jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fitri Afriani | Yuant Tiandho | Aan Priyanto | William Xaveriano Waresindo | Dhewa Edikresnha | Fenny Martha Dwivany | Dian Ahmad Hapidin | Khairurrijal Khairurrijal
Source: Materials Letters (article, 2024-12-03)
Field: Materials Science — Biomaterials

Fabrication of rotary forcespun glucomannan/PEO nanofibers optimized using response surface methodology and machine learning Materials science ; Response surface methodology ; Fabrication ; Nanofiber ; Glucomannan ; Nanotechnology ; Composite material ; Computer science ; Machine learning Electrospun Nanofibers in Biomedical Applications ; Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties
Authors: Xuan‐Feng Jiang | Xiangfei Ye | Liangshun Zhang | Jiaping Lin
Source: Chemistry of Materials (article, 2024-12-05)
Field: Materials Science — Polymers and Plastics

Discovery of Copolymer Resins with Optimal Viscosity–Toughness–Heat Resistance Trade-Offs via the Material Genome Approach Accelerated discovery of polymer materials for specific applications requires the achievement of multiple, sometimes competing design objectives. Vast chemical space and the absence of well-established structure–processing–property relationships have considerably challenged the accelerated discovery of polymer materials. Herein, taking high-performance copolymer resins as an example, we propose a generalizable pipeline that integrates the screening of molecular structures, experimental validation of properties, and chemical interpretability to address both of these challenges. By means of the material genome paradigm, machine learning models are established for the structure–property relationship in conjunction with the additional viscosity for the evaluation of processing performance. Importantly, molecules of copolymer resins with low viscosity, high toughness, and high heat resistance are designed for a large search space. Experimental studies further verify that the newly designed copolymer resins possess excellent comprehensive performance, which cannot be achieved through time-consuming trial-and-error design methods. In addition, gene analysis is employed to interpret the structure–processing–property relationships of copolymer resins. The machine-learning-assisted material genome pipeline proposed in this work provides a promising strategy for the accelerated design of molecular structures in a large search space and for the understanding of structure–property relationships from data-informed perspectives. Copolymer ; Toughness ; Materials science ; Viscosity ; Composite material ; Heat resistance ; Polymer ; Polymer science Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: Joy Datta | Nikhil Koratkar | Dibakar Datta
Source: Energy Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unlocking the potential of open-tunnel oxides: DFT-guided design and machine learning-enhanced discovery for next-generation industry-scale battery technologies Lithium–ion batteries (LIBs) are ubiquitous in everyday applications. Battery (electricity) ; Scale (ratio) ; Computer science ; Engineering ; Physics Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Song Wei | Xiang Luo | Shaofeng Shao | Lei Zhang
Source: Dyes and Pigments (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Ionic liquid-assisted preparation of Ag–Zn–In–S quaternary quantum dot thin films and luminescence performance optimized by machine learning Quantum dot ; Luminescence ; Ionic liquid ; Materials science ; Ionic bonding ; Thin film ; Chemical engineering ; Optoelectronics ; Nanotechnology ; Ion ; Chemistry ; Organic chemistry ; Catalysis Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; ZnO doping and properties
Authors: Wenlei Qin | Shanshan Zheng | Kaiheng Guo | Ming Yang | Jingyun Fang
Source: Journal of Hazardous Materials (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Predicting reaction kinetics of reactive bromine species with organic compounds by machine learning: Feature combination and knowledge transfer with reactive chlorine species Chlorine ; Kinetics ; Bromine ; Chemistry ; Reactive intermediate ; Chemical kinetics ; Reactive oxygen species ; Feature (linguistics) ; Environmental chemistry ; Organic chemistry ; Biochemistry ; Catalysis Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Zhifan Ye | Jin Zhao | Haoyun Xing | Guice Yao | Dichu Xu | Dongsheng Wen
Source: International Journal of Heat and Mass Transfer (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Improving heat transfer prediction across catalytic SiC interface through a multiscale framework leveraging machine learning and data fusion techniques Interface (matter) ; Fusion ; Computer science ; Materials science ; Heat transfer ; Transfer of learning ; Machine learning ; Artificial intelligence ; Thermodynamics ; Composite material Thermal properties of materials ; Advanced ceramic materials synthesis ; Silicon Carbide Semiconductor Technologies
Authors: Guan-Ming Chen | Zi-Hao Ye | Zhiming Li | Junliang Zhang
Source: Cell Reports Physical Science (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Universal descriptors of quasi transition states for small-data-driven asymmetric catalysis prediction in machine learning model In enantioselectivity prediction models, the demand for numerous descriptors and extensive datasets poses a substantial challenge. Descriptor selection, fraught with uncertainty, compounds the issue, while amassing requisite data remains a daunting task. The introduction of descriptors derived from quasi transition states offers a promising avenue to alleviate this burden. However, the challenge of descriptor selection persists. Here, we report a small-data-driven model based on universal descriptors. Key differentiating properties between diastereomeric quasi transition states, encompassing single-point energies, frontier orbital energies, Cartesian forces, and charges of core atoms, are proposed as universal descriptors. The model’s efficacy is validated through its application to the asymmetric aldol and Negishi reactions, utilizing 3 experimental variables, merely less than 9 universal descriptors, and fewer than 150 training samples. Moreover, a method is presented using Cartesian forces to rectify discrepancies between quasi transition states and true transition states. This strategy circumvents tedious large-scale descriptor exploration and screening, offering an efficient choice for small-data-driven enantioselectivity prediction. Transition (genetics) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: Hui Chen | Duncan T. L. Alexander | C. Hébert
Source: Nano Letters (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorization based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high-quality phase spectral and spatial signatures via consecutive factorizations. After validating PSNMF with synthetic data sets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nanomineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, but data sets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis. Sharpening ; Hyperspectral imaging ; Computer science ; Noise (video) ; Chemical imaging ; Materials science ; Artificial intelligence ; Image (mathematics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Qiang Ma | Yi Wang | Xianglong Zhang | Qing‐Tai Zhao | Jie Guo | Guo Jia-hu | Xu Ren | Jin Huang | Yingjie Zhang | Yonghong Xie | Jiming Hao
Source: Materials Science and Engineering B (article, 2024-11-01)
Field: Chemistry — Inorganic Chemistry

Computational design of Metal-Organic Frameworks for sustainable energy and environmental applications: Bridging theory and experiment Bridging (networking) ; Sustainable energy ; Sustainable development ; Metal-organic framework ; Computer science ; Environmental science ; Engineering physics ; Biochemical engineering ; Nanotechnology ; Materials science ; Engineering ; Chemistry ; Political science ; Electrical engineering ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Madeline A. Murphy | Kyle Noordhoek | Sallye R. Gathmann | Paul J. Dauenhauer | Christopher J. Bartel
Source: ChemRxiv (preprint, 2024-07-03)
Field: Materials Science — Materials Chemistry

Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops Chemical transformations on catalyst surfaces occur through series and parallel reaction pathways. These complex networks and their behavior can be most simply evaluated through a three-species surface reaction loop (A* to B* to C* to A*) that is internal to the overall chemical reaction. Application of an oscillating dynamic catalyst to this reactive loop has been shown to exhibit one of three types of behavior: (1) a positive net flux of molecules about the loop in the clockwise direction, (2) a negative net flux of molecules about the loop in the counterclockwise direction, or (3) negligible flux of molecules about the loop at the limit cycle of reaction. Three-species surface loops were simulated with microkinetic modeling to assess the reaction loop behavior resulting from a catalytic surface oscillating between two or more catalyst surface energy states. Selected input parameters for the simulations spanned 11-dimensional parameter space using 127,688 different parameter combinations. Their converged limit cycle solutions were analyzed for their loop turnover frequencies, the majority of which were found to be approximately zero. Classification and regression machine learning models were trained to predict the sign and magnitude of the loop turnover frequency and successfully performed above accessible baselines. Notably, the classification models exhibited a baseline weighted F1 score of 0.48, whereas trained models achieved weighted F1 scores of 0.91 and 0.96 when trained on the parameters used to define the simulations and derived rate constants, respectively. The trained models successfully predicted catalytic loop behavior, and interpretation of these models revealed all input parameters to be important for the prediction and performance of each model. Catalysis ; Resonance (particle physics) ; Flow (mathematics) ; Nuclear magnetic resonance ; Mechanics ; Physics ; Computer science ; Chemistry ; Particle physics ; Organic chemistry Machine Learning in Materials Science
Authors: Weiyi Xia | Masahiro Sakurai | Timothy Liao | Renhai Wang | Chao Zhang | Huaijun Sun | Kai‐Ming Ho | James R. Chelikowsky | Cai‐Zhuang Wang
Source: APL Machine Learning (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js &amp;gt; 1.0 T) and significant magnetic anisotropy (K1 &amp;gt; 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties. Ternary operation ; Metastability ; Magnet ; Materials science ; Magnetic anisotropy ; Anisotropy ; Tetragonal crystal system ; Atom (system on chip) ; Magnetization ; Crystal structure ; Computer science ; Crystallography ; Chemistry ; Physics ; Magnetic field ; Optics ; Parallel computing Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Vincent Martinetto | Karan Shah | Attila Cangi | Aurora Pribram−Jones
Source: Machine Learning Science and Technology (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Inverting the Kohn–Sham equations with physics-informed machine learning Abstract Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory, maps a set of real interacting electrons to a set of fictitious non-interacting electrons that share the same probability density. Ensuring that the density remains the same depends on the exchange-correlation (XC) energy and, by a derivative, the XC potential. Inversions provide a method to obtain exact XC potentials from target electronic densities, in hopes of gaining insights into accuracy-boosting approximations. Neural networks provide a new avenue to perform inversions by learning the mapping from density to potential. In this work, we learn this mapping using physics-informed machine learning methods, namely physics informed neural networks and Fourier neural operators. We demonstrate the capabilities of these two methods on a dataset of one-dimensional atomic and molecular models. The capabilities of each approach are discussed in conjunction with this proof-of-concept presentation. The primary finding of our investigation is that the combination of both approaches has the greatest potential for inverting the Kohn–Sham equations at scale. Kohn–Sham equations ; Mathematical physics ; Physics ; Theoretical physics ; Algebra over a field ; Psychology ; Mathematics ; Quantum mechanics ; Pure mathematics ; Density functional theory Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Mohammadamin Tavakoli | Ryan J. Miller | Mirana C. Angel | Michael A. Pfeiffer | Eugene S. Gutman | Aaron Mood | David L. Van Vranken | Pierre Baldi
Source: Journal of Chemical Information and Modeling (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

PMechDB: A Public Database of Elementary Polar Reaction Steps Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb. Interpretability ; Cheminformatics ; Computer science ; Upload ; Database ; Download ; Chemical database ; Information retrieval ; World Wide Web ; Chemistry ; Artificial intelligence ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Jun Zhu | Mengdan Song | Qi-Ling Qian | Yue Yang | Guangren Qian | Jia Zhang
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for deconstructing contributions of atomic characterizations to achieve hybridization-determined electron transfer in a perovskite catalyst Machine learning deconstructed the atomic contribution of a perovskite to catalytic toluene decomposition and found that wider hybridization resulted in smaller impedance, faster electron transfer speed, and enhanced catalytic activity. Perovskite (structure) ; Electron transfer ; Catalysis ; Electron ; Materials science ; Physics ; Chemical physics ; Atomic physics ; Chemistry ; Physical chemistry ; Nuclear physics ; Crystallography Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Gyoung S. Na
Source: Analytical Chemistry (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Deep Learning for Generating Phase-Conditioned Infrared Spectra Infrared (IR) spectroscopy is an efficient method for identifying unknown chemical compounds. To accelerate IR spectrum analysis, various calculation and machine learning methods for simulating IR spectra of molecules have been studied in chemical science. However, existing calculation and machine learning methods assumed a rigid constraint that all molecules are in the gas phase, i.e., they overlooked the phase dependency of the IR spectra. In this paper, we propose an efficient phase-aware machine learning method to generate phase-conditioned IR spectra from 2D molecular structures. To this end, we devised a phase-aware graph neural network and combined it with a transformer decoder. To the best of our knowledge, the proposed method is the first IR spectrum generator that can generate the phase-conditioned IR spectra of real-world complex molecules. The proposed method outperformed state-of-the-art methods in the tasks of generating IR spectra on a benchmark dataset containing experimentally measured 11,546 IR spectra of 10,288 unique molecules. All implementations of the proposed method are publicly available at https://github.com/ngs00/PASGeN. Infrared spectroscopy ; Chemistry ; Spectral line ; Infrared ; Molecule ; Phase (matter) ; Artificial neural network ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Algorithm ; Optics ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Stefano Martire | Sergio Decherchi | Andrea Cavalli
Source: Journal of Chemical Theory and Computation (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

OBIWAN: An Element-Wise Scalable Feed-Forward Neural Network Potential Estimating the potential energy of a molecular system at a quantum level of theory is a task of paramount importance in computational chemistry. The often employed density functional theory approach allows one to accomplish this task, yet most often at significant computational costs. This prompted the community to develop so-called machine learning potentials to achieve near-quantum accuracy at molecular mechanics computational cost. In this paper, we introduce OBIWAN, a feed-forward neural network that bears some relevant structural properties that also led to the definition of a new kind of general-purpose neural network layer. Its featurization process scales efficiently with newly added atomic species. This allows one to seamlessly add new atom types without requiring to change the topology of the network. Also, this allows one to train on new data sets leveraging a previously trained OBIWAN, hence converging very quickly. This avoids training from scratch and renders the approach more compliant with a green computing perspective. Scalability ; Computer science ; Artificial neural network ; Element (criminal law) ; Data mining ; Artificial intelligence ; Data science ; Database Machine Learning in Materials Science ; Fault Detection and Control Systems ; Nuclear Physics and Applications
Authors: Mitchell Mika | Nathaniel Tomczak | Clare Finney | Jennifer Carter | Assel Aitkaliyeva
Source: Journal of Materiomics (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Automating selective area electron diffraction phase identification using machine learning Selective area electron diffraction (SAED) patterns can provide valuable insight into the structure of a material. However, the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow. In this work, we utilize the recent advances in computer vision and machine learning (ML) to automate the indexing of SAED patterns. The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys. The most successful approach trained a neural network (NN) to make a classification of the phase and zone axis, and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification. The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization. Selected area diffraction ; Identification (biology) ; Bottleneck ; Workflow ; Phase (matter) ; Artificial neural network ; Computer science ; Automatic summarization ; Sample (material) ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Machine learning ; Nanotechnology ; Transmission electron microscopy ; Chemistry ; Biology ; Chromatography ; Database ; Ecology ; Embedded system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties