jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Selina K. Kaiser | Jessi E. S. van der Hoeven | George Yan | Kang Rui Garrick Lim | Hio Tong Ngan | Sadhya Garg | Mustafa Karatok | Michael Aizenberg | Joanna Aizenberg | Philippe Sautet | C. M. Friend | R. J. Madix
Source: ACS Catalysis (article, 2023-08-30)
Field: Chemistry — Organic Chemistry

Identifying the Optimal Pd Ensemble Size in Dilute PdAu Alloy Nanomaterials for Benzaldehyde Hydrogenation Unraveling metal nuclearity effects is central for active site identification and the development of high-performance heterogeneous catalysts. Herein, a platform of nanostructured palladium (Pd) in gold (Au) dilute alloy nanoparticles supported on raspberry-colloid-templated (RCT) silica was employed to systematically assess the impact of the Pd ensemble size for the low-nuclearity regime in the Au surface layer, from single atoms to clusters, on the catalytic performance in the liquid-phase hydrogenation of benzaldehyde to benzyl alcohol. Combining catalyst evaluation, detailed characterization, and mechanistic studies based on density functional theory, we show that Pd single atoms in the Au surface plane (corresponding to samples with 4 atom % Pd in Au) are virtually inactive in this reaction and benzyl alcohol production is optimal over small Pd clusters (corresponding to samples with 10–12 atom % Pd in Au) due to superior benzaldehyde adsorption and transition state stabilization for the C–H bond formation step. For larger Pd ensembles (samples with ≥10 atom % Pd in Au), C–O bond hydrogenolysis occurs, promoting toluene formation and decreasing the selectivity toward benzyl alcohol, in line with a relatively lowered C–O bond cleavage barrier. Nevertheless, the nanostructured bimetallic Pd13Au87/SiO2-RCT catalyst still outperforms monometallic Pd counterparts in terms of selectivity for benzyl alcohol over toluene at comparable conversion and rate. Furthermore, the stability is improved compared to pure Pd nanoparticles due to inhibited particle agglomeration in the RCT silica matrix. Benzyl alcohol ; Benzaldehyde ; Catalysis ; Bimetallic strip ; Palladium ; Toluene ; Chemistry ; Heterogeneous catalysis ; Selectivity ; Nanoparticle ; Materials science ; Organic chemistry ; Nanotechnology Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Zhe Li | Rongrong Hong | Zhuoyi Zhang | Haiqiang Wang | Xuanhao Wu | Zhongbiao Wu
Source: Molecules (review, 2023-05-04)
Field: Materials Science — Materials Chemistry

Single-Atom Catalysts in Environmental Engineering: Progress, Outlook and Challenges Recently, single-atom catalysts (SACs) have attracted wide attention in the field of environmental engineering. Compared with their nanoparticle counterparts, SACs possess high atomic efficiency, unique catalytic activity, and selectivity. This review summarizes recent studies on the environmental remediation applications of SACs in (1) gaseous: volatile organic compounds (VOCs) treatment, NOx reduction, CO2 reduction, and CO oxidation; (2) aqueous: Fenton-like advanced oxidation processes (AOPs), hydrodehalogenation, and nitrate/nitrite reduction. We present the treatment activities and reaction mechanisms of various SACs and propose challenges and future opportunities. We believe that this review will provide constructive inspiration and direction for future SAC research in environmental engineering. Catalysis ; NOx ; Environmental remediation ; Nitrate ; Nitrite ; Nanotechnology ; Chemistry ; Nanoparticle ; Selective catalytic reduction ; Aqueous solution ; Environmental chemistry ; Biochemical engineering ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Combustion ; Engineering ; Ecology ; Contamination Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Albert Musaelian | Anders Johansson | Simon Batzner | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2023-04-20)
Field: Materials Science — Materials Chemistry

Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combination of innovative model architecture, massive parallelization, and models and implementations optimized for efficient GPU utilization. The resulting Allegro architecture bridges the accuracy-speed tradeoff of atomistic simulations and enables description of dynamics in structures of unprecedented complexity at quantum fidelity. To illustrate the scalability of Allegro, we perform nanoseconds-long stable simulations of protein dynamics and scale up to a 44-million atom structure of a complete, all-atom, explicitly solvated HIV capsid on the Perlmutter supercomputer. We demonstrate excellent strong scaling up to 100 million atoms and 70% weak scaling to 5120 A100 GPUs. Scaling ; Scalability ; Computer science ; Supercomputer ; Computational science ; Robustness (evolution) ; Molecular dynamics ; Statistical physics ; Parallel computing ; Speedup ; Algorithm ; Physics ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Parallel Computing and Optimization Techniques
Authors: Xiaming Feng | Xiang Lin | Kaiwen Deng | Hongyu Yang | Cheng Yan
Source: Molecules (review, 2023-06-29)
Field: Materials Science — Polymers and Plastics

Facile Ball Milling Preparation of Flame-Retardant Polymer Materials: An Overview To meet the growing needs of public safety and sustainable development, it is highly desirable to develop flame-retardant polymer materials using a facile and low-cost method. Although conventional solution chemical synthesis has proven to be an efficient way of developing flame retardants, it often requires organic solvents and a complicated separation process. In this review, we summarize the progress made in utilizing simple ball milling (an important type of mechanochemical approach) to fabricate flame retardants and flame-retardant polymer composites. To elaborate, we first present a basic introduction to ball milling, and its crushing, exfoliating, modifying, and reacting actions, as used in the development of high-performance flame retardants. Then, we report the mixing action of ball milling, as used in the preparation of flame-retardant polymer composites, especially in the formation of multifunctional segregated structures. Hopefully, this review will provide a reference for the study of developing flame-retardant polymer materials in a facile and feasible way. Fire retardant ; Materials science ; Polymer ; Ball mill ; Ball (mathematics) ; Mechanochemistry ; Composite material ; Nanotechnology Flame retardant materials and properties ; Synthesis and properties of polymers ; Polymer Nanocomposites and Properties
Authors: Peter McMichael | Xavier Schultze | Henri Cramail | Frédéric Peruch
Source: Polymer Chemistry (review, 2023-01-01)
Field: Materials Science — Biomaterials

Sourcing, thermodynamics, and ring-opening (co)polymerization of substituted δ-lactones: a review Substituted δ-lactones are mostly biobased monomers with unfavorable to intermediate thermodynamic parameters. Nevertheless, they can lead to the synthesis of (co)polyesters by ring-opening (co)polymerization mediated by either organocatalysis or metal-based catalysis. Polymerization ; Monomer ; Organocatalysis ; Chemistry ; Polyester ; Ring (chemistry) ; Ring-opening polymerization ; Catalysis ; Polymer chemistry ; Organic chemistry ; Polymer ; Enantioselective synthesis biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Chemistry and Chemical Engineering
Authors: Kan Hatakeyama‐Sato | Seigo Watanabe | Naoki Yamane | Yasuhiko Igarashi | Kenichi Oyaizu
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Using GPT-4 in parameter selection of polymer informatics: improving predictive accuracy amidst data scarcity and ‘Ugly Duckling’ dilemma Data scarcity in materials informatics hinders structure–property relationships. Using GPT-4 can address challenges, improving predictions like polymer refractive indices. Scarcity ; Dilemma ; Informatics ; Property (philosophy) ; Computer science ; Selection (genetic algorithm) ; Polymer ; Materials science ; Artificial intelligence ; Mathematics ; Engineering ; Economics ; Microeconomics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Semiconductor materials and devices
Authors: Alejandro Fonseca | Aram L. Bugaev | Anna Yu. Pnevskaya | K. Janssens | Carlos Márquez | Dirk De Vos
Source: Communications Chemistry (article, 2023-07-05)
Field: Chemistry — Organic Chemistry

Copper–cobalt double metal cyanides as green catalysts for phosphoramidate synthesis Phosphoramidates are common and widespread backbones of a great variety of fine chemicals, pharmaceuticals, additives and natural products. Conventional approaches to their synthesis make use of toxic chlorinated reagents and intermediates, which are sought to be avoided at an industrial scale. Here we report the coupling of phosphites and amines promoted by a Cu<sub>3</sub>[Co(CN)<sub>6</sub>]<sub>2</sub>-based double metal cyanide heterogeneous catalyst using I<sub>2</sub> as additive for the synthesis of phosphoramidates. This strategy successfully provides an efficient, environmentally friendly alternative to the synthesis of these valuable compounds in high yields and it is, to the best of our knowledge, the first heterogeneous approach to this protocol. While the detailed study of the catalyst structure and of the metal centers by PXRD, FTIR, EXAFS and XANES revealed changes in their coordination environment, the catalyst maintained its high activity for at least 5 consecutive iterations of the reaction. Preliminary mechanism studies suggest that the reaction proceeds by a continuous change in the oxidation state of the Cu metal, induced by a O<sub>2</sub>/I<sup>-</sup> redox cycle. Catalysis ; Chemistry ; Cobalt ; Redox ; Copper ; Combinatorial chemistry ; Metal ; Reagent ; Cyanide ; Extended X-ray absorption fine structure ; Heterogeneous catalysis ; Environmentally friendly ; XANES ; Oxidation state ; Organic chemistry ; Absorption spectroscopy ; Spectroscopy Chemical Synthesis and Reactions ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Behnam Amiri-Ramsheh | Menad Nait Amar | Mohammadhadi Shateri | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2023-08-02)
Field: Materials Science — Polymers and Plastics

On the evaluation of the carbon dioxide solubility in polymers using gene expression programming Evaluation, prediction, and measurement of carbon dioxide (CO<sub>2</sub>) solubility in different polymers are crucial for engineers in various chemical applications, such as extraction and generation of novel materials. In this paper, correlations based on gene expression programming (GEP) were generated to predict the value of carbon dioxide solubility in three polymers. Results showed that the generated correlations could represent an outstanding efficiency and provide predictions for carbon dioxide solubility with satisfactory average absolute relative errors of 9.71%, 5.87%, and 1.63% for polystyrene (PS), polybutylene succinate-co-adipate (PBSA), and polybutylene succinate (PBS), respectively. Trend analysis based on Henry's law illustrated that increasing pressure and decreasing temperature lead to an increase in carbon dioxide solubility. Finally, outlier discovery was applied using the leverage approach to detect the suspected data points. The outlier detection demonstrated the statistical validity of the developed correlations. William's plot of three generated correlations showed that all of the data points are located in the valid zone except one point for PBS polymer and three points for PS polymer. Solubility ; Carbon dioxide ; Polymer ; Outlier ; Adipate ; Materials science ; Chemistry ; Computer science ; Organic chemistry ; Polymer chemistry ; Artificial intelligence Polymer Foaming and Composites ; Injection Molding Process and Properties ; Phase Equilibria and Thermodynamics
Authors: Jackson Burns | Kevin Spiekermann | Himaghna Bhattacharjee | Dionisios G. Vlachos | William H. Green
Source: The Journal of Open Source Software (article, 2023-11-05)
Field: Materials Science — Materials Chemistry

Machine Learning Validation via Rational DatasetSampling with astartes Machine Learning (ML) has become an increasingly popular tool to accelerate traditional workflows. Critical to the use of ML is the process of splitting datasets into training, validation, and testing subsets that are used to develop and evaluate models. Common practice in the literature is to assign these subsets randomly. Although this approach is fast and efficient, it only measures a model’s capacity to interpolate. Testing errors from random splits may be overly optimistic if given new data that is dissimilar to the scope of the training set; thus, there is a growing need to easily measure performance for extrapolation tasks. To address this issue, we report astartes, an open-source Python package that implements many similarityand distance-based algorithms to partition data into more challenging splits. Separate from astartes, users can then use these splits to better assess out-of-sample performance with any ML model of choice. This publication focuses on use-cases within cheminformatics. However, astartes operates on arbitrary vector inputs, so its principals and workflow are generalizable to other ML domains as well. astartes is available via the Python package managers pip and conda and is publicly hosted on GitHub (github.com/JacksonBurns/astartes). Computer science ; Sampling (signal processing) ; Machine learning ; Artificial intelligence ; Computer vision Machine Learning in Materials Science ; Machine Learning and Data Classification ; Fault Detection and Control Systems
Authors: Chen Ling
Source: Chem (article, 2023-08-31)
Field: Chemistry — Inorganic Chemistry

Phonon-promoted superionic conduction in fluorite-structured compounds Thermal conduction ; Phonon ; Chemical physics ; Metastability ; Ion ; Fast ion conductor ; Fluorite ; Condensed matter physics ; Ionic bonding ; Lattice (music) ; Materials science ; Ionic conductivity ; Chemistry ; Crystallography ; Physics ; Physical chemistry Inorganic Fluorides and Related Compounds ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: He Cao | Zijing Liu | Xingyu Lu | Yuan Yao | Yu Li
Source: arXiv (Cornell University) (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data. Our novel contribution, InstructMol, a multi-modal LLM, effectively aligns molecular structures with natural language via an instruction-tuning approach, utilizing a two-stage training strategy that adeptly combines limited domain-specific data with molecular and textual information. InstructMol showcases substantial performance improvements in drug discovery-related molecular tasks, surpassing leading LLMs and significantly reducing the gap with specialized models, thereby establishing a robust foundation for a versatile and dependable drug discovery assistant. Drug discovery ; Computer science ; Modal ; Generalization ; Domain (mathematical analysis) ; Data science ; Artificial intelligence ; Bioinformatics ; Chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Nonappa Nonappa
Source: Chemical Communications (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Precision nanoengineering for functional self-assemblies across length scales As nanotechnology continues to push the boundaries across disciplines, there is an increasing need for engineering nanomaterials with atomic-level precision for self-assembly across length scales, <i>i.e.</i>, from the nanoscale to the macroscale. Although molecular self-assembly allows atomic precision, extending it beyond certain length scales presents a challenge. Therefore, the attention has turned to size and shape-controlled metal nanoparticles as building blocks for multifunctional colloidal self-assemblies. However, traditionally, metal nanoparticles suffer from polydispersity, uncontrolled aggregation, and inhomogeneous ligand distribution, resulting in heterogeneous end products. In this feature article, I will discuss how virus capsids provide clues for designing subunit-based, precise, efficient, and error-free self-assembly of colloidal molecules. The atomically precise nanoscale proteinic subunits of capsids display rigidity (conformational and structural) and patchy distribution of interacting sites. Recent experimental evidence suggests that atomically precise noble metal nanoclusters display an anisotropic distribution of ligands and patchy ligand bundles. This enables symmetry breaking, consequently offering a facile route for two-dimensional colloidal crystals, bilayers, and elastic monolayer membranes. Furthermore, inter-nanocluster interactions mediated <i>via</i> the ligand functional groups are versatile, offering routes for discrete supracolloidal capsids, composite cages, toroids, and macroscopic hierarchically porous frameworks. Therefore, engineered nanoparticles with atomically precise structures have the potential to overcome the limitations of molecular self-assembly and large colloidal particles. Self-assembly allows the emergence of new optical properties, mechanical strength, photothermal stability, catalytic efficiency, quantum yield, and biological properties. The self-assembled structures allow reproducible optoelectronic properties, mechanical performance, and accurate sensing. More importantly, the intrinsic properties of individual nanoclusters are retained across length scales. The atomically precise nanoparticles offer enormous potential for next-generation functional materials, optoelectronics, precision sensors, and photonic devices. Nanoengineering ; Nanotechnology ; Length scale ; Materials science ; Physics ; Mechanics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Annick De Backer | Sara Bals | Sandra Van Aert
Source: Ultramicroscopy (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

A decade of atom-counting in STEM: From the first results toward reliable 3D atomic models from a single projection Quantitative structure determination is needed in order to study and understand nanomaterials at the atomic scale. Materials characterisation resulting in precise structural information is a crucial point to understand the structure-property relation of materials. Counting the number of atoms and retrieving the 3D atomic structure of nanoparticles plays an important role here. In this paper, an overview will be given of the atom-counting methodology and its applications over the past decade. The procedure to count the number of atoms will be discussed in detail and it will be shown how the performance of the method can be further improved. Furthermore, advances toward mixed element nanostructures, 3D atomic modelling based on the atom-counting results, and quantifying the nanoparticle dynamics will be highlighted. Atom (system on chip) ; Nanomaterials ; Nanotechnology ; Atomic units ; Nanostructure ; Characterization (materials science) ; Atomic number ; Nanoparticle ; Projection (relational algebra) ; Atom probe ; Point (geometry) ; Materials science ; Computer science ; Physics ; Atomic physics ; Mathematics ; Algorithm ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Yajie Tian | Jieling Li | Anhe Wang | Qi Li | Honglei Jian | Shuo Bai
Source: Macromolecular Bioscience (review, 2023-07-19)
Field: Materials Science — Biomaterials

Peptide‐Based Optical/Electronic Materials: Assembly and Recent Applications in Biomedicine, Sensing, and Energy Storage The unique optical and electronic properties of living systems are impressive. Peptide-based supramolecular self-assembly systems attempt to mimic these properties by preparation optical/electronic function materials with specific structure through simple building blocks, rational molecular design, and specific kinetic stimulation. From the perspective of building blocks and assembly strategies, the unique optical and electronic properties of peptide-based nanostructures, including peptides self-assembly and peptides regulate the assembly of external function subunits, are systematically reviewed. Additionally, their applications in biomedicine, sensing, and energy storage are also highlighted. This bioinspired peptide-based function material is one of the hot candidates for the new generation of green intellect materials, with many advantages such as biocompatibility, environmental friendliness, and adjustable morphology. Nanotechnology ; Biomedicine ; Supramolecular chemistry ; Nanostructure ; Biocompatibility ; Materials science ; Biocompatible material ; Peptide ; Function (biology) ; Chemistry ; Engineering ; Bioinformatics ; Biomedical engineering Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Polydiacetylene-based materials and applications
Authors: Georgios P. Gakis | Ioannis G. Aviziotis | Costas A. Charitidis
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A structure–activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of <i>in vivo</i> and <i>in vitro</i> toxicity assessment methods for ENMs, has led to emerging <i>in silico</i> techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity. In silico ; Nanomaterials ; Toxicity ; Nanotoxicology ; Nanotechnology ; Metal toxicity ; Biochemical engineering ; Computer science ; Environmental chemistry ; Chemistry ; Materials science ; Nanoparticle ; Engineering ; Biochemistry Nanoparticles: synthesis and applications ; Thermal and Kinetic Analysis ; Electrochemical Analysis and Applications