jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Victor Kwan | Pranav Ballaney | Titiksha | Styliani Consta
Source: arXiv (Cornell University) (preprint, 2023-03-02)
Field: Chemistry — Spectroscopy

Limitations of atomistic modeling to reveal ejection of proteins from charged nanodroplets Molecular dynamics using atomistic modeling is frequently used to unravel the mechanisms of macroion release from electrosprayed droplets. However, atomistic modeling is currently feasible for only the smallest window of droplet sizes appearing at the end of a disintegrating droplet's lifetime. The relevance of the observations made to the actual droplet evolution, which is much longer that the simulated sizes, has not been addressed. Here, we perform a systematic study of desolvation mechanisms of poly(ethylene glycol) (PEG), protonated peptides of different compositions and proteins in order to (a) examine whether atomistic modeling can establish the extrusion mechanism of proteins from droplets and (b) obtain insight for the charging mechanism in larger droplets than those simulated. Atomistic modeling of PEG charging shows that above a critical droplet size charging occurs transiently by transfer of ions from the solvent to the macroion, while below the critical size, the capture of the ion from PEG has a lifetime sufficiently long for extrusion of the charged PEG from an aqueous droplet. This is the first report of the role of droplet curvature in the conformations and charging of macroions. Simulations with highly hydrophobic peptides show that partial extrusion of a peptide from the droplet surface is rare relative to desolvation by drying-out. Differently from what has been presented in the literature we argue that atomistic simulations have not sufficiently established extrusion mechanism of proteins from droplets and their charging mechanism. Moreover, we argue that release of highly charged proteins can occur at an earlier stage of a droplet's lifetime than predicted by atomistic modeling. In this earlier stage, we emphasize the key role of jets emanating from a droplet at the point of charge-induced instability in the release of proteins. Molecular dynamics ; Extrusion ; Chemical physics ; Ethylene glycol ; PEG ratio ; Chemistry ; Ion ; Curvature ; Aqueous solution ; Materials science ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Abhiraj Sharma | Alfredo Metere | Phanish Suryanarayana | Lucas Erlandson | Edmond Chow | John E. Pask
Source: arXiv (Cornell University) (preprint, 2023-02-20)
Field: Materials Science — Materials Chemistry

GPU acceleration of local and semilocal density functional calculations in the SPARC electronic structure code We present a GPU-accelerated version of the real-space SPARC electronic structure code for performing Kohn-Sham density functional theory calculations within the local density and generalized gradient approximations. In particular, we develop a modular math kernel based implementation for NVIDIA architectures wherein the computationally expensive operations are carried out on the GPUs, with the remainder of the workload retained on the CPUs. Using representative bulk and slab examples, we show that GPUs enable speedups of up to 6x relative to CPU-only execution, bringing time to solution down to less than 30 seconds for a metallic system with over 14,000 electrons, and enabling significant reductions in computational resources required for a given wall time. Computer science ; Computational science ; Parallel computing ; Code (set theory) ; Acceleration ; Kernel (algebra) ; Remainder ; Density functional theory ; Electronic structure ; Modular design ; Physics ; Mathematics ; Discrete mathematics ; Quantum mechanics ; Programming language ; Arithmetic Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Condensed Matter Physics
Authors: Kris Strunge | Tucker Burgin | Thaddeus W. Golbek | Steven J. Roeters | Jim Pfaendtner | Tobias Weidner
Source: ChemRxiv (preprint, 2023-04-23)
Field: Chemistry — Organic Chemistry

Umbrella-like helical structure of alpha-synuclein at the air-water interface observed with experimental and theoretical sum frequency generation spectroscopy The misfolding of ⍺-synuclein (aS) into amyloid aggregates is associated with severe brain disorders. Aggregat-ed copies of aS are found in the amyloid aggregates observed in brain tissues from Parkinson’s patients. Surfaces are known to catalyze the formation of amyloid aS aggregates. Despite the importance of the role of inter-faces and several decades of structural studies, the 3D structure of aS when bound to interfaces is still not completely clear. Hydrophobic interfaces are particularly important here. We report interface-specific sum-frequency generation (SFG) experiments to determine how monomeric aS binds to the air-water interface, a model system for hydrophobic surfaces in general. We model the SFG data by combining the experimental data directly to theoretical spectra calculations from molecular dynamics simulations. We find that aS, which is an intrinsically disordered protein in solution, folds into a defined, mostly helical, secondary structure at the air-water interface. The binding pose is reminiscent of an umbrella-shape, where the C-terminus represents the ‘pole’ and protrudes into the water phase, while the N-terminus and the NAC region span the canopy at the interface. In this binding pose, aS is prone to aggregate, which could explain the catalytic effect of hydrophobic interfaces and air bubbles on aS fibrillation. Alpha-synuclein ; Spectroscopy ; Chemical physics ; Biophysics ; Chemistry ; Monomer ; Molecular dynamics ; Amyloid (mycology) ; Fibril ; Crystallography ; Protein structure ; Physics ; Computational chemistry ; Biochemistry ; Biology Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Photochemistry and Electron Transfer Studies
Authors: Elisa Guida | Andrea Bisso | Cristina Fenollar‐Ferrer | Marco Napoli | Claudio Anselmi | Javier E. Girardini | Paolo Carloni | Giannino Del Sal
Source:  (preprint, 2023-03-30)
Field: Chemistry — Organic Chemistry

Supplementary Methods, Figures 1-4 from Peptide Aptamers Targeting Mutant p53 Induce Apoptosis in Tumor Cells Supplementary Methods, Figures 1-4 from Peptide Aptamers Targeting Mutant p53 Induce Apoptosis in Tumor Cells Aptamer ; Mutant ; Apoptosis ; Peptide ; Cancer research ; Tumor cells ; P53 protein ; Chemistry ; Cell biology ; Molecular biology ; Biology ; Biochemistry ; Gene Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; RNA modifications and cancer
Authors: Senlin Fang
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

ICVAE: Interpretable conditional variational autoencoder for de novo molecular design ICVAE: Interpretable conditional variational autoencoder for de novo molecular design paper code and data. Autoencoder ; Artificial intelligence ; Computer science ; Mathematics ; Deep learning Machine Learning in Materials Science
Authors: Sean Colby
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-11-20)
Field: Chemistry — Spectroscopy

Method for simultaneous characterization and expansion of reference libraries for small molecule identification A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation. Identification (biology) ; Characterization (materials science) ; Computer science ; Computational biology ; Information retrieval ; Materials science ; Nanotechnology ; Biology ; Botany Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications
Authors: Zihan Zhou | Ruiying Liu | Chaolong Ying | Ruimao Zhang | Tianshu Yu
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Molecular Conformation Generation via Shifting Scores Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecular conformation via diffusion requires learning to reverse a noising process. Diffusion on inter-atomic distances instead of conformation preserves SE(3)-equivalence and shows superior performance compared to alternative techniques, whereas related generative modelings are predominantly based upon heuristical assumptions. In response to this, we propose a novel molecular conformation generation approach driven by the observation that the disintegration of a molecule can be viewed as casting increasing force fields to its composing atoms, such that the distribution of the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann distribution. The corresponding generative modeling ensures a feasible inter-atomic distance geometry and exhibits time reversibility. Experimental results on molecular datasets demonstrate the advantages of the proposed shifting distribution compared to the state-of-the-art. Molecule ; Molecular dynamics ; Gaussian ; Equivalence (formal languages) ; Distribution (mathematics) ; Statistical physics ; Chemical physics ; Computer science ; Chemistry ; Classical mechanics ; Computational chemistry ; Physics ; Mathematics ; Quantum mechanics ; Pure mathematics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rafaela Gomes | Marina Caldeira Tonucci | Maiyara Carolyne Prete | César Ricardo Teixeira Tarley | Bruno Eduardo Baeta | Robson José de Cássia Franco Afonso
Source: Journal of the Brazilian Chemical Society (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A Hybrid Molecularly Imprinted Polymer for Selective Adsorption of Urinary S-Phenylmercapturic Acid A hybrid molecular imprinted polymer (HMIP) obtained by the organic monomer 4-vinylpyridine (4-VP) and the inorganic precursor tetraethyl orthosilicate (TEOS) was synthesized for the selective extraction of urinary S-phenylmercapturic acid (u-SPMA), a biomarker of benzene exposure. The chemical and structural characterization of the synthetic adsorbent was performed by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and textural analysis employing N2 adsorption-desorption isotherms. Selective and adsorptive performance of the imprinted polymers were evaluated by kinetic, isothermal, thermodynamic, and selectivity studies. The kinetic data were well adjusted to pseudo-second order and intraparticle diffusion models, leading to selectivity coefficient (K) value of 0.03 g mg-1 min-1 for HMIP. In addition, the adsorption isotherms were better described by the Sip model achieving a maximum adsorption capacity of 284.81 μg g-1 (at 288.15 K). Adsorption ; Thermogravimetric analysis ; Chemistry ; Molecularly imprinted polymer ; Fourier transform infrared spectroscopy ; Selectivity ; Desorption ; Polymer ; Infrared spectroscopy ; Chemical engineering ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications
Authors: Timur Garipov | Sebastiaan De Peuter | Ge Yang | Vikas Garg | Samuel Kaski | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Compositional Sculpting of Iterative Generative Processes High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composing iterative generative processes, such as GFlowNets and diffusion models, is that to realize the desired target distribution, all steps of the generative process need to be coordinated, and satisfy delicate balance conditions. In this work, we propose Compositional Sculpting: a general approach for defining compositions of iterative generative processes. We then introduce a method for sampling from these compositions built on classifier guidance. We showcase ways to accomplish compositional sculpting in both GFlowNets and diffusion models. We highlight two binary operations $\unicode{x2014}$ the harmonic mean ($p_1 \otimes p_2$) and the contrast ($p_1 \unicode{x25D1}\,p_2$) between pairs, and the generalization of these operations to multiple component distributions. We offer empirical results on image and molecular generation tasks. Computer science ; Generative grammar ; Iterative and incremental development ; Binary number ; Classifier (UML) ; Generative model ; Unicode ; Component (thermodynamics) ; Process (computing) ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Programming language ; Mathematics ; Software engineering Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Fractal and DNA sequence analysis
Authors: Jeff Guo | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design Generative molecular design has moved from proof-of-concept to real-world applicability, as marked by the surge in very recent papers reporting experimental validation. Key challenges in explainability and sample efficiency present opportunities to enhance generative design to directly optimize expensive high-fidelity oracles and provide actionable insights to domain experts. Here, we propose Beam Enumeration to exhaustively enumerate the most probable sub-sequences from language-based molecular generative models and show that molecular substructures can be extracted. When coupled with reinforcement learning, extracted substructures become meaningful, providing a source of explainability and improving sample efficiency through self-conditioned generation. Beam Enumeration is generally applicable to any language-based molecular generative model and notably further improves the performance of the recently reported Augmented Memory algorithm, which achieved the new state-of-the-art on the Practical Molecular Optimization benchmark for sample efficiency. The combined algorithm generates more high reward molecules and faster, given a fixed oracle budget. Beam Enumeration shows that improvements to explainability and sample efficiency for molecular design can be made synergistic. Oracle ; Enumeration ; Sample (material) ; Generative model ; Benchmark (surveying) ; Computer science ; Probabilistic logic ; Generative grammar ; Reinforcement learning ; Sampling (signal processing) ; Theoretical computer science ; Computer engineering ; Artificial intelligence ; Mathematics ; Programming language ; Chemistry ; Computer vision Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: C. McNamara | J. M. Rickman | Helen M. Chan | Junyan Zhang
Source: Journal of Applied Physics (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Interacting fronts in a prototypical, two-dimensional model of entropy-stabilized reactive phase formation Solid-state reactions proceeding from an initial microstructure enable the synthesis of complex materials in a wide variety of applications. However, despite the importance of such reactions in materials’ processing, the connection between an initial, reactant microstructure and the ensuing transformation kinetics has been relatively little studied. In this work, we employ computer simulation of a reaction–diffusion model and a quantitative analysis of the associated kinetic equations to examine the propagation and interaction of evolving fronts in a prototypical system transforming via a solid-state reaction. It is found that the interaction between fronts dictates transformation kinetics. We then use our results to describe the kinetics of phase formation in the Co–Ti–O system, which contains the entropy-stabilized line compound cobalt dititanate, CoTi2O5. This system is of particular interest as it has been shown that the solid-state synthesis may be exploited to produce single-crystal cobalt dititanate. Kinetics ; Microstructure ; Thermodynamics ; Materials science ; Cobalt ; Chemical kinetics ; Reaction–diffusion system ; Chemical physics ; Phase (matter) ; Solid-state ; Chemistry ; Statistical physics ; Physical chemistry ; Crystallography ; Physics ; Classical mechanics ; Metallurgy Solidification and crystal growth phenomena
Authors: Amalachukwu Ifeyinwa | Vincent Ismael Egbulefu Ajiwe | Ozioma Juliana Anekwe | Emeka Christian Ezeudu | Cecilia Nkemjika Aduaka
Source: Science Journal of Chemistry (article, 2023-03-31)
Field: Materials Science — Biomaterials

Kinetics and Thermodynamic Study of the Acetylation of &amp;lt;i&amp;gt;Cucumeropsis mannii &amp;lt;/i&amp;gt;Seed Shell The use of lignocellulosic materials for oil sorption from aqueous medium has directed attention to acetylation as a means of increasing the hydrophobicity of these materials. In this paper, acetylation of <i>Cucumeropsis mannii </i>seed shell, a readily available agricultural waste, was studied. Effects of different acetylation conditions on the extent of acetylation of the seed shell was investigated. The kinetics of the acetylation process was studied using pseudo first order, pseudo second order, and intraparticle diffusion models. The thermodynamics of the acetylation process was also studied. FTIR analysis provided evidence of successful acetylation of the seed shell. The extent of acetylation increased with increase in acetylation duration, temperature and catalyst concentration. Pseudo second order kinetics best described the acetylation process, with minimum extent of acetylation and rate constant values of 0.317 and -0.0148 min<sup>-1</sup>, respectively. Thermodynamic studies revealed that the acetylation process was endothermic in nature. The critical extent of acetylation, heat capacity at constant pressure, and change in entropy values were 1.002, 0.162 kJ.mol<sup>-1</sup>.K<sup>-1</sup>, and 0.020 kJ.mol<sup>-1</sup>.K<sup>-1</sup>, respectively. The acetylation process was spontaneous at temperatures of 328 K ‒ 348 K, but non-spontaneous at lower temperatures (308 K and 318 K). The critical temperature of acetylation was 419 K, which shows that acetylation of CMSS can easily take place at mild conditions. Acetylation ; Chemistry ; Kinetics ; Enthalpy ; Endothermic process ; Thermodynamics ; Biochemistry ; Organic chemistry ; Physics ; Adsorption biodegradable polymer synthesis and properties ; Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites
Authors: Manna Dai
Source:  (preprint, 2023-10-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design &lt;p&gt;Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery.&lt;/p&gt; Metamaterial ; Computer science ; Constraint (computer-aided design) ; Deep learning ; Electronic engineering ; Artificial intelligence ; Physics ; Optics ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Millimeter-Wave Propagation and Modeling
Authors: É. S. Mkrtchyan | A. A. Popova | И. Н. Шубин
Source: Perspektivnye Materialy (article, 2023-01-01)
Field: Materials Science — General Materials Science

Study of promising carbon sorbents obtained by high-temperature activation in the purification of aqueous solutions from dyes The paper presents the results of studies conducted to determine the sorption capacity of a highly porous carbon material obtained by implementing two activation options - high-temperature alkaline activation and activation with additional processing of the material with steam. As a result, the obtained activated highly porous carbon material had a specific surface area of 2600 – 2700 m2/g and a pore volume of more than 1.3 cm3/g. A high sorption activity was established with respect to organic dyes — methylene blue (carbonizate = 1075 mg/g; AC-1 = 1865 mg/g; AC-2 = 2010 mg/g) and sunset (carbonizate = 66 mg/g; AC-1 = 934 mg/g; AC-2 = 972 mg/g). As a result of kinetic studies, the time of onset of sorption equilibrium for the studied sorbents was determined from 15 to 30 minutes. The obtained kinetic data were processed by pseudo-first and -second order, Elovich and intraparticle diffusion models. The pseudo-second order model has the highest determination coefficients R2 (when removing methylene blue dye molecules: AC-2 R2 = 1; AC-1 R2 = 1; carbonizate R2 = 0.9999; when removing sunset dye molecules: AC-2 R2 = 1; AC-1; R2 = 0.9999; carbonizate R2 = 0.9994). It has been established that a chemical interaction occurs between the dye molecules and the functional groups of the sorbent. As a result of research, it was found that activated carbon material can be a highly effective absorber of organic pollutants from aqueous solutions. Sorption ; Sorbent ; Aqueous solution ; Activated carbon ; Chemistry ; Methylene blue ; Diffusion ; Carbon fibers ; Molecule ; Nuclear chemistry ; Chemical engineering ; Organic chemistry ; Adsorption ; Materials science ; Catalysis ; Thermodynamics Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry