jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dulyawich Palaporn | Supree Pinitsoontorn | Ken Kurosaki | G. Jeffrey Snyder
Source: Advanced Materials Technologies (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Porous Ag<sub>2</sub>Se Fabricated by a Modified Cold Sintering Process with the Average <i>zT</i> Around Unity Near Room Temperature Abstract Silver selenide (Ag 2 Se) is a promising material for thermoelectric applications near room temperature. This work proposes a new fabrication route for bulk Ag 2 Se via a modified cold sintering process (mCSP) to enhance its figure‐of‐merit ( zT ). Unlike conventional CSP, mCSP employs thiol‐amine (TA) as a liquid medium that completely undissolves Ag 2 Se, leading to a free flow of the TA solution within interparticle channels. By sintering at temperatures above the boiling point of TA, the liquid evaporates, leaving behind a bulk Ag 2 Se material with a tube‐like porous structure (open pores). These induced defects strongly influence thermoelectric properties of Ag 2 Se, with a substantial increase in the Seebeck coefficient and decrease in electrical conductivity. The overall power factor of the mCSP sample with TA surpasses that of the control sample without TA, consistent with effective band modeling of Ag 2 Se. Moreover, the created micropores during mCSP effectively suppresses the thermal conductivity, leading to a maximum zT above 1.0 at 400 K by optimizing mCSP parameters. Importantly, the average zT within the 300–400 K range is around unity, representing one of the highest reported values. Further optimization of mCSP parameters can enhance average zT beyond unity, making Ag 2 Se more competitive with state‐of‐the‐art materials near room temperature. Materials science ; Sintering ; Thermoelectric effect ; Thermal conductivity ; Seebeck coefficient ; Porosity ; Thermoelectric materials ; Fabrication ; Nanotechnology ; Composite material ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Perfectionism, Procrastination, Anxiety Studies ; Quantum Dots Synthesis And Properties
Authors: А. А. Балашов | Katarzyna Bielska | G. Li | Aleksandra A. Kyuberis | Szymon Wójtewicz | J. Domysławska | R. Ciuryło | Н. Ф. Зобов | Daniel Lisak | Jonathan Tennyson | O. L. Polyansky
Source: The Journal of Chemical Physics (article, 2023-06-21)
Field: Chemistry — Spectroscopy

Measurement and calculation of CO (7–0) overtone line intensities Intensities of 14 lines in the sixth overtone (7-0) band of carbon monoxide (12C16O) are measured in the visible range between 14 300 and 14 500 cm-1 using a frequency-stabilized cavity ring-down spectrometer. This is the first observation of such a high and weak overtone spectrum of the CO molecule. A theoretical model is constructed and tested based on the use of a high accuracy ab initio dipole moment curve and a semi-empirical potential energy curve. Accurate studies of high overtone transitions provide a challenge to both experiment and theory as the lines are very weak: below 2 × 10-29 cm molecule-1 at 296 K. Agreement between theory and experiment within the experimental uncertainty of a few percent is obtained. However, this agreement is only achieved after issues with the stability of the Davidson correction to the multi-reference configuration interaction calculations are addressed. Overtone ; Overtone band ; Dipole ; Line (geometry) ; Atomic physics ; Ab initio ; Spectrometer ; Transition dipole moment ; Range (aeronautics) ; Ab initio quantum chemistry methods ; Chemistry ; Spectral line ; Potential energy ; Molecule ; Physics ; Molecular physics ; Optics ; Materials science ; Quantum mechanics ; Geometry ; Mathematics ; Excited state Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate
Authors: Elizabeth A. Pogue | Alexander New | K. McElroy | Nam Q. Le | Michael J. Pekala | Ian McCue | Eddie Gienger | Janna Domenico | Elizabeth Hedrick | Tyrel M. McQueen | Brandon Wilfong | Christine Piatko | Christopher R. Ratto | Andrew Lennon | Christine Chung | Timothy Montalbano | Gregory Bassen | Christopher D. Stiles
Source: npj Computational Materials (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

Closed-loop superconducting materials discovery Abstract Discovery of novel materials is slow but necessary for societal progress. Here, we demonstrate a closed-loop machine learning (ML) approach to rapidly explore a large materials search space, accelerating the intentional discovery of superconducting compounds. By experimentally validating the results of the ML-generated superconductivity predictions and feeding those data back into the ML model to refine, we demonstrate that success rates for superconductor discovery can be more than doubled. Through four closed-loop cycles, we report discovery of a superconductor in the Zr-In-Ni system, re-discovery of five superconductors unknown in the training datasets, and identification of two additional phase diagrams of interest for new superconducting materials. Our work demonstrates the critical role experimental feedback provides in ML-driven discovery, and provides a blueprint for how to accelerate materials progress. Superconductivity ; Blueprint ; Closed loop ; Loop (graph theory) ; Computer science ; Identification (biology) ; Drug discovery ; Physics ; Condensed matter physics ; Bioinformatics ; Mechanical engineering ; Engineering ; Biology ; Mathematics ; Control engineering Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Michael Walker | Gabriella Pizzuto | Hatem Fakhruldeen | Andrew I. Cooper
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Go with the flow: deep learning methods for autonomous viscosity estimations Closed-loop experiments can accelerate material discovery by automating both experimental manipulations and decisions that have traditionally been made by researchers. Fast and non-invasive measurements are particularly attractive for closed-loop strategies. Viscosity is a physical property for fluids that is important in many applications. It is fundamental in application areas such as coatings; also, even if viscosity is not the key property of interest, it can impact our ability to do closed-loop experimentation. For example, unexpected increases in viscosity can cause liquid-handling robots to fail. Traditional viscosity measurements are manual, invasive, and slow. Here we use convolutional neural networks (CNNs) as an alternative to traditional viscometry by non-invasively extracting the spatiotemporal features of fluid motion under flow. To do this, we built a workflow using a dual-armed collaborative robot that collects video data of fluid motion autonomously. This dataset was then used to train a 3-dimensional convolutional neural network (3D-CNN) for viscosity estimation, either by classification or by regression. We also used these models to identify unknown laboratory solvents, again based on differences in fluid motion. The 3D-CNN model performance was compared with the performance of a panel of human participants for the same classification tasks. Our models strongly outperformed human classification in both cases. For example, even with training on fewer than 50 videos for each liquid, the 3D-CNN model gave an average accuracy of 88% for predicting the identity of five different laboratory solvents, compared to an average accuracy of 32% for human observation. For comparison, random category selection would give an average accuracy of 20%. Our method offers an alternative to traditional viscosity measurements for autonomous chemistry workflows that might be used both for process control (<i>e.g.</i>, choosing not to pipette liquids that are too viscous) or for materials discovery (<i>e.g.</i>, identifying new polymerization catalysts on the basis of viscosification). Convolutional neural network ; Viscosity ; Artificial intelligence ; Computer science ; Viscometer ; Workflow ; Machine learning ; Motion (physics) ; Property (philosophy) ; Artificial neural network ; Materials science ; Database Machine Learning in Materials Science ; Data Stream Mining Techniques ; Metabolomics and Mass Spectrometry Studies
Authors: Nicholas Walker | Sanghoon Lee | John Dagdelen | Kevin Cruse | Samuel Gleason | Alexander Dunn | Gerbrand Ceder | A. Paul Alivisatos | Kristin A. Persson | Anubhav Jain
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Extracting structured seed-mediated gold nanorod growth procedures from scientific text with LLMs The synthesis of gold nanorods remains largely heuristically understood. Large language models provide a route for extracting their structured synthesis procedures from scientific articles to accelerate investigation into synthesis pathways. Nanorod ; Nanotechnology ; Computer science ; Materials science Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Giancarla Alberti | Camilla Zanoni | Stefano Spina | Lisa Rita Magnaghi | Raffaela Biesuz
Source: Chemosensors (article, 2023-02-15)
Field: Chemistry — Analytical Chemistry

Trends in Molecularly Imprinted Polymers (MIPs)-Based Plasmonic Sensors In recent years, plasmonic sensors have been used in various fields ranging from environmental monitoring, pharmaceutical analysis, medical diagnosis, and food quality assessment to forensics. A significant amount of information on plasmonic sensors and their applications already exists and there is a continuing development of reliable, selective, sensitive, and low-cost sensors. Combining molecularly imprinting technology with plasmonic sensors is an increasingly timely and important challenge to obtain portable, easy-to-use, particularly selective devices helpful in detecting analytes at the trace level. This review proposes an overview of the applications of molecularly imprinted plasmonic chemosensors and biosensors, critically discussing the performances, pros, and cons of the more recently developed devices. Molecularly imprinted polymer ; Nanotechnology ; Computer science ; Plasmon ; Molecular imprinting ; Materials science ; Chemistry ; Optoelectronics Analytical chemistry methods development ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Xiaowei Jia | Wenlong Mu | Zhu‐Bao Shao | Ying‐Jun Xu
Source: International Journal of Molecular Sciences (article, 2023-01-24)
Field: Materials Science — Polymers and Plastics

Flame-Retardant Cycloaliphatic Epoxy Systems with High Dielectric Performance for Electronic Packaging Materials Flame-retardant cycloaliphatic epoxy systems have long been studied; however, the research suffers from slow and unsatisfactory advances. In this work, we synthesized a kind of phosphorus-containing difunctional cycloaliphatic epoxide (called BCEP). Then, triglycidyl isocyanurate (TGIC) was mixed with BCEP to achieve epoxy systems that are rich in phosphorus and nitrogen elements, which were cured with 4-methylhexahydrobenzene anhydride (MeHHPA) to obtain a series of flame-retardant epoxy resins. Curing behaviors, flame retardancy, thermal behaviors, dielectric performance, and the chemical degradation behaviors of the cured epoxy system were investigated. BCEP-TGIC systems showed a high curing activity, and they can be efficiently cured, in which the incorporation of TGIC decreased the curing activity of the resin. As the ratio of BCEP and TGIC was 1:3, the cured resin (BCEP<sub>1</sub>-TGIC<sub>3</sub>) showed a relatively good flame retardancy with a limiting oxygen index value of 25.2%. In the cone calorimeter test, they presented a longer time to ignition and a lower heat release than the commercially available cycloaliphatic epoxy resins (ERL-4221). BCEP-TGIC systems presented good thermal stability, as the addition of TGIC delayed the thermal weight loss of the resin. BCEP<sub>1</sub>-TGIC<sub>3</sub> had high dielectric performance and outperformed ERL-4221 over a frequency range of 1 HZ to 1 MHz. BCEP<sub>1</sub>-TGIC<sub>3</sub> could achieve degradation under mild conditions in an alkali methanol/water solution. Benefiting from the advances, BCEP-TGIC systems have potential applications as electronic packaging materials in electrical and electronic fields. Epoxy ; Fire retardant ; Materials science ; Curing (chemistry) ; Limiting oxygen index ; Composite material ; Thermal stability ; Dielectric ; Chemistry ; Organic chemistry ; Combustion Flame retardant materials and properties ; Synthesis and properties of polymers ; Polymer composites and self-healing
Authors: Xu Chen | Pinyuan Li | Eugen Hruška | Fang Liu
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states Due to the limitation of solvent models, quantum chemistry calculation of solution-phase molecular properties often deviates from experimental measurements. Recently, Δ-machine learning (Δ-ML) was shown to be a promising approach to correcting errors in the quantum chemistry calculation of solvated molecules. However, this approach's applicability to different molecular properties and its performance in various cases are still unknown. In this work, we tested the performance of Δ-ML in correcting redox potential and absorption energy calculations using four types of input descriptors and various ML methods. We sought to understand the dependence of Δ-ML performance on the property to predict the quantum chemistry method, the data set distribution/size, the type of input feature, and the feature selection techniques. We found that Δ-ML can effectively correct the errors in redox potentials calculated using density functional theory (DFT) and absorption energies calculated by time-dependent DFT. For both properties, the Δ-ML-corrected results showed less sensitivity to the DFT functional choice than the raw results. The optimal input descriptor depends on the property, regardless of the specific ML method used. The solvent-solute descriptor (SS) is the best for redox potential, whereas the combined molecular fingerprint (cFP) is the best for absorption energy. A detailed analysis of the feature space and the physical foundation of different descriptors well explained these observations. Feature selection did not further improve the Δ-ML performance. Finally, we analyzed the limitation of our Δ-ML solvent effect approach in data sets with molecules of varying degrees of electronic structure errors. Excited state ; Quantum chemistry ; Quantum chemical ; Quantum ; Ground state ; Chemical physics ; Chemistry ; Phase (matter) ; Computational chemistry ; Atomic physics ; Physics ; Molecule ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Trupti Mohanty | K.S. Ravi Chandran | Taylor D. Sparks
Source: APL Machine Learning (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Machine learning guided optimal composition selection of niobium alloys for high temperature applications Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime. Alloy ; Materials science ; Quinary ; Ultimate tensile strength ; Superalloy ; Bayesian optimization ; Niobium ; Python (programming language) ; Computer science ; Mechanical engineering ; Machine learning ; Metallurgy ; Engineering Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Advanced Materials Characterization Techniques
Authors: Eric R. Beyerle | Ziyue Zou | Pratyush Tiwary
Source: Current Opinion in Solid State and Materials Science (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence Nucleation ; Artificial intelligence ; Computer science ; Crystal (programming language) ; Identification (biology) ; Graphics ; Machine learning ; Materials science ; Physics ; Computer graphics (images) ; Thermodynamics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions
Authors: Hadeel Moustafa | Peder Lyngby | Jens Jørgen Mortensen | Kristian S. Thygesen | Karsten W. Jacobsen
Source: Physical Review Materials (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Hundreds of new, stable, one-dimensional materials from a generative machine learning model We use a generative neural network model to create thousands of new one-dimensional (1D) materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of the new materials are shown with density-functional theory calculations to be dynamically stable and with heats of formation within 0.2 eV of the convex hull of known materials. Some of the new materials could also have been obtained by chemical element substitution in the training materials, but completely new classes of materials are also produced. The band structures, electronic densities of states, work functions, effective masses, and phonon spectra of the new materials are calculated, and the data are added to the C1DB. Materials science ; Convex hull ; Generative grammar ; Density functional theory ; Artificial neural network ; Phonon ; Stability (learning theory) ; Work (physics) ; Regular polygon ; Machine learning ; Artificial intelligence ; Computational chemistry ; Computer science ; Condensed matter physics ; Thermodynamics ; Geometry ; Physics ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Ayana Ghosh
Source: Computational Materials Science (article, 2023-12-23)
Field: Materials Science — Materials Chemistry

Towards physics-informed explainable machine learning and causal models for materials research Workflow ; Computer science ; Variety (cybernetics) ; Data science ; Process (computing) ; Artificial intelligence ; Key (lock) ; Perspective (graphical) ; Focus (optics) ; Encoding (memory) ; Machine learning ; Cognitive science ; Management science ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Animesh Ghose | Mikhail Segal | Fanchen Meng | Zhu Liang | Mark S. Hybertsen | Xiaohui Qu | Eli Stavitski | Shinjae Yoo | Deyu Lu | Matthew R. Carbone
Source: Physical Review Research (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty-aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the x-ray absorption spectra of small molecules, as well as their pointwise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates. Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large-scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties. Pointwise ; Artificial neural network ; Computer science ; Machine learning ; Ground truth ; Uncertainty quantification ; Artificial intelligence ; Sampling (signal processing) ; Data mining ; Mathematics ; Filter (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Munish Kumar Gupta | Mehmet Erdi Korkmaz | C. Sherin Shibi | Nimel Sworna Ross | Gurminder Singh | Recep Demirsöz | Muhammad Jamil | Grzegorz Królczyk
Source: Tribology International (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Tribological characteristics of additively manufactured 316 stainless steel against 100 cr6 alloy using deep learning Under different working conditions, the tribological characteristics of materials show a complicated and non-linear relation. As a result, it is crucial to advance tribology by prioritising a data-driven strategy to estimate service capability in order to expedite the material design and preparation. With this aim, the present work firstly deals with the implementation of novel deep learning technologies in predicting tribological characteristics of additively manufactured and casted 316 stainless steel against 100 cr6 alloy. The coefficient of friction and frictional forces data from ball-on-flat experiments were used to develop the different deep learning models i.e., CNN, CNN-LSTM, and ATTENTION based CNN. Then, the wear tracks of tested samples were analysed with the SEM analysis. According to the findings of the wear rate, the AM material wears with an average of 58% less intensity than the casted material. In addition, the performance of the CNN Attention model demonstrated higher levels of accuracy and lower loss metrics in comparison to the CNN and CNN-LSTM classifiers. Tribology ; Materials science ; Friction coefficient ; Alloy ; Service life ; Ball (mathematics) ; Deep learning ; Coefficient of friction ; Mechanical engineering ; Relation (database) ; Computer science ; Artificial intelligence ; Composite material ; Mathematics ; Engineering ; Data mining ; Geometry Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Advanced materials and composites