authors: Masa Tsuchiya | Kenichi Yoshikawa | Alessandro Giuliani
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-26)
Field: Chemistry — Physical and Theoretical Chemistry

Genomic-Thermodynamic Phase Synchronization: Maxwell’s Demon-Like Regulation of Cell Fate Transition Abstract Dynamic criticality - the balance between order and chaos - is fundamental to genome regulation and cellular transitions. In this study, we investigate the distinct behaviors of gene expression dynamics in MCF-7 breast cancer cells under two stimuli: Heregulin (HRG), which promotes cell-fate transitions, and Epidermal Growth Factor (EGF), which binds to the same receptor but fails to induce cell-fate changes. We model the system as an open, non-equilibrium thermodynamic system and introduce a convergence-based framework for robust estimation of information-thermodynamic metrics. Our analysis reveals that the Shannon entropy of the critical point (CP) dynamically synchronizes with the entropy of the rest of the whole expression system (WES), reflecting coordinated transitions between ordered and disordered phases. This phase synchronization is driven by net mutual information scaling with CP entropy dynamics, demonstrating how the CP governs genome-wide coherence. Furthermore, higher-order mutual information emerges as a defining feature of the nonlinear gene expression network, capturing collective effects beyond simple pairwise interactions. By achieving thermodynamic phase synchronization, the CP orchestrates the entire expression system. Under HRG stimulation, the CP becomes active, functioning as a Maxwell’s demon with dynamic, rewritable chromatin memory to guide a critical transition in cell fate. In contrast, under EGF stimulation, the CP remains passive during non-critical expression dynamics. These findings establish a biophysical framework for cell-fate determination, paving the way for innovative approaches in cancer research and stem cell therapy. Maxwell's demon ; Synchronization (alternating current) ; Demon ; Phase transition ; Transition (genetics) ; Physics ; Statistical physics ; Biology ; Genetics ; Quantum mechanics ; Gene ; Mathematics ; Topology (electrical circuits) ; Combinatorics thermodynamics and calorimetric analyses ; Advanced Thermodynamics and Statistical Mechanics ; Gene Regulatory Network Analysis
Authors: Liang Hou | Hongyuan Zhou | Dandan Zhang | Ganqing Lu | Dejiang Zhang | Tingting Liu | Shuyuan Xiao | Tianbao Yu
Source: arXiv (Cornell University) (preprint, 2024-07-27)
Field: Materials Science — Surfaces, Coatings and Films

High-efficiency broadband achromatic metalens in the visible The metalenses have been extensively studied for their compact and flexible characteristics in focusing and imaging applications. However, it remains a significant challenge to design a broadband achromatic metalens that maintains high efficiency under arbitrary polarization incidence. In this work, we design a broadband achromatic metalens that achieves polarization-independent, high-efficiency focusing by effectively utilizing both co-polarization and cross-polarization components of the transmitted light. Using a minimalist anisotropic nanofin library, we optimize the phase distribution of the metalens at each designed wavelength with the particle swarm algorithm. Numerical simulations demonstrate a stable focal length with a deviation of less than 4$\%$ and an average focusing efficiency of 80.5$\%$ in the visible wavelength range of 450 to 650 nm. Moreover, we design a multi-wavelength off-axis bi-focal metalens to demonstrate the flexible control of output light phase and dispersion achieved by this method. The generality of this design enables its implementation in various metasurface devices, accelerating applications in broadband imaging and virtual/augmented reality. Achromatic lens ; Broadband ; Optics ; Physics Optical Coatings and Gratings ; Optical Polarization and Ellipsometry ; Photonic Crystals and Applications