jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Praveen Kumar Saravanan | Dinesh Bhalothia | Guo-Heng Huang | Amisha Beniwal | Mingxing Cheng | Yu-Chieh Chao | Ming-Wei Lin | Po‐Chun Chen | Tsan‐Yao Chen
Source: Nanomaterials (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Sub-Millisecond Laser-Irradiation-Mediated Surface Restructure Boosts the CO Production Yield of Cobalt Oxide Supported Pd Nanoparticles The catalytic conversion of CO<sub>2</sub> into valuable commodities has the potential to balance ongoing energy and environmental issues. To this end, the reverse water-gas shift (RWGS) reaction is a key process that converts CO<sub>2</sub> into CO for various industrial processes. However, the competitive CO<sub>2</sub> methanation reaction severely limits the CO production yield; therefore, a highly CO-selective catalyst is needed. To address this issue, we have developed a bimetallic nanocatalyst comprising Pd nanoparticles on the cobalt oxide support (denoted as CoPd) via a wet chemical reduction method. Furthermore, the as-prepared CoPd nanocatalyst was exposed to sub-millisecond laser irradiation with per-pulse energies of 1 mJ (denoted as CoPd-1) and 10 mJ (denoted as CoPd-10) for a fixed duration of 10 s to optimize the catalytic activity and selectivity. For the optimum case, the CoPd-10 nanocatalyst exhibited the highest CO production yield of ∼1667 μmol g<sup>-1</sup><sub>catalyst</sub>, with a CO selectivity of ∼88% at a temperature of 573 K, which is a 41% improvement over pristine CoPd (~976 μmol g<sup>-1</sup><sub>catalyst</sub>). The in-depth analysis of structural characterizations along with gas chromatography (GC) and electrochemical analysis suggested that such a high catalytic activity and selectivity of the CoPd-10 nanocatalyst originated from the sub-millisecond laser-irradiation-assisted facile surface restructure of cobalt oxide supported Pd nanoparticles, where atomic CoOx species were observed in the defect sites of the Pd nanoparticles. Such an atomic manipulation led to the formation of heteroatomic reaction sites, where atomic CoOx species and adjacent Pd domains, respectively, promoted the CO<sub>2</sub> activation and H<sub>2</sub> splitting steps. In addition, the cobalt oxide support helped to donate electrons to Pd, thereby enhancing its ability of H<sub>2</sub> splitting. These results provide a strong foundation to use sub-millisecond laser irradiation for catalytic applications. Catalysis ; Cobalt ; Bimetallic strip ; Materials science ; Nanoparticle ; Cobalt oxide ; Selectivity ; Yield (engineering) ; Oxide ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Peter A. Macdonald | Sumanta Banerjee | Alan R. Kennedy | Alexander van Teijlingen | Stuart D. Robertson | Tell Tuttle | Robert E. Mulvey
Source: Angewandte Chemie (article, 2023-05-03)
Field: Chemistry — Inorganic Chemistry

Alkali Metal Dihydropyridines in Transfer Hydrogenation Catalysis of Imines: Amide Basicity versus Hydride Surrogacy Abstract Catalytic reduction of a representative set of imines, both aldimines and ketimines, to amines has been studied using transfer hydrogenation from 1,4‐dicyclohexadiene. Unusually, this has been achieved using s‐block pre‐catalysts, namely 1‐metallo‐2‐ tert ‐butyl‐1,2‐dihydropyridines, 2‐ t BuC 5 H 5 NM, M( t BuDHP), where M=Li–Cs. Reactions have been monitored in C 6 D 6 and tetrahydrofuran‐d 8 (THF‐d 8 ). A definite trend is observed in catalyst efficiency with the heavier alkali metal t BuDHPs outperforming the lighter congeners. In general, Cs( t BuDHP) is the optimal pre‐catalyst with, in the best cases, reactions producing quantitative yields of amines in minutes at room temperature using 5 mol % catalyst. Supporting the experimental study, Density Functional Theory (DFT) calculations have also been carried out which reveal that Cs has a pathway with a significantly lower rate determining step than the Li congener. In the postulated initiation pathways DHP can act as either a base or as a surrogate hydride. Chemistry ; Catalysis ; Hydride ; Tetrahydrofuran ; Amide ; Aldimine ; Alkali metal ; Base (topology) ; Homogeneous catalysis ; Medicinal chemistry ; Inorganic chemistry ; Metal ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions ; Chemical Synthesis and Analysis
Authors: Yi Jiang | Maidina Aireti | Xudong Leng | Xu Ji | Jing Liu | X.Y. Cui | Haiming Duan | Qun Jing | Haibin Cao
Source: Nanomaterials (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Structures, Electronic, and Magnetic Properties of CoKn (n = 2–12) Clusters: A Particle Swarm Optimization Prediction Jointed with First-Principles Investigation Transition-metal-doped clusters have long been attracting great attention due to their unique geometries and interesting physical and/or chemical properties. In this paper, the geometries of the lowest- and lower-energy CoK<i><sub>n</sub></i> (<i>n</i> = 2-12) clusters have been screened out using particle swarm optimization and first principles relaxation. The results show that except for CoK<sub>2</sub> the other CoK<i><sub>n</sub></i> (<i>n</i> = 3-12) clusters are all three-dimensional structures, and CoK<sub>7</sub> is the transition structure from which the lowest energy structures are cobalt atom-centered cage-like structures. The stability, the electronic structures, and the magnetic properties of CoK<i><sub>n</sub></i> clusters (<i>n</i> = 2-12) clusters are further investigated using the first principles method. The results show that the medium-sized clusters whose geometries are cage-like structures are more stable than smaller-sized clusters. The electronic configuration of CoK<i><sub>n</sub></i> clusters could be described as 1S1P1D according to the spherical jellium model. The main components of petal-shaped D molecular orbitals are Co-d and K-s states or Co-d and Co-s states, and the main components of sphere-like S molecular orbitals or spindle-like P molecular orbitals are K-s states or Co-s states. Co atoms give the main contribution to the total magnetic moments, and K atoms can either enhance or attenuate the total magnetic moments. CoK<i><sub>n</sub></i> (<i>n</i> = 5-8) clusters have relatively large magnetic moments, which has a relation to the strong Co-K bond and the large amount of charge transfer. CoK<sub>4</sub> could be a magnetic superatom with a large magnetic moment of 5 μ<sub>B.</sub> Superatom ; Magnetic moment ; Jellium ; Atomic orbital ; Electronic structure ; Molecular orbital ; Atomic physics ; Atom (system on chip) ; Molecular physics ; Materials science ; Chemistry ; Condensed matter physics ; Physics ; Molecule ; Metal ; Quantum mechanics Graphene research and applications ; Nanocluster Synthesis and Applications ; Advancements in Battery Materials
Authors: Shubham Jamdade | Rishi Gurnani | Hanjun Fang | Salah Eddine Boulfelfel | Rampi Ramprasad | David S. Sholl
Source: Industrial & Engineering Chemistry Research (article, 2023-01-18)
Field: Chemistry — Inorganic Chemistry

Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening Metal–organic frameworks (MOFs) are an important class of porous crystalline materials for applications ranging from gas adsorption and separation to catalysis. There are thousands of potential MOFs available for separation applications. We developed a computational approach to screen MOFs for the separation of oxygen–helium mixtures at low temperatures (100–200 K), conditions that were motivated by issues associated with propulsion in space-based settings. We used detailed molecular simulations for a small number of MOFs to develop screening methods that were then used to estimate the optimum temperatures for separations using pressure swing adsorption for 2932 MOFs from the CoRE MOF database and the swing capacity and oxygen–helium selectivity at these temperatures. We used the stability of the best-performing structures in the presence of moisture as a means to provide a short list of high-performance materials. In addition to identifying specific materials for oxygen–helium separations, this approach could prove useful for selecting adsorbents for other gas separations. Helium ; Pressure swing adsorption ; Adsorption ; Metal-organic framework ; Materials science ; Oxygen ; Gas separation ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Spacecraft and Cryogenic Technologies ; Inorganic Fluorides and Related Compounds
Authors: Maxime Van Haeverbeke | Charlotte Cums | Thijs Vackier | Dries Braeken | Michiel Stock | Hans Steenackers | Bernard De Baets
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-12-20)
Field: Chemistry — Electrochemistry

Impedimetric biofilm characterization with microelectrode arrays using equivalent electrical circuit features and ensemble classifiers Microelectrode ; Dielectric spectroscopy ; Biofilm ; Biological system ; Multielectrode array ; Equivalent circuit ; Materials science ; Electrical impedance ; Computer science ; Electrode ; Pattern recognition (psychology) ; Artificial intelligence ; Electrochemistry ; Chemistry ; Biology ; Engineering ; Electrical engineering Electrochemical Analysis and Applications ; Biosensors and Analytical Detection ; Bacterial biofilms and quorum sensing
Authors: Taein Lee | Sayak Subhra Panda | Grant E. K. Hall | Yunjia Song | John D. Tovar | Howard E. Katz
Source: Materials Advances (article, 2023-01-01)
Field: Materials Science — Biomaterials

Relation among absorbance shifts, mineralization morphology, and electronic conductivity of π-peptide aggregates with different amino acid residues Three conceivably unrelated observations on peptide assemblies; absorbances, morphologies, and conductivities are interrelated. An alanine methyl group was sufficient to alter all three observations, a steric effect from a small perturbation. Steric effects ; Absorbance ; Peptide ; Mineralization (soil science) ; Chemistry ; Alanine ; Amino acid residue ; Conductivity ; Amino acid ; Biophysics ; Crystallography ; Stereochemistry ; Peptide sequence ; Biochemistry ; Chromatography ; Organic chemistry ; Biology ; Physical chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Lipid Membrane Structure and Behavior
Authors: Hidetoshi Fukuyama
Source: Annual Review of Condensed Matter Physics (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

“More Is Different” and Sustainable Development Goals: Thermoelectricity The thermal Green's function formalism bridging between macroscopic observables and microscopic processes via linear response theory was established in the early 1960s, when I started my research career. I recall stimulating experiences with the help of this technique in exploring transport and thermodynamic properties of Bloch electrons in magnetic fields, especially orbital magnetism and the Hall effect, and this technique is useful for understanding narrow gap systems like Dirac and Weyl electrons, which are of current interest. Recent ongoing challenges on thermoelectricity based on the Kubo–Luttinger formula are briefly introduced; it is an important scientific issue in view of sustainable development goals that awaits contributions from condensed matter physics, where the thermal Green's function is again a powerful tool. Physics ; Thermoelectric effect ; Observable ; Electron ; Magnetism ; Condensed matter physics ; Formalism (music) ; Theoretical physics ; Bridging (networking) ; Engineering physics ; Quantum mechanics ; Computer science Advanced Thermoelectric Materials and Devices ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Ziyang Wei | George Yan | Philippe Sautet
Source: The Journal of Chemical Physics (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Toward more accurate surface properties of ceria using many-body perturbation theory Despite the wide applications, the ab initio modeling of the ceria based catalyst is challenging. The partial occupation in the 4f orbitals creates a fundamental challenge for commonly used density functional theory (DFT) methods, including semilocal functionals with Hubbard U correction to force localization and hybrid functionals. In this work, we benchmark the random phase approximation (RPA) for ceria surface properties, including surface energy and hydrogenation energy, compared to the results utilizing the DFT + U approach or hybrid functionals. We show that, for the latter approaches, different surface properties require opposite directions of parameter tuning. This forms a dilemma for the parameter based DFT methods, as the improvement of a certain property by tuning parameters will inevitably lead to the worsening of other properties. Our results suggest that the parameter-free many-body perturbation theory methods exemplified by RPA are a promising strategy to escape the dilemma and provide highly accurate descriptions, which will allow us to better understand the catalytic reactions in ceria related systems. Density functional theory ; Ab initio ; Perturbation theory (quantum mechanics) ; Hybrid functional ; Atomic orbital ; Benchmark (surveying) ; Statistical physics ; Perturbation (astronomy) ; Surface (topology) ; Materials science ; Computer science ; Physics ; Computational chemistry ; Quantum mechanics ; Mathematics ; Chemistry Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies ; Advanced Condensed Matter Physics
Authors: Mohd Zaki | Jayadeva | Mausam Mausam | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials domain that can evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials student who has cleared their undergraduate degree. We classify these questions based on their structure and the materials science domain-based subcategories. Further, we evaluate the performance of GPT-3.5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting. It is observed that GPT-4 gives the best performance (~62% accuracy) as compared to GPT-3.5. Interestingly, in contrast to the general observation, no significant improvement in accuracy is observed with the chain of thought prompting. To evaluate the limitations, we performed an error analysis, which revealed conceptual errors (~64%) as the major contributor compared to computational errors (~36%) towards the reduced performance of LLMs. We hope that the dataset and analysis performed in this work will promote further research in developing better materials science domain-specific LLMs and strategies for information extraction. Benchmark (surveying) ; Computer science ; Domain (mathematical analysis) ; Clearance ; Domain knowledge ; Comprehension ; Question answering ; Knowledge base ; Knowledge extraction ; Information extraction ; Data science ; Contrast (vision) ; Natural language processing ; Information retrieval ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Guomin Zhang | Vipul Mann | Venkat Venkatasubramanian
Source: arXiv (Cornell University) (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer Various template-based and template-free approaches have been proposed for single-step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data-driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry-aware retrosynthesis prediction framework that combines powerful data-driven models with prior domain knowledge. We present a tree-to-sequence transformer architecture that utilizes hierarchical SMILES grammar-based trees, incorporating crucial chemistry information that is often overlooked by SMILES text-based representations, such as local structures and functional groups. The proposed framework, grammar-based molecular attention tree transformer (G-MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G-MATT achieves a promising top-1 accuracy of 51% (top-10 accuracy of 79.1%), invalid rate of 1.5%, and bioactive similarity rate of 74.8% on the USPTO- 50K dataset. Additional analyses of G-MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures. Retrosynthetic analysis ; Transformer ; Grammar ; Computer science ; Artificial intelligence ; Tree (set theory) ; Natural language processing ; Machine learning ; Chemistry ; Engineering ; Linguistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Bingchao Qin | Li‐Dong Zhao
Source: Materials Lab (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Progress and Challenges for Thermoelectric Cooling: From Materials and Devices to Manifold Applications Due to the unique advantages of precise temperature control, fast response, noiselessness, miniaturization, and eco-friendliness, thermoelectric cooling (TEC) technology has been recognized as an optimal solution to mitigate the global warming and energy crisis issues, as well as being an effective alternative for thermal management. In this review, we started with the discussion of the current TEC materials and devices, and then provided an extensive summary of the manifold applications of TEC technology including electronic thermal management, electric vehicles, zero energy buildings, medical treatments, and civil applications, etc. Finally, challenging aspects of TEC materials and devices, as well as the possible research directions for future applications in various fields for TEC technology, were proposed, providing important prospect and significant guidance for TEC community. Manifold (fluid mechanics) ; Thermoelectric effect ; Materials science ; Engineering physics ; Thermoelectric cooling ; Mechanical engineering ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Heat Transfer and Optimization
Authors: Yuta Mizuno | Tamiki Komatsuzaki
Source: arXiv (Cornell University) (preprint, 2023-08-08)
Field: Materials Science — Materials Chemistry

Finding Optimal Pathways in Chemical Reaction Networks Using Ising Machines Finding optimal pathways in chemical reaction networks is essential for elucidating and designing chemical processes, with significant applications such as synthesis planning and metabolic pathway analysis. Such a chemical pathway-finding problem can be formulated as a constrained combinatorial optimization problem, aiming to find an optimal combination of chemical reactions connecting starting materials to target materials in a given network. Due to combinatorial explosion, the computation time required to find an optimal pathway increases exponentially with the network size. Ising machines, including quantum and simulated annealing devices, are promising novel computers dedicated to such hard combinatorial optimization. However, to the best of our knowledge, there has yet to be an attempt to apply Ising machines to chemical pathway-finding problems. In this article, we present the first Ising/quantum computing application for chemical pathway-finding problems. The Ising model, translated from a chemical pathway-finding problem, involves several types of penalty terms for violating constraints. It is not obvious how to set appropriate penalty strengths of different types. To address this challenge, we employ Bayesian optimization for parameter tuning. Furthermore, we introduce a novel technique that enhances tuning performance by grouping penalty terms according to the underlying problem structure. The performance evaluation and analysis of the proposed algorithm were conducted using a D-Wave Advantage system and simulated annealing. The benchmark results reveal challenges in finding exact optimal pathways. Concurrently, the results indicate the feasibility of finding approximate optimal pathways, provided that a certain degree of relative error in cost value is acceptable. Simulated annealing ; Computer science ; Quantum annealing ; Ising model ; Benchmark (surveying) ; Computation ; Mathematical optimization ; Quantum ; Quantum computer ; Algorithm ; Mathematics ; Statistical physics Machine Learning in Materials Science ; Bayesian Modeling and Causal Inference ; Quantum Computing Algorithms and Architecture
Authors: Daniele Rosa‐Gastaldo | Andrea Dalla Valle | Tommaso Marchetti | Luca Gabrielli
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Biomaterials

Sequence-selective duplex formation and template effect in recognition-encoded oligoanilines A new family of duplex-forming recognition encoded oligomers, capable of sequence selective duplex formation and template directed synthesis, was developed. Monomers equipped with both amine and aldehyde groups were functionalized with 2-trifluoromethylphenol or phosphine oxide as H-bond recognition units. Duplex formation and assembly properties of homo- and hetero-oligomers were studied by <sup>19</sup>F and <sup>1</sup>H NMR experiments in chloroform. The designed backbone prevents the undesired 1,2-folding allowing sequence-selective duplex formation, and the stability of the antiparallel duplex is 3-fold higher than the parallel arrangement. Dynamic combinatorial chemistry was exploited for the templated synthesis of complementary oligomers, showing that an aniline dimer can template the formation of the complementary imine. The key role of the H-bond recognition confers to the system the ability to discriminate a mutated donor monomer incapable of H-bonding. Sequence selective duplex formation combined with the template effect makes this system an attractive target for further studies. Duplex (building) ; Sequence (biology) ; Chemistry ; Computational biology ; Stereochemistry ; Biology ; DNA ; Biochemistry Supramolecular Self-Assembly in Materials ; Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes