jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Harshad M. Paranjape
Source: Shape Memory and Superelasticity (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Advancing Nitinol Implant Design and Simulation Through Data-Driven Methodologies Abstract Recent advances in the Data Science methods for acquiring and analyzing large amounts of materials deformation data have the potential to tremendously benefit Nitinol (Nickel–Titanium shape memory alloy) implant design and simulation. We review some of these data-driven methodologies and provide a perspective on adapting these techniques to Nitinol design and simulation. We organize the review in a three-tiered approach. The methods in the first tier relate to data acquisition. We review methods for acquiring full-field deformation data from implants and methods for quantifying uncertainty in such data. The second-tier methods relate to combining data from multiple sources to gain a holistic understanding of complex deformation phenomena such as fatigue. Methods in the third tier relate to making data-driven simulation of the deformation response of Nitinol. A wide adaption of these methods by the Nitinol cardiovascular implant community may be facilitated by building consensus on best practices and open exchange of computational tools. Computer science ; Deformation (meteorology) ; Perspective (graphical) ; Field (mathematics) ; Experimental data ; Materials science ; Artificial intelligence Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties
Authors: Sirui Bi | Victor S.C. Fung | Jiaxin Zhang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties. Deep generative models are recently proposed to solve inverse problems, but these are currently struggling in expensive forward operators, precisely localizing the exact solutions and fully exploring the parameter spaces without missing solutions. In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent. Given a target property, the learned invertible model provides a posterior over the parameter space; we identify these posterior samples as an intelligent prior initialization which enables us to narrow down the search space. We then perform gradient descent to calibrate the inverse solutions within a local region. Meanwhile, a space-filling sampling is imposed on the latent space to better explore and capture all possible solutions. We evaluate our approach on three benchmark tasks and create two datasets of real-world applications from quantum chemistry and additive manufacturing and find our method achieves superior performance compared to several state-of-the-art baseline methods. The iPage code is available at https://github.com/jxzhangjhu/MatDesINNe. Computer science ; Gradient descent ; Initialization ; Benchmark (surveying) ; Inference ; Invertible matrix ; Bayesian optimization ; Inverse problem ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Algorithm ; Theoretical computer science ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Gaussian Processes and Bayesian Inference
Authors: Mahmut Gür | Merve Zurnacı | Eda Altınöz | Nesrin Şener | Çiğdem Şahın | Merve Şenturan | İzzet Şener | M. Serdar Çavuş | Ergin Murat Altuner
Source: Hittite Journal of Science & Engineering (article, 2023-06-30)
Field: Chemistry — Organic Chemistry

Novel 1,3,4-Thiadiazole Derivatives as Antibiofilm, Antimicrobial, Efflux Pump Inhibiting Agents and Their ADMET Characterizations In this study, 1,3,4-thiadiazole derivatives were obtained from the reaction of benzophenone-4,4'-dicarboxylic acid and N-substitute-thiosemicarbazide compounds with each other. After the synthesis of the final products, some biological properties of these compounds such as antibiofilm, antimicrobial and efflux pump inhibiting efficiencies were evaluated. According to the MBC/MFC test, all the activities were found to be bacteriostatic, also, especially the biofilm inhibition activity of C1 against K. pneumoniae is noteworthy. In addition, C4 was observed to exhibit efflux pump inhibition activity in E. coli, whereas C2 and C3 in K. pneumoniae. The absorption and emission values of the molecules were obtained and electrochemical studies were performed. In addition; absorption, metabolism, distribution, excretion and toxicity (ADMET) scores were predicted using the pharmacokinetic properties of all 1,3,4-thiadiazole compounds. Finally, the electrochemical stabilities of the synthesized molecules have been analyzed by using cyclic voltammetry in 0.1 M TBAPF6 in DMSO as a supporting electrolyte. Efflux ; Chemistry ; Antimicrobial ; Electrochemistry ; Absorption (acoustics) ; Benzophenone ; Combinatorial chemistry ; Biofilm ; Electrolyte ; Cyclic voltammetry ; Nuclear chemistry ; Organic chemistry ; Bacteria ; Biochemistry ; Electrode ; Materials science ; Biology Synthesis and biological activity ; Phenothiazines and Benzothiazines Synthesis and Activities ; Click Chemistry and Applications
Authors: Megan McCarthy | Jacob Startt | Rémi Dingreville | Aidan P. Thompson | Mitchell Wood
Source: arXiv (Cornell University) (preprint, 2023-03-08)
Field: Materials Science — Materials Chemistry

Atomic Representations of Local and Global Chemistry in Complex Alloys The exceptional properties observed in complex concentrated alloys (CCAs) arise from the interplay between crystalline order and chemical disorder at the atomic scale, complicating a unique determination of properties. In contrast to conventional alloys, CCA properties emerge as distributions due to varying local chemical environments and the specific scale of measurement. Currently there are few ways to quantitatively define, track, and compare local alloy compositions (versus a global label, i.e. equiatomic) contained in a CCA. Molecular dynamics is used here to build descriptive metrics that connect a global alloy composition to the diverse local alloy compositions that define it. A machine-learned interatomic potential for MoNbTaTi is developed and we use these metrics to investigate how property distributions change with excursions in global-local composition space. Short-range order is examined through the lens of local chemistry for the equiatomic composition, demonstrating stark changes in vacancy formation energy with local chemistry evolution. Alloy ; Molecular dynamics ; Atomic units ; Space (punctuation) ; Scale (ratio) ; Chemical physics ; Chemical composition ; Order (exchange) ; Composition (language) ; Statistical physics ; Chemistry ; Materials science ; Physics ; Computer science ; Computational chemistry ; Thermodynamics ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; nanoparticles nucleation surface interactions
Authors: Wei Tan | Boyuan Gou | Xiangdong Ding | Jun Sun | Ekhard K. H. Salje
Source: Physical Review Research (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Wild avalanches and mild fluctuations during filiform corrosion of Mg in NaCl solution The filiform corrosion of Mg in NaCl solution was characterized by acoustic emission spectroscopy, measurements of the free-corrosion potential, and optical observations under in situ conditions. Three stages of corrosion were identified: an incubation period, localized attack, and filament propagation. The corrosion mechanisms include the Mg dissolution, the breakdown of hydroxide/oxide surface films, and the burst of gas bubbles. Localized attack and filament propagation are distinguished by a gradual transition from correlated avalanches (wild events) to predominantly stochastic processes (mild events), which still contain some weak avalanche signals. Our approach provides a way to monitor corrosion under in situ conditions. Corrosion ; Protein filament ; Dissolution ; Oxide ; Hydroxide ; Materials science ; In situ ; Chemical physics ; Chemistry ; Metallurgy ; Composite material ; Inorganic chemistry ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: P. V. Stishenko | Thomas W. Keal | Scott M. Woodley | Volker Blüm | B. Hourahine | Reinhard J. Maurer | Andrew J. Logsdail
Source: The Journal of Open Source Software (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Atomic Simulation Interface (ASI): applicationprogramming interface for electronic structure codes Stishenko et al., (2023). Atomic Simulation Interface (ASI): application programming interface for electronic structure codes. Journal of Open Source Software, 8(85), 5186, https://doi.org/10.21105/joss.05186 Interface (matter) ; Computer science ; Computational science ; Software ; Application programming interface ; Electronic structure ; Graphical user interface ; Programming language ; Operating system ; Physics ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ion-surface interactions and analysis
Authors: Martin Šípka | Johannes C. B. Dietschreit | Grajciar, Lukáš | Gómez-Bombarelli, Rafael
Source: arXiv (Cornell University) (preprint, 2023-01-09)
Field: Materials Science — Materials Chemistry

Differentiable Simulations for Enhanced Sampling of Rare Events Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs). We propose using differentiable simulations (DiffSim) for the discovery and enhanced sampling of chemical transformations without a need to resort to preselected CVs, using only a distance metric. Reaction path discovery and estimation of the biasing potential that enhances the sampling are merged into a single end-to-end problem that is solved by path-integral optimization. This is achieved by introducing multiple improvements over standard DiffSim such as partial backpropagation and graph mini-batching making DiffSim training stable and efficient. The potential of DiffSim is demonstrated in the successful discovery of transition paths for the Muller-Brown model potential as well as a benchmark chemical system - alanine dipeptide. Differentiable function ; Benchmark (surveying) ; Computer science ; Metric (unit) ; Sampling (signal processing) ; Path (computing) ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Machine Learning and Algorithms
Authors: Yuji Hamamoto | Thanh Ngoc Pham | Malthe Kjær Bisbo | Bjørk Hammer | Yoshitada Morikawa
Source: Physical Review Materials (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Machine-learned search for the stable structures of silicene on Ag(111) The honeycomb lattice of silicene exhibits a variety of nontrivial reconstructions on the Ag(111) surface, whose diversity hampers the theoretical prediction of experimentally unidentified structures using computationally expensive density functional theory (DFT) calculations. We here apply an efficient method based on an evolutionary algorithm and a Gaussian process, which is trained on the fly with DFT calculations, to the search for the stable structures of silicene on Ag(111). We demonstrate that the structure search method can not only reproduce the well-known structures, but also predict the existence of metastable structures that are close in stability to the most stable ones. Detailed analyses of the obtained results reveal that such metastable structures play crucial roles in the stabilization of less ordered phases often observed experimentally. The present method can replace the conventional manual search based on intuition and is widely applicable to the investigations of new systems such as emerging two-dimensional materials. Silicene ; Materials science ; Metastability ; Density functional theory ; Intuition ; Lattice (music) ; Gaussian ; Statistical physics ; Chemical physics ; Nanotechnology ; Physics ; Graphene ; Quantum mechanics Graphene research and applications ; Topological Materials and Phenomena ; Quantum many-body systems
Authors: Fan Chen | Muhammad Nadeem Akram | Xuyuan Chen
Source: Molecules (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Novel Red-Emitting Eu3+-Doped Y2(WxMo1−xO4)3 Phosphor with High Conversion Efficiency for Lighting and Display Applications In this study, a series of trivalent europium-doped tungstate and molybdate samples were synthesized using an improved sol-gel and high-temperature solid-state reaction method. The samples had different W/Mo ratios and were calcined at various temperatures ranging from 800 to 1000 °C. The effects of these variables on the crystal structure and photoluminescence characteristics of the samples were investigated. It was found that a doping concentration of 50% for europium yielded the best quantum efficiency based on previous research. The crystal structures were found to be dependent on the W/Mo ratio and calcination temperature. Samples with x ≤ 0.5 had a monoclinic lattice structure that did not change with calcination temperature. Samples with x > 0.75 had a tetragonal structure that remained unchanged with calcination temperature. However, samples with x = 0.75 had their crystal structure solely dependent on the calcination temperature. At 800-900 °C, the crystal structure was tetragonal, while at 1000 °C, it was monoclinic. Photoluminescence behavior was found to correlate with crystal structure and grain size. The tetragonal structure had significantly higher internal quantum efficiency than the monoclinic structure, and smaller grain size had higher internal quantum efficiency than larger grain size. External quantum efficiency initially increased with increasing grain size and then decreased. The highest external quantum efficiency was observed at a calcination temperature of 900 °C. These findings provide insight into the factors affecting the crystal structure and photoluminescence behavior in trivalent europium-doped tungstate and molybdate systems. Tetragonal crystal system ; Photoluminescence ; Monoclinic crystal system ; Calcination ; Materials science ; Tungstate ; Europium ; Crystal structure ; Quantum efficiency ; Phosphor ; Doping ; Analytical Chemistry (journal) ; Crystallography ; Grain size ; Luminescence ; Mineralogy ; Chemistry ; Optoelectronics ; Metallurgy ; Organic chemistry Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Perovskite Materials and Applications
Authors: Kizashi Yamaguchi | Hiroshi Isobe | Mitsuo Shoji | Takashi Kawakami | Koichi Miyagawa
Source: Molecules (review, 2023-10-16)
Field: Chemistry — Inorganic Chemistry

The Nature of the Chemical Bonds of High-Valent Transition–Metal Oxo (M=O) and Peroxo (MOO) Compounds: A Historical Perspective of the Metal Oxyl–Radical Character by the Classical to Quantum Computations This review article describes a historical perspective of elucidation of the nature of the chemical bonds of the high-valent transition metal oxo (M=O) and peroxo (M-O-O) compounds in chemistry and biology. The basic concepts and theoretical backgrounds of the broken-symmetry (BS) method are revisited to explain orbital symmetry conservation and orbital symmetry breaking for the theoretical characterization of four different mechanisms of chemical reactions. Beyond BS methods using the natural orbitals (UNO) of the BS solutions, such as UNO CI (CC), are also revisited for the elucidation of the scope and applicability of the BS methods. Several chemical indices have been derived as the conceptual bridges between the BS and beyond BS methods. The BS molecular orbital models have been employed to explain the metal oxyl-radical character of the M=O and M-O-O bonds, which respond to their radical reactivity. The isolobal and isospin analogy between carbonyl oxide R<sub>2</sub>C-O-O and metal peroxide LFe-O-O has been applied to understand and explain the chameleonic chemical reactivity of these compounds. The isolobal and isospin analogy among Fe=O, O=O, and O have also provided the triplet atomic oxygen (<sup>3</sup>O) model for non-heme Fe(IV)=O species with strong radical reactivity. The chameleonic reactivity of the compounds I (Cpd I) and II (Cpd II) is also explained by this analogy. The early proposals obtained by these theoretical models have been examined based on recent computational results by hybrid DFT (UHDFT), DLPNO CCSD(T<sub>0</sub>), CASPT2, and UNO CI (CC) methods and quantum computing (QC). Reactivity (psychology) ; Natural bond orbital ; Chemistry ; Computational chemistry ; Atomic orbital ; Chemical bond ; Transition metal ; Analogy ; Chemical physics ; Density functional theory ; Physics ; Organic chemistry ; Quantum mechanics ; Catalysis Metal-Catalyzed Oxygenation Mechanisms ; Free Radicals and Antioxidants ; Porphyrin and Phthalocyanine Chemistry
Authors: Carmellina Daniela Bădiceanu | Cătălina Mareş | Diana Camelia Nuță | Speranța Avram | Constantin Drăghici | Ana-Maria Udrea | Irina Zarafu | Cornel Chiriţă | Marilena Viorica Hovaneț | Carmen Limban
Source: Processes (article, 2023-05-25)
Field: Chemistry — Organic Chemistry

N-Substituted (Hexahydro)-1H-isoindole-1,3(2H)-dione Derivatives: New Insights into Synthesis and Characterization Novel phthalimide derivatives, namely N-(1,3-dioxoisoindolin-2-yl)-2-(2-methyl-4-oxoquinazolin-3(4H)-yl)acetamide (1a) and N-(1,3-dioxoisoindolin-2-yl)thiophene-2-carboxamide (1b), and hexahydrophthalimide derivative N-(1,3-dioxohexahydro-1H-isoindol-2(3H)-yl)-2-(2-methyl-4-oxoquinazolin-3(4H)-yl)acetamide (2), have been synthesized. The phthalimide derivatives were synthesized from phthalic anhydride and 2-(2-methyl-4-oxoquinazolin-3(4H)-yl)acetohydrazide or thiophene-2-carbohydrazide, and the hexahydrophthalimide derivative has been synthesized from hexahydrophthalic anhydride and 2-(2-methyl-4-oxoquinazolin-3(4H)-yl)acetohydrazide. The chemical structures of the compounds are elucidated by Nuclear Magnetic Resonance (NMR) and Infrared (IR) spectra. The new in vitro antioxidant activities of the obtained substances were evaluated using the DPPH method. All tested compounds showed antioxidative activity, the most active compound being 1b. Bioinformatics tools were used for the prediction of pharmacokinetics and pharmacodynamics profiles. Our results showedthat all compounds have a suitable intestinal absorption rate, good BBB and CNS permeabilities and have as molecular targets MAO B, COX-2 and NF-KB, important for antioxidant activities. Acetamide ; Phthalimide ; Phthalic anhydride ; Chemistry ; Phthalimides ; Thiophene ; Organic chemistry ; Stereochemistry ; Medicinal chemistry ; Catalysis Quinazolinone synthesis and applications ; Synthesis and biological activity ; Cancer therapeutics and mechanisms
Authors: Viktor Zaverkin | David Holzmüller | Henrik Christiansen | Federico Errica | Francesco Alesiani | Makoto Takamoto | Mathias Niepert | Johannes Kästner
Source: Research Square (preprint, 2023-11-13)
Field: Materials Science — Materials Chemistry

Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials <title>Abstract</title> Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning (AL), which uses either biased or unbiased molecular dynamics (MD) simulations to generate candidate pools, aims to address this objective. Existing biased and unbiased MD simulations, however, are prone to miss either rare events or extrapolative regions—areas of the configurational space where unreliable predictions are made. Simultaneously exploring both regions is necessary for developing uniformly accurate MLIPs. In this work, we demonstrate that MD simulations, when biased by the MLIP’s energy uncertainty, effectively capture extrapolative regions and rare events without the need to know a priori the system’s transition temperatures and pressures. Exploiting automatic differentiation, we enhance bias-forces-driven MD simulations by introducing the concept of bias stress. We also employ calibrated ensemble-free uncertainties derived from sketched gradient features to yield MLIPs with similar or better accuracy than ensemble-based uncertainty methods at a lower computational cost. We use the proposed uncertainty-driven AL approach to develop MLIPs for two benchmark systems: alanine dipeptide and MIL-53(Al). Compared to MLIPs trained with conventional MD simulations, MLIPs trained with the proposed data-generation method more accurately represent the relevant configurational space for both atomic systems. Molecular dynamics ; Computer science ; Benchmark (surveying) ; A priori and a posteriori ; Set (abstract data type) ; Space (punctuation) ; Uncertainty quantification ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics