jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hesam Makki | Alessandro Troisi
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Morphology of conducting polymer blends at the interface of conducting and insulating phases: insight from PEDOT:PSS atomistic simulations Having phase-separated conductive and less-conductive domains is a common morphology in semiconducting polymer blends as it exists in the case of PEDOT:PSS, which is a representative example with a wide range of applications. In this paper, we constructed atomistic models for the interface between the PEDOT-rich (conductive) grains and the PSS-rich (less-conductive) phase through molecular dynamics simulations. Our models are obtained from experimentally relevant compositions, based on precise force field parameters, and through a robust relaxation procedure. We show that both PEDOT-rich and PSS-rich phases consist of PEDOT lamellae embedded in PSS chains. The size of these lamellae depends on the PEDOT concentration in each phase and our model predictions are in quantitative agreement with the experimental data. Furthermore, our models suggest that neither the phases nor the interfaces are entirely connected by π-π stacking. Thus, inter-lamellae tunnelling is essential for both intra- and inter-grain charge transport. We also show that a small increase (≈8 wt%) in the PEDOT concentration results in rather larger lamellae sizes, considerably more oriented lamellae, and slightly better inter-lamellae connectivity, which result in enhanced intra-grain conductivity. Moreover, we show how enhancing phase separation between PEDOT-rich and PSS-rich domains (similar to the effect of polar co-solvents), <i>i.e.</i>, pulling out PEDOT from the PSS-rich phase and adding it in the PEDOT-rich phase, highly enhances the intra-grain connectivity but decreases the inter-grain conduction paths through the interface. Our results explain how the marginal extra degree of phase separation (based on experimentally obtained values) could result in a great enhancement in the overall film conductivity. PEDOT:PSS ; Materials science ; Electrical conductor ; Morphology (biology) ; Conductive polymer ; Interface (matter) ; Chemical physics ; Molecular dynamics ; Polymer ; Chemical engineering ; Composite material ; Nanotechnology ; Computational chemistry ; Contact angle Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical Analysis and Applications
Authors: Rocío García‐Vázquez | Jesper Tranekjær Jørgensen | Klas Bratteby | Vladimir Shalgunov | Lars Hvass | Matthias M. Herth | Andreas Kjær | Umberto Maria Battisti
Source: Pharmaceuticals (article, 2022-02-18)
Field: Chemistry — Organic Chemistry

Development of 18F-Labeled Bispyridyl Tetrazines for In Vivo Pretargeted PET Imaging Pretargeted PET imaging is an emerging and fast-developing method to monitor immuno-oncology strategies. Currently, tetrazine ligation is considered the most promising bioorthogonal reaction for pretargeting in vivo. Recently, we have developed a method to <sup>18</sup>F-label ultrareactive tetrazines by copper-mediated fluorinations. However, bispyridyl tetrazines-one of the most promising structures for in vivo pretargeted applications-were inaccessible using this strategy. We believed that our successful efforts to <sup>18</sup>F-label H-tetrazines using low basic labeling conditions could also be used to label bispyridyl tetrazines via aliphatic nucleophilic substitution. Here, we report the first direct <sup>18</sup>F-labeling of bispyridyl tetrazines, their optimization for in vivo use, as well as their successful application in pretargeted PET imaging. This strategy resulted in the design of [<sup>18</sup>F]<b>45</b>, which could be labeled in a satisfactorily radiochemical yield (RCY = 16%), molar activity (A<sub>m</sub> = 57 GBq/µmol), and high radiochemical purity (RCP > 98%). The [<sup>18</sup>F]<b>45</b> displayed a target-to-background ratio comparable to previously successfully applied tracers for pretargeted imaging. This study showed that bispyridyl tetrazines can be developed into pretargeted imaging agents. These structures allow an easy chemical modification of <sup>18</sup>F-labeled tetrazines, paving the road toward highly functionalized pretargeting tools. Moreover, bispyridyl tetrazines led to near-instant drug release of iTCO-tetrazine-based 'click-to-release' reactions. Consequently, <sup>18</sup>F-labeled bispyridyl tetrazines bear the possibility to quantify such release in vivo in the future. In vivo ; Medicine ; Pet imaging ; Nuclear medicine ; Medical physics ; Positron emission tomography ; Biology Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Martin Šebesta | Hana Vojtková | Veronika Cyprichová | Avinash P. Ingle | Martin Urík | Marek Kolenčík
Source: International Journal of Molecular Sciences (review, 2022-12-24)
Field: Materials Science — Materials Chemistry

Mycosynthesis of Metal-Containing Nanoparticles—Synthesis by Ascomycetes and Basidiomycetes and Their Application Fungi contain species with a plethora of ways of adapting to life in nature. Consequently, they produce large amounts of diverse biomolecules that can be generated on a large scale and in an affordable manner. This makes fungi an attractive alternative for many biotechnological processes. Ascomycetes and basidiomycetes are the most commonly used fungi for synthesis of metal-containing nanoparticles (NPs). The advantages of NPs created by fungi include the use of non-toxic fungus-produced biochemicals, energy efficiency, ambient temperature, pressure conditions, and the ability to control and tune the crystallinity, shape, and size of the NPs. Furthermore, the presence of biomolecules might serve a dual function as agents in NP formation and also capping that can tailor the (bio)activity of subsequent NPs. This review summarizes and reviews the synthesis of different metal, metal oxide, metal sulfide, and other metal-based NPs mediated by reactive media derived from various species. The phyla ascomycetes and basidiomycetes are presented separately. Moreover, the practical application of NP mycosynthesis, particularly in the fields of biomedicine, catalysis, biosensing, mosquito control, and precision agriculture as nanofertilizers and nanopesticides, has been studied so far. Finally, an outlook is provided, and future recommendations are proposed with an emphasis on the areas where mycosynthesized NPs have greater potential than NPs synthesized using physicochemical approaches. A deeper investigation of the mechanisms of NP formation in fungi-based media is needed, as is a focus on the transfer of NP mycosynthesis from the laboratory to large-scale production and application. Nanotechnology ; Biomolecule ; Nanoparticle ; Metal ; Materials science ; Biochemical engineering ; Chemistry ; Organic chemistry Nanoparticles: synthesis and applications ; Bone Tissue Engineering Materials
Authors: Ziming Ren | Dong Zhang | Chen Jiao | Danqin Li | Yun Wu | Xiuyun Wang | Cong Gao | Yefan Lin | Yong‐Ling Ruan | Yiping Xia
Source: The Plant Journal (article, 2022-08-09)
Field: Chemistry — Organic Chemistry

Comparative transcriptome and metabolome analyses identified the mode of sucrose degradation as a metabolic marker for early vegetative propagation in bulbs of <i>Lycoris</i> Vegetative propagation (VP) is an important practice for production in many horticultural plants. Sugar supply constitutes the basis of VP in bulb flowers, but the underlying molecular basis remains elusive. By performing a combined sequencing technologies coupled with ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry approach for metabolic analyses, we compared two Lycoris species with contrasting regeneration rates: high-regeneration Lycoris sprengeri and low-regeneration Lycoris aurea. A comprehensive multi-omics analyses identified both expected processes involving carbohydrate metabolism and transcription factor networks, as well as the metabolic characteristics for each developmental stage. A higher abundance of the differentially expressed genes including those encoding ethylene responsive factors was detected at bulblet initiation stage compared to the late stage of bulblet development. High hexose-to-sucrose ratio correlated to bulblet formation across all the species examined, indicating its role in the VP process in Lycoris bulb. Importantly, a clear difference between cell wall invertase (CWIN)-catalyzed sucrose unloading in high-regeneration species and the sucrose synthase-catalyzed pathway in low-regeneration species was observed at the bulblet initiation stage, which was supported by findings from carboxyfluorescein tracing and quantitative real-time PCR analyses. Collectively, the findings indicate a sugar-mediated model of the regulation of VP in which high CWIN expression or activity may promote bulblet initiation via enhancing apoplasmic unloading of sucrose or sugar signals, whereas the subsequent high ratio of hexose-to-sucrose likely supports cell division characterized in the next phase of bulblet formation. Sucrose ; Biology ; Hexose ; Invertase ; Metabolome ; Biochemistry ; Botany ; Sucrose synthase ; Transcriptome ; Enzyme ; Gene expression ; Gene ; Metabolite Chemical synthesis and alkaloids ; Botanical Research and Applications ; Fungal Biology and Applications
Authors: Aini Palizhati | Steven B. Torrisi | Muratahan Aykol | Santosh K. Suram | Jens S. Hummelshøj | Joseph H. Montoya
Source: Scientific Reports (article, 2022-03-18)
Field: Materials Science — Materials Chemistry

Agents for sequential learning using multiple-fidelity data Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values. The fidelities of data come from the results of DFT calculations as low fidelity and experimental results as high fidelity. We demonstrate performance gains of agents which incorporate multi-fidelity data in two contexts: either using a large body of low fidelity data as a prior knowledge base or acquiring low fidelity data in-tandem with experimental data. This advance provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. This may also serve as a reference point for those who are interested in practical strategies that can be used when multiple data sources are available for active or sequential learning campaigns. Fidelity ; Computer science ; Benchmark (surveying) ; Machine learning ; High fidelity ; Artificial intelligence ; Hyperparameter ; Synthetic data ; Data mining ; Data science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Expert finding and Q&A systems
Authors: Ruiyuan Kang | Panos Liatsis | Dimitrios C. Kyritsis
Source: Energies (review, 2022-04-30)
Field: Chemistry — Spectroscopy

Emission Quantification via Passive Infrared Optical Gas Imaging: A Review Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of interest, offers non-invasive remote sensing, and provides the capability for spatially resolved measurements. It has been broadly applied to emission detection, localization, and visualization; however, emission quantification is a long-standing challenge for passive IOGI. In order to facilitate the development of quantitative IOGI, in this review, we summarize theoretical findings suggesting that a single pixel value does not provide sufficient information for quantification and then we proceed to collect, organize, and summarize effective and potential methods that can support IOGI to quantify column density, concentration, and emission rate. Along the way, we highlight the potential of the strong coupling of artificial intelligence (AI) with quantitative IOGI in all aspects, which substantially enhances the feasibility, performance, and agility of quantitative IOGI, and alleviates its heavy reliance on prior context-based knowledge. Despite progress in quantitative IOGI and the shift towards low-carbon/carbon-free fuels, which reduce the complexity of quantitative IOGI application scenarios, achieving accurate, robust, convenient, and cost-effective quantitative IOGI for engineering purposes, interdisciplinary efforts are still required to bring together the evolution of imaging equipment. Advanced AI algorithms, as well as the simultaneous development of diagnostics based on relevant physics and AI algorithms for the accurate and correct extraction of quantitative information from infrared images, have thus been introduced. Computer science ; Greenhouse gas ; Context (archaeology) ; Quantitative assessment ; Infrared ; Remote sensing ; Environmental science ; Risk analysis (engineering) ; Physics ; Optics ; Geology Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: M Karthikeyan | Durga Madhab Mahapatra | Abdul Syukor Abd Razak | Abdulaziz A.M. Abahussain | Baranitharan Ethiraj | Lakhveer Singh
Source: Catalysis Reviews (article, 2022-07-30)
Field: Materials Science — Materials Chemistry

Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects There have been significant advancements in catalysis today, especially with the aid of artificial intelligence (AI). Understanding the relationship between the descriptors and catalytic performance and underlying principles is crucial for hydrogen evolution reaction (HER) catalysis. As of today, the usage and application of machine learning (ML) are soaring and there has been a phenomenal demand owing to its immense pattern recognition capability and optimization potential. Its applications in computational quantum mechanical modeling have inspired promising development in HER catalyst scrutiny and screening. The present communication reviews the general scheme of ML, data sources, and computational tools for HER catalyst screening and therefore provides a strong basis for its application. Furthermore, the analysis elucidates how ML algorithms are used for the prediction of a) adsorption energies, b) reaction descriptors, c) structure–property relationships, d) catalyst discovery, and e) synthesis conditions and reaction design of HER catalysts. Lastly, key constraints and the need for an integrative platform have been highlighted. Based on literature surveys, recent trends, material advancements, systems understanding, and key challenges in deploying ML tools, future directions with a road map have been provided for an evolved design and development in the case of HER catalysis. Scrutiny ; Computer science ; Biochemical engineering ; Catalysis ; Artificial intelligence ; Key (lock) ; Nanotechnology ; Data science ; Machine learning ; Management science ; Engineering ; Chemistry ; Materials science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Osvaldo N. Oliveira | Maria Cristina Ferreira de Oliveira
Source: Frontiers in Chemistry (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Materials Discovery With Machine Learning and Knowledge Discovery Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities. Artificial intelligence ; Machine learning ; Knowledge extraction ; Computer science ; Perspective (graphical) ; Scientific discovery ; Interpretation (philosophy) ; Drug discovery ; Discovery learning ; Data science ; Chemistry ; Cognitive science ; Mathematics ; Mathematics education ; Psychology Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Cameron McElfresh | Collin Roberts | Sicong He | Sergey V. Prikhodko | Jaime Marian
Source: Computational Materials Science (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys Materials science ; Microstructure ; Titanium alloy ; Metallurgy ; Alloy Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Zhi‐Wen Zhao | Marcos del Cueto | Alessandro Troisi
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-one-group-out cross-validation, in which the ML model is trained to explicitly perform extrapolations of unseen chemical families. This approach can be used across materials science and chemistry problems to improve the added value of ML predictions, instead of using extrapolative ML models that were trained with a regular cross-validation. We consider as a case study the problem of the discovery of non-fullerene acceptors because novel classes of acceptors are naturally classified into distinct chemical families. We show that conventional ML methods are not useful in practice when attempting to predict the efficiency of a completely novel class of materials. The approach proposed in this work increases the accuracy of the predictions to enable at least the categorization of materials with a performance above and below the median value. Computer science ; Machine learning ; Class (philosophy) ; Artificial intelligence ; Categorization ; Value (mathematics) ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Yilin Yang | Zhitao Guo | Andrew J. Gellman | John R. Kitchin
Source: The Journal of Physical Chemistry C (article, 2022-01-19)
Field: Materials Science — Materials Chemistry

Simulating Segregation in a Ternary Cu–Pd–Au Alloy with Density Functional Theory, Machine Learning, and Monte Carlo Simulations Simulation of the segregation profile of multicomponent alloys is important to investigate the catalytic properties of alloy catalysts. Density functional theory (DFT) is too expensive to use directly to evaluate the potential energies of the slab configurations during the simulations. In this work, we build a neural network (NN) based on 5278 DFT calculations as a surrogate model to evaluate the potential energies of the fcc(111) slabs for a ternary Cu–Pd–Au alloy. The trained NN is capable of predicting the Cu–Pd–Au potential energies across the whole ternary space with high accuracy. Combining the NN with Monte Carlo simulation, we obtained the segregation profile of Cu–Pd–Au at 600 K across the bulk composition space. The simulation results are qualitatively consistent with the experimental data for PdAu and CuAu, but they are incorrect along the PdCu line. Further DFT calculations show that the perfect fcc(111) slab is not capable of capturing the CuPd segregation behavior on undercoordinated surfaces under the realistic conditions. Ternary operation ; Density functional theory ; Monte Carlo method ; Materials science ; Slab ; Alloy ; Work (physics) ; Space (punctuation) ; Statistical physics ; Thermodynamics ; Chemical physics ; Computational chemistry ; Chemistry ; Physics ; Computer science ; Metallurgy ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; nanoparticles nucleation surface interactions
Authors: Du Chen | Feng Jiang | Liang Fang | Yongbin Zhu | Caichao Ye | Weishu Liu
Source: Rare Metals (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Machine learning assisted discovering of new M <sub>2</sub> X <sub>3</sub> ‐type thermoelectric materials Abstract Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try‐and‐error efforts to experience‐based discovering and first‐principles calculation. However, both the experiment and first‐principles calculation deriving routes to determine a new compound are time and resources consuming. Here, we demonstrated a machine learning approach to discover new M 2 X 3 ‐type thermoelectric materials with only the composition information. According to the classic Bi 2 Te 3 material, we constructed an M 2 X 3 ‐type thermoelectric material library with 720 compounds by using isoelectronic substitution, in which only 101 compounds have crystalline structure information in the Inorganic Crystal Structure Database (ICSD) and Materials Project (MP) database. A model based on the random forest (RF) algorithm plus Bayesian optimization was used to explore the underlying principles to determine the crystal structures from the known compounds. The physical properties of constituent elements (such as atomic mass, electronegativity, ionic radius) were used to define the feature of the compounds with a general formula 1 M 2 M 1 X 2 X 3 X ( 1 M + 2 M: 1 X + 2 X + 3 X = 2:3). The primary goal is to find new thermoelectric materials with the same rhombohedral structure as Bi 2 Te 3 by machine learning. The final trained RF model showed a high accuracy of 91% on the prediction of rhombohedral compounds. Finally, we selected four important features to proceed with the polynomial fitting with the prediction results from the RF model and used the acquired polynomial function to make further discoveries outside the pre‐defined material library. Thermoelectric effect ; Type (biology) ; Computer science ; Random forest ; Electronegativity ; Feature (linguistics) ; Materials science ; Algorithm ; Crystal structure ; Artificial intelligence ; Polynomial ; Thermoelectric materials ; Machine learning ; Thermodynamics ; Crystallography ; Physics ; Mathematics ; Chemistry ; Mathematical analysis Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography