jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joanna Bons | Christie L. Hunter | Rita Chupalov | Jason Causon | Alexandra Antonoplis | Jacob Rose | Brendan MacLean | Birgit Schilling
Source: Journal of the American Society for Mass Spectrometry (article, 2023-09-11)
Field: Chemistry — Spectroscopy

Localization and Quantification of Post-Translational Modifications of Proteins Using Electron Activated Dissociation Fragmentation on a Fast-Acquisition Time-of-Flight Mass Spectrometer Protein post-translational modifications (PTMs) are crucial and dynamic players in a large variety of cellular processes and signaling. Proteomic technologies have emerged as the method of choice to profile PTMs. However, these analyses remain challenging due to potential low PTM stoichiometry, the presence of multiple PTMs per proteolytic peptide, PTM site localization of isobaric peptides, and neutral losses. Collision-induced dissociation (CID) is commonly used to characterize PTMs, but the application of collision energy can lead to neutral losses and incomplete peptide sequencing for labile PTM groups. In this study, we assessed the performance of an alternative fragmentation, electron activated dissociation (EAD), to characterize, site localize, and quantify peptides with labile modifications in comparison to CID, both operated on a recently introduced fast-scanning quadrupole-time-of-flight (QqTOF) mass spectrometer. We analyzed biologically relevant phosphorylated, succinylated, malonylated, and acetylated synthetic peptides using targeted parallel reaction monitoring (PRM or MRM<sup>HR</sup>) assays. We report that electron-based fragmentation preserves the malonyl group from neutral losses. The novel tunable EAD kinetic energy maintained labile modification integrity and provided better peptide sequence coverage with strong PTM-site localization fragment ions. Activation of a novel trap-and-release technology significantly improves the duty cycle and provided significant MS/MS sensitivity gains by an average of 6-11-fold for EAD analyses. Evaluation of the quantitative EAD PRM workflows revealed high reproducibility with coefficients of variation of ∼2-7%, as well as very good linearity and quantification accuracy. This novel workflow combining EAD and trap-and-release technology provides high sensitivity, alternative fragmentation information to achieve confident PTM characterization and quantification. Chemistry ; Mass spectrometry ; Fragmentation (computing) ; Electron-transfer dissociation ; Peptide ; Ion trap ; Quadrupole ion trap ; Dissociation (chemistry) ; Electron-capture dissociation ; Collision-induced dissociation ; Analytical Chemistry (journal) ; Biophysics ; Tandem mass spectrometry ; Combinatorial chemistry ; Chromatography ; Biochemistry ; Physical chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Yeonghun Kang | Jihan Kim
Source: arXiv (Cornell University) (preprint, 2023-08-01)
Field: Materials Science — Materials Chemistry

ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4 and GPT-3.5-turbo), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid structured queries. The system is comprised of three core components (i.e. an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. The study further explores the merits and constraints of using large language models (LLMs) AI system in material sciences using and showcases its transformative potential for future advancements. Key (lock) ; Pipeline (software) ; Computer science ; Variety (cybernetics) ; Transformative learning ; Property (philosophy) ; Artificial intelligence ; Core (optical fiber) ; Programming language ; Computer security Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Srivatsan Krishnan | Amir Yazdanbakhsh | Shvetank Prakash | Jason Jabbour | Ikechukwu Uchendu | Susobhan Ghosh | Behzad Boroujerdian | Daniel Richins | Devashree Tripathy | Aleksandra Faust | Vijay Janapa Reddi
Source:  (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design Machine learning (ML) has become a prevalent approach to tame the complexity of design space exploration for domain-specific architectures. While appealing, using ML for design space exploration poses several challenges. First, it is not straightforward to identify the most suitable algorithm from an ever-increasing pool of ML methods. Second, assessing the trade-offs between performance and sample efficiency across these methods is inconclusive. Finally, the lack of a holistic framework for fair, reproducible, and objective comparison across these methods hinders the progress of adopting ML-aided architecture design space exploration and impedes creating repeatable artifacts. To mitigate these challenges, we introduce ArchGym, an open-source gymnasium and easy-to-extend framework that connects a diverse range of search algorithms to architecture simulators. To demonstrate its utility, we evaluate ArchGym across multiple vanilla and domain-specific search algorithms in the design of a custom memory controller, deep neural network accelerators, and a custom SoC for AR/VR workloads, collectively encompassing over 21K experiments. The results suggest that with an unlimited number of samples, ML algorithms are equally favorable to meet the user-defined target specification if its hyperparameters are tuned thoroughly; no one solution is necessarily better than another (e.g., reinforcement learning vs. Bayesian methods). We coin the term "hyperparameter lottery" to describe the relatively probable chance for a search algorithm to find an optimal design provided meticulously selected hyperparameters. Additionally, the ease of data collection and aggregation in ArchGym facilitates research in ML-aided architecture design space exploration. As a case study, we show this advantage by developing a proxy cost model with an RMSE of 0.61% that offers a 2,000-fold reduction in simulation time. Code and data for ArchGym is available at https://bit.ly/ArchGym. Hyperparameter ; Computer science ; Bayesian optimization ; Machine learning ; Architecture ; Reinforcement learning ; Design space exploration ; Artificial intelligence ; Domain (mathematical analysis) ; Space (punctuation) ; Computer engineering ; Embedded system Machine Learning in Materials Science ; Software Engineering Research ; Advanced Memory and Neural Computing
Authors: Siyu Tian | Zekun Ren | Selvaraj Venkataraj | Yuanhang Cheng | Daniil Bash | Felipe Oviedo | J. Senthilnath | Vijila Chellappan | Yee‐Fun Lim | Armin G. Aberle | Benjamin P. MacLeod | Fraser G. L. Parlane | Curtis P. Berlinguette | Qianxiao Li | Tonio Buonassisi | Zhe Liu
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films thicknessML predicts film thickness from reflection and transmission spectra. Transfer learning enables thickness prediction of different materials with good performance. Transfer learning also bridges the gap between simulation and experiment. Reflection (computer programming) ; Transfer of learning ; Characterization (materials science) ; Perovskite (structure) ; Spectral line ; Data transmission ; Materials science ; Scarcity ; Thin film ; Transfer (computing) ; Transmission (telecommunications) ; Computer science ; Optics ; Chemical engineering ; Nanotechnology ; Artificial intelligence ; Physics ; Engineering ; Telecommunications ; Economics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Kanji Hasegawa | Satoru Goto | Chihiro Tsunoda | Chihiro Kuroda | Yuta Okumura | Ryosuke Hiroshige | Ayako Wada-Hirai | Shota Shimizu | Hideshi Yokoyama | Tomohiro Tsuchida
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Using singular value decomposition to analyze drug/β-cyclodextrin mixtures: insights from X-ray powder diffraction patterns The article discusses the use of mathematical models and linear algebra to understand the crystalline structures and interconversion pathways of drug complexes with β-cyclodextrin (β-CD). It involved the preparation and analysis of mixtures of indomethacin, diclofenac, famotidine, and cimetidine with β-CD using techniques such as differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), and proton nuclear magnetic resonance (<sup>1</sup>H-NMR). Singular value decomposition (SVD) analysis is used to identify the presence of different polymorphs in the mixtures of these drugs and β-CD, determine interconversion pathways, and distinguish between different forms. In general, linear algebra or artificial intelligence (AI) is used to approximate the contribution of distinguishable entities to various phenomena. We expected linear algebra to completely reveal all eight entities present in the diffractogram dataset. However, after performing the SVD procedure, we found that only six independent basis functions were extracted, and the entities of the INM α-form and the CIM B-form were not included. It is considered that this is due to that data processing is limited to revealing only six or seven independent factors, as it is a small world. The authors caution that these may not always reproduce or approach reality in complicated real-world situations. Singular value decomposition ; Differential scanning calorimetry ; Powder diffraction ; Famotidine ; Cyclodextrin ; Linear algebra ; Decomposition ; Pure mathematics ; Chemistry ; Materials science ; Mathematics ; Crystallography ; Physics ; Thermodynamics ; Organic chemistry ; Algorithm ; Geometry X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: K. E. Rady | Asmaa Reda Abd El-Salam | Ezzat A. ELFadaly | M. H. Aly
Source: Applied Physics A (article, 2023-03-08)
Field: Materials Science — Materials Chemistry

Role of Mn2+ ion in the optimization of the structural and dielectric properties of Co–Zn ferrite Abstract Mn-substituted Co–Zn ferrite nanomaterials with the general form Co 0.8−x Mn x Zn 0.2 Fe 2 O 4 (x = 0.0, 0.1, 0.2, and 0.3) were prepared using the coprecipitation method. Based on X-ray diffraction, it can be confirmed that all samples have a single-phase cubic structure with an average crystallite size ranging from 23.46 to 32.66 nm. In addition, the lattice parameter increased from 8.32 to 8.37 Å. In this study, the theoretical density was calculated, and the Fourier-transform infrared spectra of the prepared samples were investigated. Further, the cation distribution of the system was estimated. The proposed cation distribution was confirmed by calculating the theoretical lattice parameter and comparing it with the experimental lattice parameter. We conducted transmission electron microscopy to confirm the obtained particle size. At room temperature, the electrical measurements of the prepared samples were performed using the LCR circuit as a function of frequency up to 5 MHz. In this study, we obtained enhanced dielectric properties by substituting Mn ions in the Co–Zn ferrite. As the Mn concentration increases, the AC resistivity of the samples increases. Consequently, the dielectric loss decreases, and its minimum value can be observed at x = 0.2, making the Co–Mn–Zn ferrite suitable for applications in the microwave frequency range. The results show that all dielectric properties exhibit a normal behavior with frequency. We obtained an improvement in the dielectric properties of the prepared samples, making them suitable for use in high-frequency applications due to the substitution by Mn ions. Crystallite ; Analytical Chemistry (journal) ; Ferrite (magnet) ; Coprecipitation ; Dielectric ; Materials science ; Lattice constant ; Ion ; Dielectric loss ; Diffraction ; Chemistry ; Inorganic chemistry ; Optics ; Metallurgy Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Multiferroics and related materials
Authors: Siwei Pan | Qing Hu | Yaohong Zhao | Qing Wang | Yuanyuan Li | Yihua Qian | Chunqing He
Source: Materials (article, 2023-12-05)
Field: Materials Science — Surfaces, Coatings and Films

Fabrication of a Fluorocarbon Low Surface Energy Coating for Anti-Stain Applications In the long-term working state, stains such as dust, oil, and charged particles in the environment are prone to deposit on the surface of the power equipment, which has great security risks. To achieve anti-stain performance, fluorocarbon composite coating with a low surface energy was prepared and studied. In this paper, SiO<sub>2</sub> nanoparticles were used as inorganic fillers and fluorocarbon resin was used as the substrate to form anti-stain coatings. By adjusting and optimizing the ratio of fillers and organic resins, coatings with different static contact angles were constructed. The optimum composite coating has a contact angle of 151 ± 2° and a surface energy of 9.6 mJ/m<sup>2</sup>. After high-temperature treatment (up to 200 °C), immersion in corrosive solutions (pH 3-11), and sandpaper abrasion (after 5 abrasion cycles), the coating has been proven to show good thermal, chemical and mechanical stability. Our study provides significant research and market opportunities for the anti-stain application of the fluorocarbon composite coating on power equipment. Sandpaper ; Materials science ; Fluorocarbon ; Contact angle ; Coating ; Abrasion (mechanical) ; Composite material ; Surface energy ; Composite number ; Fabrication ; Thermal stability ; Chemical engineering Surface Modification and Superhydrophobicity ; Polymer Nanocomposite Synthesis and Irradiation ; Silicone and Siloxane Chemistry
Authors: Wei Wang | Fan Yang | Yunlai Lu | Luo Zhi | Fangya Li | You Wu | Jianbing Zhang | Zufeng Xiao | Wei Li | Caiqin Qin
Source: Coatings (article, 2023-05-16)
Field: Materials Science — Polymers and Plastics

Application of Magnesium Hydroxide/Diphenoxy Phosphate in Silicone Rubber Flame Retardant Cable Material Deketoxime–type room–temperature vulcanized silicone rubber cable materials were prepared using α, ω–dihydroxy polydimethylsiloxane, carbon black, calcium carbonate, magnesium hydroxide, piperazine bis (diphenoxy phosphate) salt (PBDP), and melamine diphenoxy phosphate (MDP). The effects of carbon black and flame retardants on the mechanical properties, flame–retardant properties, and electrical insulation properties of silicone cable coatings were investigated. The research results showed that the products obtained had good mechanical and electrical insulation properties, with tensile strength greater than 3.0 MPa, dielectric strength greater than 22 kV/mm, and volume resistivity higher than 6.5 × 1014 Ω·cm. When 30 parts of Mg(OH)2:MDP = 2:1 are added to 100 parts of resin, the flame–retardant performance of wire and cable materials can be significantly improved. Under the thermal radiation illumination of 50 kW/m2, the ignition time (TTI) of the Mg(OH)2/MDP coating increased by 16 s, and the maximum heat release rate (pkHRR) and total heat release rate (THR) decreased by 29.7% and 68.8%, respectively, compared with silicone rubber without flame retardant. The silicone rubber coatings prepared were flame retardant up to the FV–1 level. Fire retardant ; Materials science ; Composite material ; Vulcanization ; Ultimate tensile strength ; Magnesium ; Carbon black ; Silicone rubber ; Flammability ; Natural rubber ; Metallurgy Flame retardant materials and properties ; Silicone and Siloxane Chemistry ; High voltage insulation and dielectric phenomena
Authors: Christophe Mendibide | Flavien Vucko | Mario J. Martinez | Gaurav R. Joshi | Jean Kittel
Source: International Journal of Hydrogen Energy (article, 2023-06-15)
Field: Materials Science — Metals and Alloys

Effect of degraded environmental conditions on the service behavior of a X65 pipeline steel not designed for hydrogen transport With the international drive to deploy green energies and decarbonized intermediates in the place of fossil fuel sources, a large number of developed countries are actively preparing for a future where hydrogen plays a strategic role as an energy storage medium. Producing and using hydrogen requires the rapid expansion of a dedicated, economically viable industrial sector. Nevertheless, questions on how to safely store, transport and distribute hydrogen remain an important priority today. In countries with existing natural gas transport grids, the possibility to retrofit these networks to store and transport hydrogen-natural gas blends is being studied. A key challenge is to evaluate how pressurized H2 would interact with steel structures with regards structural embrittlement of the latter, with a view to exploiting existing transport infrastructures for storage and transport applications. In this work, we evaluate the H2-performance of a non-hydrogen service ×65 pipeline steel. The cracking susceptibility of this steel grade has been evaluated at 100 bar H2 using slow strain rate testing, Constant strain testing and fracture toughness measurements. Accompanying hydrogen permeation tests under pressure provide diffusion data and elucidate the discussion. Exposures were carried out in dry or wet H2 and with or without H2S contamination at levels representative of biogas. The results underline that the impact of dry or wet hydrogen on this grade are moderate. The presence of traces of H2S together with humidity could risk seriously degrading the mechanical performance of the ×65 steel grade. Hydrogen embrittlement ; Hydrogen ; Natural gas ; Cracking ; Environmental science ; Fossil fuel ; Biogas ; Materials science ; Hydrogen fuel ; Process engineering ; Nuclear engineering ; Waste management ; Chemistry ; Composite material ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Corrosion Behavior and Inhibition
Authors: Wei-Feng Sun | Wen Kwang Chern | John Chok You Chan | Zhong Chen
Source: Polymers (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

A Reactive Molecular Dynamics Study on Crosslinked Epoxy Resin Decomposition under High Electric Field and Thermal Aging Conditions To reveal the microscopic mechanism of synergetic thermal-electrical degradation during a partial discharge process in epoxy insulation materials, the decomposition of crosslinked epoxy resin is investigated using reactive molecular dynamics simulations under high electric field and thermal degradation conditions. Bond-boost acceleration method is employed in reactive molecular dynamics simulations to successfully establish epoxy polymer models with a crosslink degree of 93%. Active molecular species derived from electrical partial discharges are considered in the current work. Small molecule products and decomposition temperature in the degradation process under an electric field are calculated to elucidate the effect of nitric acid and ozone molecules, being the active products generated by electrical partial discharges, on the synergetic thermal-electrical degradation of epoxy resin. Both nitric acid and ozone exacerbate thermal impact decomposition of crosslinked epoxy polymer by decreasing initial decomposition temperature from 1050 K to 940 K and 820 K, respectively. It is found that these active products can oxidize hydroxyl groups and carbon-nitrogen bridge bonds in epoxy molecular chains, leading to the aggravation of epoxy resin decomposition, as manifested by the significant increase in the decomposed molecular products. In contrast, thermal degradation of the epoxy resin without the active species is not expedited by increasing electric field. These strongly oxidative molecules are easily reduced to negative ions and able to obtain kinetic energies from electric field, which result in chemical corrosion and local temperature increase to accelerate decomposition of epoxy insulation materials. Epoxy ; Thermal decomposition ; Materials science ; Composite material ; Decomposition ; Electric field ; Polymer ; Chemical engineering ; Chemistry ; Organic chemistry High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Dielectric materials and actuators
Authors: Shulin Wang | Yujing Ji | Jiangtao Liu | Zhimeng Liu | Xiaowei Zhang | Yujie Guo | Jing Lin | Jinzhang Tao | Jitti Kasemchainan | Yu Jiang | Hongyi Gao
Source: Journal of Energy Storage (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Integrating crystal structure and numerical data for predictive models of lithium-ion battery materials: A modified crystal graph convolutional neural networks approach Convolutional neural network ; Computer science ; Artificial neural network ; Machine learning ; Crystal (programming language) ; Artificial intelligence ; Test set ; Flexibility (engineering) ; Deep learning ; Data mining ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Shuhao Zhang | Małgorzata Z. Makoś | Ryan B. Jadrich | Elfi Kraka | Kipton Barros | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev | Nicholas Lubbers | Richard A. Messerly | Justin S. Smith
Source: ChemRxiv (preprint, 2023-04-16)
Field: Materials Science — Materials Chemistry

Exploring the frontiers of chemistry with a generalreactive machine learning potential Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, developing reactive MLIPs requires prior knowledge of reaction networks to generate fitting data and refitting to extensive datasets for each new application. In this work, we develop a general reactive MLIP through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting potential (ANI-1xnr) is then applied to study five distinct condensed phase reactive chemistry systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early-earth small molecules. In all studies, ANI-1xnr closely matches experiment and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP that does not need to be refit for each application, enabling high-throughput in silico reactive chemistry experimentation. Reactive intermediate ; Nanoreactor ; Chemistry ; Molecular machine ; Quantum chemistry ; Nanotechnology ; Molecular dynamics ; Computer science ; Biochemical engineering ; Computational chemistry ; Nanoparticle ; Reaction mechanism ; Materials science ; Organic chemistry ; Catalysis ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research