jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pierre-Paul De Breuck | Grégoire Heymans | Gian‐Marco Rignanese
Source: Journal of Materials Informatics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Accurate experimental band gap predictions with multifidelity correction learning To improve the precision of machine-learning predictions, we investigate various techniques that combine multiple quality sources for the same property. In particular, focusing on the electronic band gap, we aim at having the lowest error by taking advantage of all available experimental measurements and density-functional theory calculations. We show that learning about the difference between high- and low-quality values, considered a correction, significantly improves the results compared to learning on the sole high-quality experimental data. As a preliminary step, we also introduce an extension of the MODNet model, which consists of using a genetic algorithm for hyperparameter optimization. Thanks to this, MODNet is shown to achieve excellent performance on the Matbench test suite. Hyperparameter ; Computer science ; Property (philosophy) ; Suite ; Test suite ; Quality (philosophy) ; Machine learning ; Artificial intelligence ; Algorithm ; Experimental data ; Mathematics ; Test case ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Wenjie Liao | Ping Liu
Source: Catalysis Science & Technology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps A data-driven framework was developed which used ML surrogate model to extract activity controlling descriptors from kinetics dataset. It enhanced mechanic understanding and predicted catalytic activities more accurately than derivate-based method. Mechanism (biology) ; Identification (biology) ; Catalysis ; Kinetics ; Biological system ; Chemistry ; Computer science ; Biochemical engineering ; Engineering ; Biology ; Physics ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Matthew D. Hannigan | Jessica L. Tami | Paul M. Zimmerman | Anne J. McNeil
Source: Macromolecules (article, 2022-12-07)
Field: Chemistry — Organic Chemistry

Rethinking Catalyst Trapping in Ni-Catalyzed Thieno[3,2-<i>b</i>]thiophene Polymerization Catalyst-transfer polymerization (CTP) is a chain-growth method used to synthesize conjugated polymers. Although CTP works well for most donor-type monomers, the polymerization stalls with thieno[3,2-<i>b</i>]thiophene when using Ni catalysts. Previous reports have rationalized this result by suggesting that the catalyst is trapped in a Ni<sup>0</sup> <i>π</i>-complex with the highly electron-rich arene. In this study, evidence is provided that the catalyst trap is more likely a Ni<sup>II</sup> complex that arises from oxidative insertion of Ni<sup>0</sup> into the C-S bonds of thieno[3,2-<i>b</i>]thiophene. This result is consistent with the known reactivity of Ni<sup>0</sup> complexes toward <i>S</i>-heteroarenes and is supported herein by <sup>31</sup>P nuclear magnetic resonance spectra acquired in situ, as well as data collected from small-molecule model reactions and density-functional theory simulations of the polymerization. We propose that this C-S insertion pathway and related off-cycle reactions may be relevant to understanding or enabling the CTP of other monomers with fused thiophenes. Thiophene ; Polymerization ; Catalysis ; Chemistry ; Reactivity (psychology) ; Monomer ; Photochemistry ; Polymer chemistry ; Conjugated system ; Living polymerization ; Polymer ; Radical polymerization ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Sathiyaraj Chinnasamy | M Ramachandran | Sowmiya Soundharaj | Manjula Selvam
Source:  (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Investigation of Various Cement Productions and Its Characterization Concrete Alumina, silica, lime, iron oxide and A powder of magnesium oxide Burned together in a kiln Used as a fine powder a uncooked fabric for mortar and urban: any compound is used for the identical motive. A binding element or object and many others. Water is the primary element When it is mixed with cement Connecting the whole together Creating a paste. Water hardens concrete thru a procedure known as hydration. Cement is a binder used in production to bond, harden and glue other substances together. Cement is rarely used alone, but to bond sand and gravel cement technology, Kothanar Supply Inc. is a privately owned company that supplies hydraulic cement and patch mixtures to businesses throughout North America, including the United States. It is not widely used in cement construction in Canada and Puerto Rico because it has higher thermal hydration than concrete, cement is less durable than concrete and is prone to cracking. It is difficult to cure and thus does not apply to areas that are easily affected by movement. Today, the most important investments in our country’s Infrastructure, transportation, culture and improvement are built with cement and concrete. Infrastructure initiatives such as the Hoover Dam and the Los Angeles Aqueduct helped shape West America, the building block of most bridges, roads, dams, and structures, releasing large amounts of CO2 into concrete each year. The cement industry, the most consumed material on earth besides water, Is the 0.33 largest business source of pollutants, emitting in step with . Against the backdrop of a growing population, per capita consumption represents a dramatic decline. Any use of non-renewable resources is essentially unsustainable. Uses fossil fuels, bulk sand and gravel to make concrete and cement Cement ; Lime ; Cracking ; Engineering ; Environmental science ; Materials science ; Waste management ; Metallurgy ; Composite material Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Environmental Engineering and Cultural Studies
Authors: Christopher Kuenneth | Rampi Ramprasad
Source: Research Square (preprint, 2022-12-13)
Field: Materials Science — Materials Chemistry

polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics Informatics ; Ultrashort pulse ; Polymer ; Computer science ; Nanotechnology ; Data science ; Materials science ; Engineering ; Physics ; Electrical engineering ; Optics ; Laser Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ruizhi Zhang | Sohan Seth | James Cumby
Source: Digital Discovery (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

Grouped representation of interatomic distances as a similarity measure for crystal structures Grouped Representation of Interatomic Distances (GRID) accurately quantifies similarity between crystal structures and can be used to predict physical properties. Similarity (geometry) ; Representation (politics) ; Measure (data warehouse) ; Crystal (programming language) ; Crystal structure ; Interatomic potential ; Similarity measure ; Statistical physics ; Mathematics ; Computer science ; Crystallography ; Physics ; Data mining ; Molecular dynamics ; Artificial intelligence ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Yannick Ureel | Florence H. Vermeire | Maarten K. Sabbe | Kevin M. Van Geem
Source: Industrial & Engineering Chemistry Research (article, 2022-12-29)
Field: Chemistry — Inorganic Chemistry

Ab Initio Group Additive Values for Thermodynamic Carbenium Ion Property Prediction Carbenium ions are important intermediates in both zeolite and plasma chemistry. The construction of kinetic models for zeolite and plasma chemistry requires the incorporation of thermodynamic properties of these carbenium ions. In this way, thermodynamic equilibrium is incorporated resulting in more accurate and general kinetic models, which facilitate rational zeolite design and plasma process development. In this work, a consistent set of 46 group additive values (GAVs) and non-nearest neighbor interactions (NNIs) is determined for the standard enthalpy of formation, standard molar entropy, and heat capacity. The GAVs are regressed based on 165 quantum chemistry calculations with the CBS-QB3 composite method for carbenium ions as CBS-QB3 was proven a valid method for carbenium property prediction. The presented group additive approach gives the first GAVs and NNIs for entropy and heat capacity and 33 novel values for enthalpy prediction. The determined non-nearest neighbor interactions account for the effect of longer range hyperconjugation, resonance stabilization, and inductive stabilization, which are shown to be crucial for the carbenium stability. The presented group additivity scheme ensures an accurate prediction of all properties with a mean absolute error of 4.82 kJ/mol, 4.92 J/(mol·K), and 1.95 J/(mol·K) for the standard enthalpy of formation, standard molar entropy, and heat capacity, respectively. The reported GAVs and NNIs ensure the thermodynamic property prediction of paraffinic, olefinic, alkynic, aromatic, and five- and six-ring naphthenic cations. Chemistry ; Heat capacity ; Enthalpy ; Carbenium ion ; Thermodynamics ; Ab initio ; Standard molar entropy ; Standard enthalpy of formation ; Additive function ; Computational chemistry ; Thermochemistry ; Ion ; Physical chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Thermal and Kinetic Analysis ; Catalysis and Oxidation Reactions
Authors: Julius Seumer | Jan H. Jensen
Source: ChemRxiv (preprint, 2022-08-08)
Field: Materials Science — Materials Chemistry

Computational evolution of new catalysts for the Morita–Baylis–Hillman reaction We present a de novo discovery of an efficient catalyst of the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate determining step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We performed five independent GA searches that resulted in 448 unique molecules, for which we were able to locate 435 true transitions states at semiempirical level of theory. The predicted activation energies of all 435 molecules where all lower than that of DABCO, which is a popular catalyst of the MBH reaction. Virtually all the molecules contain an aziridine N as the catalytically active site, which is discovered by the GA since it is either not found in the initial population or discarded early only to be redisovered as the search progresses. Many of the GA searches also introduce a substituent with a hydrogen bond donor that helps to stabilize the transition state and thus lower the barrier. Two molecules are selected for further study based on their synthetic accessibility as predicted by the retrosynthesis package Manifold. For these two molecules we compute the entire free energy reaction profile at the DFT level and show that their rate determining barriers are 1.7 and 2.4 kcal/mol lower than that of DABCO. The molecule with the lowest barrier has higher barriers for the other steps compared to DABCO, but none of the barriers are competitive with the rate-determining barrier and is predicted to outperform DABCO. This demonstrates the power of free exploration of chemical a space compared to more constrained fragment-based approaches. DABCO ; Substituent ; Molecule ; Chemistry ; Catalysis ; Population ; Transition state ; Computational chemistry ; Small molecule ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Synthesis and Catalytic Reactions
Authors: Federico Fontana | Fabrizio Gelain
Source: Nanotechnology Reviews (article, 2022-01-01)
Field: Materials Science — Biomaterials

Modeling of supramolecular biopolymers: Leading the <i>in silico</i> revolution of tissue engineering and nanomedicine Abstract The field of tissue engineering is poised to be positively influenced by the advent of supramolecular biopolymers, because of their promising tailorability coming from the bottom-up approach used for their development, absence of toxic byproducts from their gelation reaction and intrinsic better mimicry of extracellular matrix nanotopography and mechanical properties. However, a deep understanding of the phenomena ruling their properties at the meso- and macroscales is still missing. In silico approaches are increasingly helping to shine a light on questions still of out of reach for almost all empirical methods. In this review, we will present the most significant and updated efforts on molecular modeling of SBP properties, and their interactions with the living counterparts, at all scales. In detail, the currently available molecular mechanic approaches will be discussed, paying attention to the pros and cons related to their representability and transferability. We will also give detailed insights for choosing different biomolecular modeling strategies at various scales. This is a systematic overview of tools and approaches yielding to advances at atomistic, molecular, and supramolecular levels, with a holistic perspective demonstrating the urgent need for theories and models connecting biomaterial design and their biological effect in vivo . Supramolecular chemistry ; Nanotechnology ; Computer science ; Transferability ; Biochemical engineering ; In silico ; Nanomedicine ; Data science ; Chemistry ; Engineering ; Materials science Supramolecular Self-Assembly in Materials ; Connective tissue disorders research ; Supramolecular Chemistry and Complexes
Authors: Alena Vishina | Olle Eriksson | Heike C. Herper
Source: Materials Research Letters (article, 2022-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fe<sub>2</sub>C- and Mn<sub>2</sub>(W/Mo)B<sub>4</sub>-based rare-earth-free permanent magnets as a result of the high-throughput and data-mining search A high-throughput and data-mining search for rare-earth-free permanent magnets is reported for materials containing a 3<i>d</i> and <i>p</i>-element of the Periodic Table. Three of the most promising compounds, Fe2C, Mn2MoB4, and Mn2WB4, were investigated in detail by ab initio electronic structure theory coupled to atomistic spin-dynamics. For these systems doping protocols were also investigated and, in particular, (Fe0.75X0.25)2C (X = Mn, Cr, V, and Ti), Mn2XB4 (X = Mo and W) along with Mn2(X0.5Y0.5)B4 (X,Y = Mo, W, Ta, Cr) are suggested here as promising candidates for applications as permanent magnets. Several promising high-performance rare-earth-free permanent magnets have been found as a result of an ab initio high-throughput search. Alteration proposed to improve their stability and magnetic properties. Magnet ; Materials science ; Rare earth ; Doping ; Crystallography ; Ab initio ; Electronic structure ; Ab initio quantum chemistry methods ; Density functional theory ; Condensed matter physics ; Metallurgy ; Computational chemistry ; Physics ; Optoelectronics ; Chemistry Magnetic Properties of Alloys ; Boron and Carbon Nanomaterials Research ; Rare-earth and actinide compounds
Authors: Mücahit Yılmaz | Hanifi Kebiroglu
Source: Journal of Physical Chemistry and Functional Materials (article, 2022-12-11)
Field: Chemistry — Spectroscopy

Investigation of K-Serotonin Structure Using Nuclear Magnetic Resonance by Quantum Chemical Methods In this study, the K-Serotonin structure formed by adding potassium to Serotonin (5-HT) was investigated by quantum chemical methods. 5-hydroxytryptamine is a neurotransmitter that affects mood, making people feel happy and energetic. 5-HT and 5-HT-potassium were optimized with some base sets to find the optimal band gap using the density function theory (DFT) and Hartree-Fock (HF) method. The most suitable DFT/LanL2DZ was selected for the structure with and without additives. By calculating the values for 5-HT and K-5-HT, the effects of potassium on 5-HT binding were compared. Serotonin ; Density functional theory ; Potassium ; Quantum chemical ; Chemistry ; Wave function ; Quantum ; Neurotransmitter ; Computational chemistry ; Nuclear magnetic resonance ; Materials science ; Molecule ; Atomic physics ; Physics ; Quantum mechanics ; Organic chemistry ; Biochemistry Molecular spectroscopy and chirality ; Free Radicals and Antioxidants ; Analytical Chemistry and Chromatography
Authors: Lindsay R. Merte | Malthe Kjær Bisbo | Igor Sokolović | Martin Setvín | Benjamin Hagman | Mikhail Shipilin | Michael Schmid | Ulrike Diebold | Edvin Lundgren | Bjørk Hammer
Source: Angewandte Chemie (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Structure of an Ultrathin Oxide on Pt<sub>3</sub>Sn(111) Solved by Machine Learning Enhanced Global Optimization** Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory-based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure-the (4×4) surface oxide on Pt<sub>3</sub>Sn(111)-based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation. Intuition ; Computer science ; Oxide ; Surface (topology) ; Computational learning theory ; Algorithm ; Artificial intelligence ; Machine learning ; Population ; Materials science ; Mathematics ; Active learning (machine learning) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Agustina Ari Murti Budi Hastuti
Source: INDONESIAN JOURNAL OF PHARMACY (article, 2022-03-29)
Field: Chemistry — Analytical Chemistry

Utilization of UV-visible and FTIR spectroscopy coupled with chemometrics for differentiation of Indonesian tea: an exploratory study Ultraviolet (UV)-visible and Fourier-transformed infrared (FTIR) spectroscopy are two of the most popular and readily available laboratory instruments. Fingerprinting analysis of the UV-visible and FTIR spectra has been applied for food classification and authentication studies. In this study, the UV-visible and FTIR spectra of brewed tea, and their data fusion data sets, were used to build models for the classification of tea based on tea types and origins. The study included black and green tea samples from several provinces in Sumatra and Java Islands (Indonesia). Chemometric models of principal component analysis (PCA), k-nearest neighbor (kNN), and logistic regression were developed for classification purposes. All PCA models were able to well-separate the tea groups. kNN and logistic regression models based on UV-visible spectra successfully classified green and black tea with &gt;0.8 classification accuracy. The kNN model of FTIR spectra had good accuracy (0.903) for classifying tea based on its origin. ReliefF algorithm was employed to select the best features among the data fusion data sets of UV-visible and FTIR spectra. The data fusion data sets of UV-visible and FTIR spectra demonstrated good separation of tea types and origins with a high area under the ROC curve (&gt;0.8) and moderate accuracy (0.548). Therefore, UV-visible and FTIR spectroscopy may provide complementary information for tea classification based on tea types and origins. Fourier transform infrared spectroscopy ; Chemometrics ; Principal component analysis ; Black tea ; Spectroscopy ; Ultraviolet visible spectroscopy ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Chemistry ; Mathematics ; Artificial intelligence ; Computer science ; Chromatography ; Physics ; Food science ; Optics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Natsuhiko Yoshinaga | S. Tokuda
Source: Physical review. E (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Bayesian modeling of pattern formation from one snapshot of pattern Partial differential equations (PDEs) have been widely used to reproduce patterns in nature and to give insight into the mechanism underlying pattern formation. Although many PDE models have been proposed, they rely on the pre-request knowledge of physical laws and symmetries, and developing a model to reproduce a given desired pattern remains difficult. We propose a method, referred to as Bayesian modeling of PDEs (BM-PDEs), to estimate the best dynamical PDE for one snapshot of a objective pattern under the stationary state without ground truth. We apply BM-PDEs to nontrivial patterns, such as quasicrystals (QCs), a double gyroid, and Frank-Kasper structures. We also generate three-dimensional dodecagonal QCs from a PDE model. This is done by using the estimated parameters for the Frank-Kasper A15 structure, which closely approximates the local structures of QCs. Our method works for noisy patterns and the pattern synthesized without the ground-truth parameters, which are required for the application toward experimental data. Snapshot (computer storage) ; Partial differential equation ; Computer science ; Bayesian probability ; Applied mathematics ; Ground truth ; Algorithm ; Mathematics ; Statistical physics ; Artificial intelligence ; Physics ; Mathematical analysis Solidification and crystal growth phenomena ; Quasicrystal Structures and Properties ; Block Copolymer Self-Assembly
Authors: Yimin Wang | Siming Lu | Jiayong Zheng | L. Liang
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Biomaterials

Advances in Latest Application Status, Challenges, and Future Development Direction of Electrospinning Technology in the Biomedical In recent years, nanotechnology has attracted much attention in the field of biomedicine and sensing technology. As one of the simplest and most effective methods to prepare nanofiber materials, electrospinning technology has a lot of application research in biomedical fields such as sensor detection, drug delivery, and tissue engineering because of its high porosity, large surface area to volume ratio, and uniform size. In this review, we summarized the latest application progress of electrospinning materials in biomedicine, sensing, and textile industry and discussed the challenges and future development direction of electrospinning technology. Electrospinning ; Materials science ; Biomedicine ; Nanotechnology ; Nanofiber ; Drug delivery ; Textile ; Composite material ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Paul Laiu | Ying Yang | Massimiliano Lupo Pasini | Jong Youl Choi | Dongwon Shin
Source: Journal of Phase Equilibria and Diffusion (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions Ternary operation ; Gibbs free energy ; Binary number ; Artificial neural network ; Thermodynamics ; Workflow ; CALPHAD ; Computer science ; Binary data ; Materials science ; Artificial intelligence ; Machine learning ; Phase (matter) ; Data mining ; Phase diagram ; Chemistry ; Mathematics ; Physics ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Hydrogen embrittlement and corrosion behaviors in metals