jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Landers | Ian Clancy | Rafal E. Dunin–Borkowski | Dieter Weber | Andrew Stewart
Source: Ultramicroscopy (article, 2023-04-05)
Field: Materials Science — Surfaces, Coatings and Films

TEMGYM Advanced: Software for electron lens aberrations and parallelised electron ray tracing Characterisation of the electron beams trajectory in an electron microscope is possible in a few select commercial software packages, but these tools and their source code are not available in a free and accessible manner. This paper introduces the free and open-source software TEMGYM Advanced, which implements ray tracing methods that calculate the path of electrons through a magnetic or electrostatic lens and allow evaluation of the first-order properties and third-order geometric aberrations. Validation of the aberration coefficient calculations is performed by implementing two independent methods - the aberration integral and differential algebra (DA) methods and by comparing the results of each. This paper also demonstrates parallelised electron ray tracing through a series of magnetic components, which enables near real-time generation of a physically accurate beam-spot including aberrations and brings closer the realisation of a digital twin of an electron microscope. TEMGYM Advanced represents a valuable resource for the electron microscopy community, providing an accessible and open source means of characterising electron lenses. This software utilises the Python programming language to complement the growing ecosystem of free and open-source software within the electron microscopy community, and to facilitate the application of machine learning to an electron microscope digital twin for instrument automation. The software is available under GNU Public License number Three (GPL 3). Tracing ; Computer science ; Software ; Ray tracing (physics) ; Electron microscope ; Optics ; Lens (geology) ; Electrostatic lens ; Microscope ; Computational science ; Computer hardware ; Physics ; Operating system ; Beam (structure) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Engy El-Shafeiy | Amr A. Abohany | Wael M. Elmessery | Amr A. Abd El-Mageed
Source: Neural Computing and Applications (article, 2023-06-29)
Field: Chemistry — Inorganic Chemistry

Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization Abstract Coconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water. Artificial neural network ; Computer science ; Heuristic ; Artificial intelligence ; Fuzzy logic ; Machine learning Coconut Research and Applications
Authors: Prabudhya Bhattacharyya | Wuhao Chen | Xiaoli Huang | Shubhayu Chatterjee | Benchen Huang | Bryce Kobrin | Yuanqi Lyu | Thomas J. Smart | Maxwell Block | Esther Wang | Zhipan Wang | Weijie Wu | Satcher Hsieh | He Ma | Srinivas V. Mandyam | Bijuan Chen | Emily J. Davis | Zachary M. Geballe | Chong Zu | Viktor V. Struzhkin | Raymond Jeanloz | Joel E. Moore | Tian Cui | Giulia Galli | Bertrand I. Halperin | Chris R. Laumann | Norman Y. Yao
Source: arXiv (Cornell University) (preprint, 2023-06-05)
Field: Materials Science — Materials Chemistry

Imaging the Meissner effect and flux trapping in a hydride superconductor at megabar pressures using a nanoscale quantum sensor By directly altering microscopic interactions, pressure provides a powerful tuning knob for the exploration of condensed phases and geophysical phenomena. The megabar regime represents an exciting frontier, where recent discoveries include novel high-temperature superconductors, as well as structural and valence phase transitions. However, at such high pressures, many conventional measurement techniques fail. Here, we demonstrate the ability to perform local magnetometry inside of a diamond anvil cell with sub-micron spatial resolution at megabar pressures. Our approach utilizes a shallow layer of Nitrogen-Vacancy (NV) color centers implanted directly within the anvil; crucially, we choose a crystal cut compatible with the intrinsic symmetries of the NV center to enable functionality at megabar pressures. We apply our technique to characterize a recently discovered hydride superconductor, CeH$_9$. By performing simultaneous magnetometry and electrical transport measurements, we observe the dual signatures of superconductivity: local diamagnetism characteristic of the Meissner effect and a sharp drop of the resistance to near zero. By locally mapping the Meissner effect and flux trapping, we directly image the geometry of superconducting regions, revealing significant inhomogeneities at the micron scale. Our work brings quantum sensing to the megabar frontier and enables the closed loop optimization of superhydride materials synthesis. Superconductivity ; Meissner effect ; Diamagnetism ; Diamond anvil cell ; Condensed matter physics ; Gradiometer ; Diamond ; Materials science ; Nanotechnology ; Magnetometer ; Physics ; Magnetic field ; Optics ; Diffraction Diamond and Carbon-based Materials Research ; Quantum, superfluid, helium dynamics ; High-pressure geophysics and materials
Authors: Gregory S. Doerk | Kevin G. Yager
Source: Physical Review Materials (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Diversifying self-assembled phases in block copolymer thin films via blending Blending block copolymers with homopolymers and other block copolymers provides control over self-assembly kinetics, and unlocks a diversity of non-native morphologies. The authors review this emerging paradigm, focusing on the thin film regime, providing examples of enhanced ordering kinetics, control of morphology orientation, and even the formation of non-native structures that do not appear in the bulk equilibrium phase diagram. Copolymer ; Materials science ; Phase diagram ; Kinetics ; Morphology (biology) ; Phase (matter) ; Block (permutation group theory) ; Chemical physics ; Self-assembly ; Thin film ; Nanotechnology ; Chemical engineering ; Polymer science ; Composite material ; Polymer ; Organic chemistry ; Geometry Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Aaron L. Liu | Myeongyeon Lee | Rahul Venkatesh | Jessica A. Bonsu | Ron Volkovinsky | J. Carson Meredith | Elsa Reichmanis | Martha A. Grover
Source: Chemistry of Materials (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community. Computer science ; Ontology ; Electronics ; Organic electronics ; Process (computing) ; Transistor ; Electrical engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Analytical Chemistry and Sensors
Authors: Sterling G. Baird | Jason R. Hall | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and expertise. So, are the possible improvements in efficiency worth the cost of implementation? We demonstrate that the compactness of a search space (to what extent and how degenerate solutions and non-solutions are removed) affects Bayesian optimization search efficiency. Here, we use the Adaptive Experimentation (Ax) Platform by Meta and a physics-based particle packing simulation with eight or nine tunable parameters, depending on the search space compactness. These parameters represent three truncated log-normal distributions of particle sizes which exhibit compositional-invariance and permutation-invariance characteristic of formulation problems (e.g., chemical formulas, composite materials, alloys). We assess a total of four search space types which range from none up to both constraint types imposed simultaneously. In general, the removal of degeneracy through problem reformulation (as seen by the optimizers surrogate model) improves optimization efficiency. We recommend that optimization practitioners in the physical sciences carefully consider the trade-off between implementation cost and search efficiency before running expensive optimization campaigns. Degeneracy (biology) ; Curse of dimensionality ; Compact space ; Bayesian optimization ; Mathematical optimization ; Optimization problem ; Degenerate energy levels ; Homogeneous space ; Invariant (physics) ; Computer science ; Embedding ; Mathematics ; Algorithm ; Physics ; Artificial intelligence ; Pure mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Data Classification
Authors: Vinod G. Landge | Ankita Mishra | Waruna Thotamune | Audrey L. Bonds | Indunil Alahakoon | Ajith Karunarathne | Michael C. Young
Source: Chem Catalysis (article, 2023-11-01)
Field: Chemistry — Organic Chemistry

Selective C–H activation of unprotected allylamines by control of catalyst speciation Chemistry ; Catalysis ; Heck reaction ; Amine gas treating ; Combinatorial chemistry ; Palladium ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Synthesis and Catalytic Reactions
Authors: Derek van Tilborg | Francesca Grisoni
Source: ChemRxiv (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

Traversing Chemical Space with Active Deep Learning Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active learning is poised to be a solution for drug discovery in low-data regimes. In active learning, a model is updated iteratively by taking multiple smaller screening steps, instead of suggesting many molecules at once with a single model for traditional ‘one-shot’ screening. This iterative approach aims to improve models during the screening process and can adjust course along the way. Despite pioneering applications, it is currently unclear how active learning holds up to traditional approaches and what the best strategies are for prospective drug discovery applications. In this study, we combine six active learning strategies with two distinct deep learning methods and analyse their performance on three large-scale libraries. For each strategy, we investigate its capacity to discover bioactive hits and explore the chemical space. We found that active learning can achieve up to a six-fold improvement in hit retrieval compared to traditional methods in a low-data scenario. The strategy to acquire new molecules at each cycle resulted to be the primary driver of performance, and it determined the ‘molecular journey’ throughout screening cycles. Finally, we show that active learning can quickly compensate for a lack of molecular diversity in the starting set. These results advocate for the adoption of active deep learning to accelerate drug discovery in low-data regimes. Chemical space ; Active learning (machine learning) ; Drug discovery ; Computer science ; Artificial intelligence ; Machine learning ; Deep learning ; Set (abstract data type) ; Traverse ; Process (computing) ; Data science ; Bioinformatics ; Biology Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Sterling G. Baird | Ramsey Issa | Taylor D. Sparks
Source: Data in Brief (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

Materials science optimization benchmark dataset for multi-objective, multi-fidelity optimization of hard-sphere packing simulations In scientific disciplines, benchmarks play a vital role in driving progress forward. For a benchmark to be effective, it must closely resemble real-world tasks. If the level of difficulty or relevance is inadequate, it can impede progress in the field. Moreover, benchmarks should have low computational overhead to ensure accessibility and repeatability. The objective is to achieve a kind of ``Turing test'' by creating a surrogate model that is practically indistinguishable from the ground truth observation, at least within the dataset's explored boundaries. This objective necessitates a large quantity of data. This data encompasses numerous features that are characteristic of chemistry and materials science optimization tasks that are relevant to industry. These features include high levels of noise, multiple fidelities, multiple objectives, linear constraints, non-linear correlations, and failure regions. We performed 494498 random hard-sphere packing simulations representing 206 CPU days' worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. The data was logged in a free-tier shared MongoDB Atlas database, producing two core tabular datasets: a failure probability dataset and a regression dataset. The failure probability dataset maps unique input parameter sets to the estimated probabilities that the simulation will fail. The regression dataset maps input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets were used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. In the regression dataset, percentile ranks were calculated for each group of identical parameter sets to account for heteroskedastic noise, thereby ensuring reliable and accurate data. This differs from the conventional approach that imposes a-priori assumptions, such as Gaussian noise, by specifying mean and standard deviation. This technique can be extended to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and the complex optimization scenarios encountered in the real world. Computer science ; Benchmark (surveying) ; Overhead (engineering) ; Noise (video) ; Uncertainty quantification ; Data mining ; Algorithm ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mariam Akram | Malika Rani | Kiran Batool | Mohamed A. Habila | Mika Sillanpää | Aqeel Ahmed Shah | Rubia Shafique
Source: Physica Scripta (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Lead integrated two-dimensional (MXene/PbCrO<sub>4</sub>) nanocomposite designed for energy storage and photocatalytic degradation applications Abstract Supercapacitors (SCs), among other electrochemical device applications, require materials with maximal energy storage capacity, and the stacked two-dimensional titanium carbide MXene (Ti3C 2 ) sparked the development of these materials. This paper embellished to present smoothed MXene/PbCrO 4 nanocomposite via co-precipitation method along with modified sol–gel achieved lead chromate (PbCrO 4 ) nano-crystalline for energy storage and photocatalytic applications using ethylene glycol as connecting agent to restrict nano-particle growth. It is evident from photoluminescence spectra that peak intensity has decreased, whilst Raman spectra show the presence of MXene and lead peaks in the nanocomposite, whereas FTIR has revealed the presence of functional groups in synthesized material. According to calculations made using EIS spectra, the charge transfer resistance is 1.4 Ω, with the electron shift rate constant K app value 6.98 10 −9 cm s −1 . Additionally, the electrochemical performance of the designed material in supercapacitors at 0.3Ag −1 of current density indicates elevated capacitance of 5408 Fg −1 with scan rate of 10 mV s −1 using 1MKOH aqueous electrolyte, resulting in power and energy densities of 2991.8 W kg −1 and 110.1 Wh K −1 g −1 , respectively. UV–vis spectra shows the nanocomposite has a 1.86 eV band gap that, in the presence of direct sunlight, might cause the destruction of MB dye at a rate of 92.79%. These findings suggested that the newly created MXene/PbCrO 4 nanocomposite demonstrates evidence of substantial features as compared to single materials has potential as an electrode material for supercapacitors as well as best photocatalyst for the degradation of organic pollutants regarding water purification. Materials science ; Nanocomposite ; Supercapacitor ; Chemical engineering ; Photocatalysis ; Anatase ; Electrolyte ; Horizontal scan rate ; Band gap ; Raman spectroscopy ; Photoluminescence ; Capacitance ; Nanotechnology ; Electrochemistry ; Optoelectronics ; Cyclic voltammetry ; Optics ; Electrode ; Organic chemistry ; Chemistry MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Vidushi Sharma | Maxwell J. Giammona | Dmitry Yu. Zubarev | Andy Tek | Khanh Nugyuen | Linda K. Sundberg | Daniele Congiu | Young‐Hye La
Source: arXiv (Cornell University) (preprint, 2023-07-07)
Field: Materials Science — Materials Chemistry

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique. Computer science ; Domain (mathematical analysis) ; Electrolyte ; Process (computing) ; Chemical space ; Battery (electricity) ; Biological system ; Chemistry ; Mathematics ; Drug discovery ; Thermodynamics ; Physics ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering