jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saeid Bayat | Nastaran Shahmansouri | Satya RT Peddada | Alex Tessier | Adrian Butscher | James T. Allison
Source: arXiv (Cornell University) (preprint, 2023-11-24)
Field: Materials Science — Materials Chemistry

Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction In this research, we developed a graph-based framework to represent various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 30% of the labeled data to predict the systems' performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 70% of the data, which serves as the test set. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via the open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios. Enumeration ; Computer science ; Solver ; Graph ; Rank (graph theory) ; Reduction (mathematics) ; Artificial neural network ; Data mining ; Mathematical optimization ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Fuel Cells and Related Materials
Authors: Juan Alcantara | Kelley Chiu | John D. Bickel | Robert C. Rizzo | Carlos Simmerling
Source: ChemRxiv (preprint, 2023-09-25)
Field: Chemistry — Spectroscopy

Rapid rescoring and refinement of ligand-receptor complexes using REMD with a MC pose reservoir Virtual screening (VS) involves generation of poses for a library of ligands, and ranking using simplified energy functions and limited flexibility. Top scored poses are used to rank and prioritize ligands. Here we adapt the reservoir replica exchange molecular dynamics (res-REMD) method to re-rank poses generated through VS. REMD simulations are carried out, but with occasional Monte Carlo jumps to alternate VS-generated poses using a Metropolis criterion. The simulations converge within 10 ns for all systems, generating populations of alternate poses in the context of fully flexible ligand and protein side chains. The protocol is applied to four model protein-ligand complexes, where DOCK resulted in two successes and two scoring failures. In all four systems, the most populated cluster from the final ensemble exhibits high similarity to the crystallographic pose, with ligand RMSD values under 2.0 Å. Both DOCK failures were rescued. For one DOCK success, the protocol identified the correct pose, but also sampled an alternate pose at equal probability. Opportunities for future improvement and extension are discussed. Computer science ; Context (archaeology) ; Flexibility (engineering) ; DOCK ; Ranking (information retrieval) ; Rank (graph theory) ; Protocol (science) ; Fragment (logic) ; Monte Carlo method ; Algorithm ; Data mining ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Statistics ; Biology Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andrew P. Latham | Elizabeth S. Levy | Benjamin D. Sellers | Dennis Leung
Source: Preprints.org (preprint, 2023-11-13)
Field: Chemistry — Organic Chemistry

Utilizing Molecular Simulation to Examine Nanosuspension Stability Drug nanosuspensions offer a promising approach to improve bioavailability for poorly soluble drug candidates. Such formulations often necessitate the inclusion of an excipient to stabilize the drug nanoparticles. However, a rationale to choose the correct excipient for a given drug candidate remains unclear. To gain molecular insight into formulation design, this work first utilizes molecular dynamics simulation to computationally investigate drug-excipient interactions for a number of combinations that have been previously studied experimentally. We find that hydrophobic interactions drive excipient adsorption to drug nanoparticles, and that the fraction of polar surface area serves as a predictor for experimental measurements of nanosuspension stability. To test these ideas prospectively, we applied our model to an uncharacterized drug compound, GDC-0810. Our simulations predicted that salt forms of GDC-0810 would lead to more stable nanosuspensions than the neutral form; therefore, we tested the stability of salt GDC-0810 nanosuspensions and found that the salt form readily formed nanosuspensions even without excipient. To avoid computationally expensive simulations in the future, we extended our model by showing that simple, two-dimensional properties of the single drug molecule can be used to rationalize nanosuspension design without simulation. In all, our work demonstrates how computational tools can provide molecular insight into drug-excipient interactions, and aid in rational formulation design. Excipient ; Molecular dynamics ; Bioavailability ; Materials science ; Drug ; Drug delivery ; Nanoparticle ; Nanotechnology ; Stability (learning theory) ; Biological system ; Computer science ; Chromatography ; Chemistry ; Pharmacology ; Computational chemistry ; Machine learning Surfactants and Colloidal Systems ; Drug Solubulity and Delivery Systems ; Microfluidic and Capillary Electrophoresis Applications
Authors: Marco Ruggiero
Source: Preprints.org (preprint, 2023-10-09)
Field: Chemistry — Organic Chemistry

Design and Development of Novel Compounds Aimed at Optimizing the Function of Microtubules and Tubulin in the Immune System and the Brain Here, I describe the design and development of novel compounds aimed at optimizing the function of microtubules and tubulin in the immune system and the brain as they relate to the Orch OR theory of consciousness. These compounds are defined by the following molecular formulas:C65H100CoN15O22P(C6H12O5);C65H100CoN15O22P(C5H10NO4);(C65H100CoN15O22P)(C6H12N2O)n;(C65H100CoN15O22P)(C6H12O5)(C6H12N2O)n;(C6H12N2O)n(C6H12O5)m;Zn(C6H12O5)2;These molecules, formulated under the form of drugs or food supplements, have the potential to positively affect a number of areas ranging from prevention and therapy of neurological diseases to supporting the immune system. Immune system ; Microtubule ; Tubulin ; Brain function ; Function (biology) ; Consciousness ; Neuroscience ; Biology ; Computational biology ; Chemistry ; Cell biology ; Immunology Click Chemistry and Applications ; Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis
Authors: Can Chen | Yingxue Zhang | Xue Liu | Mark Coates
Source: arXiv (Cornell University) (preprint, 2023-01-07)
Field: Materials Science — Materials Chemistry

Bidirectional Learning for Offline Model-based Biological Sequence Design Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for exploitation and a backward mapping for constraint, and it relies on the neural tangent kernel (NTK) of an infinitely wide network to build a proxy model. Though effective, the NTK cannot learn features because of its parametrization, and its use prevents the incorporation of powerful pre-trained Language Models (LMs) that can capture the rich biophysical information in millions of biological sequences. We adopt an alternative proxy model, adding a linear head to a pre-trained LM, and propose a linearization scheme. This yields a closed-form loss and also takes into account the biophysical information in the pre-trained LM. In addition, the forward mapping and the backward mapping play different roles and thus deserve different weights during sequence optimization. To achieve this, we train an auxiliary model and leverage its weak supervision signal via a bi-level optimization framework to effectively learn how to balance the two mappings. Further, by extending the framework, we develop the first learning rate adaptation module \textit{Adaptive}-$η$, which is compatible with all gradient-based algorithms for offline model-based optimization. Experimental results on DNA/protein sequence design tasks verify the effectiveness of our algorithm. Our code is available~\href{https://anonymous.4open.science/r/BIB-ICLR2023-Submission/README.md}{here.} Computer science ; Leverage (statistics) ; Sequence (biology) ; Code (set theory) ; Linearization ; Algorithm ; Artificial intelligence ; Machine learning ; Programming language ; Nonlinear system Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Machine Learning and Algorithms
Authors: M. Aygün | Zeynep Aygün
Source: Nuclear Physics and Atomic Energy (article, 2023-03-25)
Field: Materials Science — Materials Chemistry

Density-dependent analytical equations of radiation shielding parameters for super alloys by linear regression analysis Super alloys have great interest with good mechanical strength, surface stability, high operating temperatures, and high resistance to corrosion and oxidation features. In the study, new, reliable, and practical equations which give the radiation shielding parameters depending on the density of super alloys are obtained. For this analysis, MAR-247, MAR 302, Inconel 625, Inconel 718, Nimocast 75, WI-52, Inconel 617, Incoloy 800HT, Inconel 939, 713LC, and 7925A super alloys are chosen. The radiation shielding parameters such as linear attenuation coefficient, effective atomic number, half value layer, mean free path, and fast neutron removal cross-section are calculated by using Phy-X/PSD program. Then, new analytical equations providing the radiation shielding parameters by linear regression analysis are evaluated. Inconel ; Incoloy ; Electromagnetic shielding ; Materials science ; Linear regression ; Corrosion ; Attenuation ; Composite material ; Alloy ; Physics ; Optics ; Mathematics Radiation Shielding Materials Analysis ; Nuclear Materials and Properties ; Graphite, nuclear technology, radiation studies
Authors: Yonatan Betancur-Ocampo | B. Manjarrez-Montañez | A. M. Martínez-Argüello | R. A. Méndez-Sánchez
Source: arXiv (Cornell University) (preprint, 2023-06-08)
Field: Chemistry — Spectroscopy

Twofold topological phase transitions induced by third-nearest-neighbor interactions in 1D chains Strong long-range hoppings up to third nearest neighbors may induce a topological phase transition in one-dimensional chains. Unlike the Su-Schrieffer-Heeger model, this transition from trivial to topological phase occurs with the emergence of a pseudospin valley structure and a twofold nontrivial topological phase. Within a tight-binding approach, these topological phases are analyzed in detail and it is shown that the low-energy excitations follow a modified Dirac equation, in which the dynamics of particles with positive and negative mass occur differently. An experimental realization in a one-dimensional elastic chain, where it is feasible to tune directly the third-nearest-neighbor hoppings, is proposed. k-nearest neighbors algorithm ; Phase transition ; Phase (matter) ; Physics ; Topology (electrical circuits) ; Condensed matter physics ; Computer science ; Mathematics ; Quantum mechanics ; Combinatorics ; Artificial intelligence Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Chemical Thermodynamics and Molecular Structure
Authors: M. A. Marieva | А. А. Шацов
Source: Powder Metallurgy аnd Functional Coatings (article, 2023-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of the concentration inhomogeneity of powder magnetic hard alloys based on the Fe-Cr-Co-Mo system and the effect of Sm additions on their magnetic properties Hysteresis alloys based on Fe-Cr-Co system are extensively used in the instrument-making industry as a material for synchronous motors of navigation systems, in the electronic industry, and other mechanical engineering fields. The following requirements are imposed on Fe-Cr-Co alloys: temperature stability of magnetic characteristics over time, manufacturability, low porosity and concentration inhomogeneity, which allow to obtain high-quality magnetic and mechanical properties. Materials based on conventional alloying systems, such as Fe-Cr-Co, have outlived themselves. An urgent line of the development of new materials and improvement of the properties of existing ones is alloying with rare-earth metals. The effect produced by Sm addition on powder analogs of Fe-Cr-Co system remains unstudied. In this paper, 22Kh15K4MS powder magnetic hard alloy alloyed with samarium in an amount of 0.5 wt. % was studied. The billets were obtained by cold pressing at a pressure of 600 MPa followed by vacuum sintering. The concentration inhomogeneity of Cr, Co, Mo, Sm was determined after 12 different sintering modes. A model of diffusion homogenization of ridge alloys, which allows to numerically evaluate the effect of sintering modes on the concentration inhomogeneity, was plotted. The distributions of chromium, cobalt, and molybdenum correspond to the asymptotically logarithmically normal law. Samarium is unevenly distributed in the structure. The effect of samarium additions on the magnetic properties of the alloy has been demonstrated. The alloying of 22Kh15K4MS alloy with 0.5 wt. % of samarium allows to obtain powder hysteresis magnets with a coercive force in the range from 3.9 to 33.0 kA/m and a residual magnetic induction from 0.44 to 0.95 T. Materials science ; Samarium ; Sintering ; Alloy ; Coercivity ; Compaction ; Metallurgy ; Cobalt ; Magnetic hysteresis ; Porosity ; Magnet ; Powder metallurgy ; Magnetization ; Pressing ; Composite material ; Condensed matter physics ; Magnetic field ; Mechanical engineering Magnetic Properties of Alloys ; Advanced materials and composites ; Metallic Glasses and Amorphous Alloys
Authors: В. А. Володин | Г. Н. Камаев | V. A. Gritsenko | Chepkova C.G. | Prosvirin I.P.
Source: Журнал технической физики (article, 2023-01-01)
Field: Materials Science — General Materials Science

Composition and optical properties of amorphous plasma-chemical silicon oxynitride of variable composition a-SiO-=SUB=-x-=/SUB=-N-=SUB=-y-=/SUB=- : H The a-SiO x N y : H films of various compositions were obtained by plasma chemical deposition from a gas mixture of 10% monosilane (diluted with argon) and nitrogen in the presence of residual oxygen in the working gas mixture. The nitrogen flow rate varied in the range from 4 to 6 cm 3 /min, the power of the high-frequency generator (13.56 MHz) varied in the range of 50-150 Watts. The electronic structure and optical properties of the films were studied using X-ray photoelectron spectroscopy, vibrational spectroscopy, transmission and reflection spectroscopy, and spectral ellipsometry. It is shown that, as the generator power decreases, the content of excess silicon in the films increases and amorphous silicon nanoclusters appear. As the generator power increases, the oxygen concentration in the films decreases. Apparently, this is due to the greater dissociation of molecular nitrogen with an increase in the power of the plasma discharge and an increase in the concentration of active nitrogen. Thus, it is possible to control the composition of aa-SiO x N y : H films not only by changing the nitrogen flow, but also by varying the generator power. Keywords: silicon oxynitride, plasma-chemical deposition, stoichiometric composition, Si nanoclusters. Silicon oxynitride ; Analytical Chemistry (journal) ; X-ray photoelectron spectroscopy ; Amorphous solid ; Silicon ; Nanoclusters ; Ellipsometry ; Materials science ; Argon ; Silane ; Nitrogen ; Chemistry ; Thin film ; Chemical engineering ; Crystallography ; Nanotechnology ; Silicon nitride ; Organic chemistry Material Properties and Applications ; Silicon Nanostructures and Photoluminescence ; Advanced Materials and Semiconductor Technologies
Authors: Deniz Akın Şahbaz
Source: Preprints.org (preprint, 2023-07-12)
Field: Chemistry — Analytical Chemistry

Enhanced Adsorption Performance Cross-Linked Chitosan/&lt;em&gt;Citrus reticulata&lt;/em&gt; Peel Waste Composites as Low Cost and Green Bio-Adsorbents: Kinetic, Equilibrium Isotherm, and Thermodynamic Studies This study revealed the synthesis of cross-linked chitosan/Citrus reticulata peel waste (C/CRPW) composites that could be used as low-cost and green bio-adsorbents for the removal of Congo red (CR) dye from aqueous solutions. C/CRPW composites containing different amounts of Citrus reticulata peel waste (CRPW) and chitosan were prepared and cross-linked with glutaraldehyde. The composites were characterized by FESEM, FTIR, and BET. The C/CRPW composites as a new type of bio-adsorbents displayed superior adsorption capability toward anionic CR molecules and the adsorption capacities increased by incorporation of CRPW. Effects of different ambient conditions such as contact time, pH, adsorbent dosage, initial adsorbate concentration, and temperature were fully studied. The conditions which obtained 43.57 mg/g of the highest adsorption capacity were conducted at pH 4, initial concentration of 100 mg/L, adsorbent dosage of 2.0 g/L, and contact time of 24 hours at 328 K. The adsorption data was found to follow the pseudo-second-order kinetic model and the Freundlich isotherm model. According to the findings of this investigation, it was observed that the C/CRWP composites can be used as adsorbents due to their advantages of simple preparation process, environmentally friendly, renewable, efficient, and low-cost. Adsorption ; Chitosan ; Glutaraldehyde ; Freundlich equation ; Aqueous solution ; Materials science ; Composite material ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Chemical engineering ; Chemistry ; Organic chemistry Dye analysis and toxicity
Authors: Ali Abou Taka | John M. Herbert | Laura M. McCaslin
Source: ChemRxiv (preprint, 2023-10-11)
Field: Chemistry — Physical and Theoretical Chemistry

Ground State Orbital Analysis Predicts S1 Charge Transfer in Donor–Acceptor Materials Donor-acceptor (D-A) materials, such as D-A co- crystals and D-A copolymers, can exhibit a wide range of unique photophysical properties with applications in next-generation optoelectronics. The properties of D-A dimer models, computed with electronic structure calculations, are often employed to predict properties of D-A materials. One of the most important D-A dimer quantities is the degree of charge transfer (DCT) in the S1 state, which correlates with properties such as fluorescence lifetimes and intersystem crossing rates. Predictive metrics of the S1 DCT generally require an excited state quantum chemistry calculation. Presented here is a novel metric for predicting the degree of charge transfer (DCT) in the S1 electronic state of D-A materials, computed solely with ground state orbital analysis. This metric computes the average of two quantities: (1) the degree of similarity between the highest occupied molecular orbital (HOMO) in the donor molecule and the D-A complex and (2) the degree of similarity between the lowest occupied molecular orbital (LUMO) in the acceptor molecule and the D-A complex. A linear relationship between this similarity metric and the DCT in the S1 state (HOMO → LUMO transition) is demonstrated for a data set of 31 D-A dimers. The integration of this novel orbital structure- function relationship into high-throughput screening methods is discussed alongside best practices for choosing molecular geometries and quantifying the DCT. HOMO/LUMO ; Intersystem crossing ; Metric (unit) ; Molecular orbital ; Acceptor ; Ground state ; Charge (physics) ; Excited state ; Similarity (geometry) ; Chemistry ; Molecule ; Electronic structure ; Materials science ; Physics ; Chemical physics ; Molecular physics ; Computational chemistry ; Atomic physics ; Computer science ; Quantum mechanics ; Singlet state Photochemistry and Electron Transfer Studies ; Organic Light-Emitting Diodes Research ; Luminescence and Fluorescent Materials
Authors: Steven M. Baksa | Simon Gelin | Seda Oturak | Spurling, Robert Jackson | Sepehrinezhad, Alireza | Jacques, Leonard | Susan Trolier‐McKinstry | van Duin, Adri C. T. | Jon‐Paul Maria | Andrew M. Rappe | Ismaïla Dabo
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Strain fluctuations unlock ferroelectricity in wurtzites Ferroelectrics are of practical interest for non-volatile data storage due to their reorientable, crystallographically defined polarization. Yet efforts to integrate conventional ferroelectrics into ultrathin memories have been frustrated by film-thickness limitations, which impede polarization reversal under low applied voltage. Wurtzite materials, including magnesium-substituted zinc oxide (Zn,Mg)O, have been shown to exhibit scalable ferroelectricity as thin films. In this work, we explain the origins of ferroelectricity in (Zn,Mg)O, showing that large strain fluctuations emerge locally in (Zn,Mg)O and can reduce local barriers to ferroelectric switching by more than 40%. We provide concurrent experimental and computational evidence of these effects by demonstrating polarization switching in ZnO/(Zn,Mg)O/ZnO heterostructures featuring built-in interfacial strain gradients. These results open up an avenue to develop scalable ferroelectrics by controlling strain fluctuations atomistically. Ferroelectricity ; Materials science ; Condensed matter physics ; Polarization (electrochemistry) ; Microelectronics ; Wurtzite crystal structure ; Dielectric ; Coercivity ; Nanotechnology ; Optoelectronics ; Zinc ; Chemistry ; Physics ; Metallurgy ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies