jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yun Fan | Yuelei Bai | Zhiyao Lü | Zhaoxu Sun | Yuchen Liu | Simiao Sha | Yiran Li | Bin Liu
Source: Journal of Materiomics (article, 2024-08-03)
Field: Materials Science — Materials Chemistry

Exploring high-performance environmental barrier coatings for rare earth silicates: A combined approach of first principles calculations and machine learning RE2Si2O7 is promising materials for environmental barrier coating (EBC), but the vast phase space poses challenges for the screening of RE2Si2O7. It follows that a combined approach of first principles calculations and machine learning is proposed for this problem, with establishing a comprehensive database comprising β-, γ- and δ-RE2Si2O7 (RE=La–Lu, Y, Sc) and correlating their mechanical/thermal properties on structural characteristics. It is revealed the [O3Si–O–SiO3] structure and polyhedron distortion affect mechanical properties of RE2Si2O7, while criteria for selecting RE2Si2O7 with low thermal conductivity are identified, including complex crystal structures, chemical bond inhomogeneity, and strong non-harmonic lattice vibrations. Also, the machine learning model accurately predicts the coefficient of thermal expansion (CTE) and minimum thermal conductivity (λmin) of RE2Si2O7, with volume and mass variations identified as critical factors, respectively. This integrated approach efficiently screens RE2Si2O7 for EBC application and enables rapid assessments of their thermal properties. Rare earth ; Materials science ; Earth (classical element) ; Astrobiology ; Nanotechnology ; Engineering physics ; Metallurgy ; Engineering ; Physics Magnesium Oxide Properties and Applications ; Nuclear materials and radiation effects ; Catalytic Processes in Materials Science
Authors: Guojing Xu | Feng Cheng | Hao Lü | Chao Hou | Xiaoyan Song
Source: Acta Materialia (article, 2024-05-20)
Field: Materials Science — General Materials Science

Predicting the Curie temperature of Sm-Co-based alloys via data-driven strategy Materials science ; Curie temperature ; Curie ; Thermodynamics ; Condensed matter physics ; Ferromagnetism Metallurgical and Alloy Processes ; Powder Metallurgy Techniques and Materials ; Advanced Materials Characterization Techniques
Authors: M. Sadeq Adnan | Majid Safaei-Farouji | Behnam Amiri-Ramsheh | Abdolhossein Hemmati‐Sarapardeh
Source: Geoenergy Science and Engineering (article, 2024-01-23)
Field: Chemistry — Analytical Chemistry

Modeling apparent viscosity of waxy crude oils doped with polymers using tree-based models, radial basis function neural networks, and Gaussian process regression Artificial neural network ; Biological system ; Tree (set theory) ; Viscosity ; Basis (linear algebra) ; Gaussian process ; Process (computing) ; Radial basis function ; Function (biology) ; Gaussian ; Artificial intelligence ; Statistical physics ; Computer science ; Mathematics ; Materials science ; Chemistry ; Physics ; Biology ; Computational chemistry ; Composite material ; Mathematical analysis ; Evolutionary biology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Ping Hu | Ming Lei | Zhi‐Jun Sui | Xinggui Zhou | De Chen | Yi‐An Zhu
Source: ACS Catalysis (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

Identifying the Active Phase on Atomically Dispersed Catalysts for Propane Dehydrogenation: Positively Charged vs Metallic Transition Metals Atomically dispersed transition-metal catalysts have received increasing research interest in heterogeneous catalysis. However, the nature of the real active phase, specifically how the oxidation state of active species may affect the catalytic performance, remains elusive. In this work, ab initio molecular dynamics and large-scale molecular dynamics simulations based on neural network potentials have been employed to assess the structural stability of 52 single- and dual-atom catalysts with transition metals including Mn–Cu, Ru–Ag, and Os–Au embedded in the metal or oxygen vacancies on the defective TiO2 surface. On the thermodynamically stable surfaces, microkinetic analysis combined with results from DFT calculations indicates the metal atoms stabilized in the Ti vacancies with a positive oxidation state generally promote propane dehydrogenation (PDH) with the assistance of adjacent O sites, whereas those in the O vacancies exhibiting metallic properties act as a sole active site for C–H bond activation. The scaling relations established show that the adsorption energies of H and H&H can be used as two simple but effective PDH activity descriptors across both positively charged and metallic metal-doped surfaces. The calculated TOF under the realistic experimental conditions reaches a maximum at a slightly negative oxidation state, implying the Pt and Ir in the metallic state would dominate the kinetics of PDH. Moreover, a high selectivity toward propylene may be attained because the scaling relation between the activation energies for the C–H bond breaking in propane and propylene is broken in the absence of multiple metallic metal–metal sites on the atomically dispersed catalysts. An understanding of this structure–activity relationship is of vital importance for the rational design and optimization of heterogeneous catalysts for light alkane dehydrogenation. Dehydrogenation ; Catalysis ; Propane ; Transition metal ; Metal ; Oxidation state ; Density functional theory ; Chemistry ; Molecular dynamics ; Physical chemistry ; Adsorption ; Propene ; Materials science ; Active site ; Chemical physics ; Photochemistry ; Inorganic chemistry ; Computational chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Timothy Manda | Godfrey Okumu Barasa | Hitler Louis | Ahmad Irfan | John Onyango Agumba | Solomon Omwoma Lugasi | Anthony M.S. Pembere
Source: Journal of Molecular Modeling (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

A data-guided approach for the evaluation of zeolites for hydrogen storage with the aid of molecular simulations Hydrogen storage ; Gravimetric analysis ; Zeolite ; Hydrogen ; Adsorption ; Materials science ; Porosity ; Nanotechnology ; Chemical engineering ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry ; Composite material ; Engineering Hydrogen Storage and Materials ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: Gabriel Vogel | Jana M. Weber
Source: Chemical Science (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Inverse design of copolymers including stoichiometry and chain architecture The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discovery. However, the scarcity of labeled polymer data and the complex hierarchical structure of synthetic polymers make generative design particularly challenging. We advance the current state-of-the-art approaches to generate not only repeating units, but monomer ensembles including their stoichiometry and chain architecture. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Using a semi-supervised setup, we enable the handling of partly labelled datasets which can be beneficial for domains with a small corpus of labelled data. Our model learns a continuous, well organized latent space (LS) that enables <i>de novo</i> generation of copolymer structures including different monomer stoichiometries and chain architectures. In an inverse design case study, we demonstrate our model for <i>in silico</i> discovery of novel conjugated copolymer photocatalysts for hydrogen production using optimization of the polymer's electron affinity and ionization potential in the latent space. Copolymer ; Stoichiometry ; Monomer ; Chain (unit) ; Inverse ; Computer science ; Materials science ; Chemistry ; Mathematics ; Organic chemistry ; Polymer ; Physics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Computational Drug Discovery Methods
Authors: Fuguo Ge | Yonghui Gao | Yujie Jiang | Yijie Yu | Qiang Bai | Yun Liu | Huibin Li | Ning Sui
Source: Colloids and Surfaces B Biointerfaces (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Design and performance analysis of multi-enzyme activity-doped nanozymes assisted by machine learning Computer science ; Chemistry ; Nanotechnology ; Materials science Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Thomas P. Matson | Christopher A. Schuh
Source: Acta Materialia (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

A “bond-focused” local atomic environment representation for a high throughput solute interaction spectrum analysis Grain boundary ; Binary number ; Representation (politics) ; Atomic units ; Materials science ; Focus (optics) ; Construct (python library) ; Scale (ratio) ; Boundary (topology) ; Computer science ; Statistical physics ; Chemical physics ; Physics ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Susanta Das | Kenneth M. Merz
Source: Journal of Chemical Information and Modeling (article, 2024-01-11)
Field: Chemistry — Spectroscopy

Molecular Gas-Phase Conformational Ensembles Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision. Maxima and minima ; Gas phase ; Computer science ; Workflow ; Chemical space ; Force field (fiction) ; Molecular dynamics ; Drug discovery ; Conformational ensembles ; Chemistry ; Biochemical engineering ; Computational chemistry ; Artificial intelligence ; Mathematics ; Engineering ; Database Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Karim Ali Shah | Rodrigo Q. Albuquerque | Christian Brütting | Holger Ruckdäschel
Source: Journal of Applied Polymer Science (article, 2024-08-26)
Field: Materials Science — Polymers and Plastics

Machine learning‐based time series analysis of polylactic acid bead foam extrusion Abstract Understanding the behavior of polymer melts during extrusion is essential for optimizing processes and developing new materials. However, analyzing the continuous data generated by an extruder poses significant challenges. This paper investigates the utility of machine learning in predicting melt pressure at the die plate in polylactic acid (PLA) bead foam extrusion, a critical parameter in the extrusion process. Utilizing a random forest (RF) model, we examine how various processing parameters influence melt pressure. By segmenting the data into time‐delayed intervals, we achieve accurate predictions. We present forecasts of melt pressure at the die for intervals of 5 s, 1 min, and 5 min, demonstrating particularly strong performance for the 5‐min forecast with a Mean Absolute Error ( MAE ) of 1.88 and the coefficient of determination ( score) of 0.90. By exploring time series data, our study demonstrates the effectiveness of the RF model and provides a foundation for more advanced and precise control strategies in polymer bead extrusion processes. Polylactic acid ; Extrusion ; Bead ; Series (stratigraphy) ; Materials science ; Composite material ; Polymer science ; Polymer ; Geology Polymer Foaming and Composites
Authors: Deepa Sreedev | Subila Kurukkal Balakrishnan | Nandakumar Kalarikkal
Source: ACS Applied Engineering Materials (article, 2024-06-06)
Field: Materials Science — Biomaterials

A Machine Learning Aided Yield Prediction Model for the Preparation of Cellulose Nanocrystals Machine learning is one of the most innovative tools that has entered the materials science toolkit in recent years. This work employs a machine learning strategy to develop a yield prediction model for producing cellulose nanocrystals (CNCs). It analyses the critical factors affecting the yield from CNCs by optimizing reaction conditions and reducing experiments. First, a data set of CNCs is established, including cellulose sources and reaction conditions. The Weighted Average Ensemble (WAE) approach is applied to an ensemble of five tree-based base models on the data set, and it was found that the WAE surpasses all the base models. The impact of critical features on yield prediction is analyzed with partial dependence plots and individual conditional expectation plots. Batch experiments are mainly used to produce CNCs, but these are time-consuming. In this context, the WAE model is a promising tool for rapidly predicting the yield, and this study provides an excellent gateway to improve the extraction of CNCs with high yields. Yield (engineering) ; Context (archaeology) ; Computer science ; Machine learning ; Base (topology) ; Set (abstract data type) ; Cellulose ; Artificial intelligence ; Process engineering ; Mathematics ; Materials science ; Engineering ; Chemical engineering Advanced Cellulose Research Studies ; Advanced Data Storage Technologies ; Biofuel production and bioconversion
Authors: José Ferraz-Caetano | Filipe Teixeira | M. Natália D. S. Cordeiro
Source: Chemosphere (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Data-driven, explainable machine learning model for predicting volatile organic compounds’ standard vaporization enthalpy The accurate prediction of standard vaporization enthalpy (Δ<sub>vap</sub>H<sub>m</sub>°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting Δ<sub>vap</sub>H<sub>m</sub>° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict Δ<sub>vap</sub>H<sub>m</sub>° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' Δ<sub>vap</sub>H<sub>m</sub>° with a mean absolute error of 3.02 kJ mol<sup>-1</sup> and a 95% test score. The model was successfully validated through the prediction of Δ<sub>vap</sub>H<sub>m</sub>° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs. Vaporization ; Enthalpy of vaporization ; Enthalpy ; Chemistry ; Standard enthalpy change of formation ; Volatile organic compound ; Thermodynamics ; Environmental chemistry ; Chromatography ; Standard enthalpy of formation ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Thantip Roongcharoen | Giorgio Conter | Luca Sementa | Giacomo Melani | Alessandro Fortunelli
Source: Journal of Chemical Theory and Computation (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated DFT Conformal Sampling of Catalytic Processes Computational modeling of catalytic processes at gas/solid interfaces plays an increasingly important role in chemistry, enabling accelerated materials and process optimization and rational design. However, efficiency, accuracy, thoroughness, and throughput must be enhanced to maximize its practical impact. By combining interpolation of DFT energetics via highly accurate Machine-Learning Potentials with conformal techniques for building the training database, we present here an original approach (that we name Conformal Sampling of Catalytic Processes, CSCP), to accelerate and achieve an accurate and thorough sampling of novel systems by exporting existing information on a worked-out case. We use methanol decomposition (of interest in the field of hydrogen production and storage) as a test catalytic reaction. Starting from worked-out Pt-based systems, we show that after only two iterations of active-learning CSCP is able to provide reaction energy diagrams for a set of 7 diverse systems (Pd, Ni, Au, Ag, Cu, Co, Fe) leading to DFT-accuracy-level predictions. Cases exhibiting a change in adsorption sites and mechanisms are also successfully reproduced as tests of catalytic path modification. The CSCP approach thus offers itself as an operative tool to fully take advantage of accumulated information to achieve high-throughput sampling of catalytic processes. Computer science ; Catalysis ; Interpolation (computer graphics) ; Sampling (signal processing) ; Conformal map ; Throughput ; Chemistry ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Guangxia Zhao | Zhuopin Xu | Liwen Tang | Xiaohong Li | Pengfei Zhang | Qi Wang
Source: Microchemical Journal (article, 2024-07-03)
Field: Chemistry — Analytical Chemistry

A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm Sorghum ; Algorithm ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Smart Agriculture and AI
Authors: Nawaf Alampara | Santiago Miret | Kevin Maik Jablonka
Source: arXiv (Cornell University) (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Less can be more for predicting properties with large language models Predicting properties from coordinate-category data -- sets of vectors paired with categorical information -- is fundamental to computational science. In materials science, this challenge manifests as predicting properties like formation energies or elastic moduli from crystal structures comprising atomic positions (vectors) and element types (categorical information). While large language models (LLMs) have increasingly been applied to such tasks, with researchers encoding structural data as text, optimal strategies for achieving reliable predictions remain elusive. Here, we report fundamental limitations in LLM's ability to learn from coordinate information in coordinate-category data. Through systematic experiments using synthetic datasets with tunable coordinate and category contributions, combined with a comprehensive benchmarking framework (MatText) spanning multiple representations and model scales, we find that LLMs consistently fail to capture coordinate information while excelling at category patterns. This geometric blindness persists regardless of model size (up to 70B parameters), dataset scale (up to 2M structures), or text representation strategy. Our findings suggest immediate practical implications: for materials property prediction tasks dominated by structural effects, specialized geometric architectures consistently outperform LLMs by significant margins, as evidenced by a clear "GNN-LM wall" in performance benchmarks. Based on our analysis, we provide concrete guidelines for architecture selection in scientific machine learning, while highlighting the critical importance of understanding model inductive biases when tackling scientific prediction problems. Scale (ratio) ; Computer science ; Language model ; Scale model ; Natural language processing ; Engineering ; Geography ; Aerospace engineering ; Cartography Machine Learning in Materials Science