jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Idil Ismail | Christopher Robertson | Scott Habershon
Source: The Journal of Chemical Physics (article, 2022-06-14)
Field: Materials Science — Materials Chemistry

Successes and challenges in using machine-learned activation energies in kinetic simulations The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods, such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has been focused on how the accuracy of ML predictions filters through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies, given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions, and decomposition of 3-hydroperoxypropanal. We find that the kinetic modeling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable and when one should take more care in applications to kinetics modeling. Kinetic energy ; Decomposition ; Activation energy ; Observable ; Artificial neural network ; Ab initio ; Computer science ; Reaction rate ; Statistical physics ; Chemistry ; Computational chemistry ; Biological system ; Thermodynamics ; Artificial intelligence ; Physical chemistry ; Physics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Peerawat Prathumrat | Mostafa Nikzad | Elnaz Hajizadeh | Reza Arablouei | Igor Sbarski
Source: Polymers for Advanced Technologies (review, 2022-03-11)
Field: Materials Science — Polymers and Plastics

Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications Abstract Shape memory elastomers (SMEs) are a class of intelligent materials characterized by their ability to deform and recover shapes under applied force and external stimuli. Heat and ultraviolet radiation are examples of the most common external stimuli. With the emerging prevalence of internet of things devices and the ensuing need for smart materials and structures, SMEs provide significant opportunities to support the development of novel applications in robotics, remotely actuated systems, and packages, including those promised for the space industry. To harness the immense potential in the emerging applications of these materials, one approach is the systematic multi‐scale modeling coupled with artificial intelligence‐assisted design leading to the development of next‐generation intelligent systems. This review covers several aspects of the synthesis/materials chemistry and applications of SMEs with a view towards enabling such an approach. The synthesis procedures emphasizing dynamic covalent bond reactions are reviewed. Then, liquid crystalline elastomers are introduced as a specific elastomeric material class that exhibits excellent shape memory characteristics and distinctive transition temperatures. The utilization of advanced manufacturing methods such as additive manufacturing, three‐dimensional printing, and the emerging four‐dimensional printing technologies assisted by machine learning are detailed in producing and predicting SMEs. Finally, the current trends in the use of SMEs are summarized in areas of industrial and space engineering and biomedical applications. Materials science ; Elastomer ; 3D printing ; Nanotechnology ; Shape-memory alloy ; Computer science ; Thermoplastic elastomer ; Scale (ratio) ; Manufacturing engineering ; Space (punctuation) ; Class (philosophy) ; Emerging technologies ; Mechanical engineering ; Systems engineering ; Artificial intelligence ; Engineering ; Polymer ; Composite material Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Jiraporn Onmankhong | Te Ma | Tetsuya Inagaki | Panmanas Sirisomboon | Satoru Tsuchikawa
Source: Infrared Physics & Technology (article, 2022-02-21)
Field: Chemistry — Analytical Chemistry

Cognitive spectroscopy for the classification of rice varieties: A comparison of machine learning and deep learning approaches in analysing long-wave near-infrared hyperspectral images of brown and milled samples Hyperspectral imaging ; Support vector machine ; Brown rice ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Computer science ; Near-infrared spectroscopy ; Data set ; Deep learning ; Test set ; Remote sensing ; Chemistry ; Physics ; Optics ; Geology ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yangyang Yu | Weiwei Zhang | Donghai Mei
Source: The Journal of Physical Chemistry C (article, 2022-01-10)
Field: Chemistry — Inorganic Chemistry

Artificial Neural Network Potential for Encapsulated Platinum Clusters in MOF-808 Metal-organic frameworks (MOFs) have been recognized as one of the ideal supporting sintering-resistant catalyst materials because of their high specific surface area and unique nano-porous structure capable of manipulating the sizes and shapes of encapsulated metal clusters. To explore the binding sites, the stability, and migration mechanisms of encapsulated metal clusters in MOF materials, a robust potential model that accurately describes the interaction between metal clusters and MOF materials is highly desired for large-scale atomic simulations. Herein, as a demonstration case, an artificial neural network potential for encapsulated platinum (Pt) clusters in MOF-808 was developed using the machine learning-based global neural network (G-NN) technique. The artificial G-NN potential was tested and validated against a series of density functional theory calculation data, including structure optimization, adsorption energies, and the migration energy barrier of Ptn (n = 1–13) clusters in MOF-808. The newly developed Pt-MOF G-NN potential was further used to predict the adsorption and migration behaviors of Ptn clusters in MOF-808. It is found that the most stable adsorption site varies with the Ptn cluster size. The migration possibility of the Ptn cluster is strongly correlated with the adsorption energies of the Ptn clusters. Finally, the CO adsorption on the single Pt atom would effectively promote the aggregation of Ptn clusters via the Ostwald ripening mechanism. Cluster (spacecraft) ; Adsorption ; Ostwald ripening ; Platinum ; Metal ; Chemical physics ; Materials science ; Porosity ; Nanotechnology ; Artificial neural network ; Chemistry ; Catalysis ; Chemical engineering ; Physical chemistry ; Computer science ; Composite material ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Prathik R. Kaundinya | Kamal Choudhary | Surya R. Kalidindi
Source: JOM (article, 2022-03-02)
Field: Materials Science — Materials Chemistry

Prediction of the Electron Density of States for Crystalline Compounds with Atomistic Line Graph Neural Networks (ALIGNN) Computer science ; Discretization ; Autoencoder ; Artificial neural network ; Graph ; Pipeline (software) ; Representation (politics) ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yannick Ureel | Maarten R. Dobbelaere | Oğuzhan Akin | Robin John Varghese | César Pernalete | Joris Thybaut | Kevin M. Van Geem
Source: Fuel (article, 2022-07-28)
Field: Materials Science — Materials Chemistry

Active learning-based exploration of the catalytic pyrolysis of plastic waste Active learning (machine learning) ; Computer science ; Yield (engineering) ; Design of experiments ; Machine learning ; Materials science ; Artificial intelligence ; Process engineering ; Engineering ; Mathematics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Mauro Bracconi
Source: Chemical Engineering and Processing - Process Intensification (article, 2022-09-21)
Field: Materials Science — Materials Chemistry

Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing Process engineering ; Manufacturing engineering ; Catalysis ; Computer science ; Biochemical engineering ; Environmental science ; Engineering ; Chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Injection Molding Process and Properties
Authors: Wei Peng | Giovanni Beggio | Alberto Pivato | Hua Zhang | Fan Lü | Pinjing He
Source: Renewable and Sustainable Energy Reviews (review, 2022-05-20)
Field: Chemistry — Analytical Chemistry

Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review Digestate ; Hyperspectral imaging ; Anaerobic digestion ; Process analytical technology ; Environmental science ; Biochemical engineering ; Process (computing) ; Process engineering ; Computer science ; Phenomics ; Methane ; Artificial intelligence ; Bioprocess ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Anaerobic Digestion and Biogas Production ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chao Rong | Lei Zhou | Bowei Zhang | Fu‐Zhen Xuan
Source: Composites Communications (article, 2022-12-25)
Field: Materials Science — Materials Chemistry

Machine learning for mechanics prediction of 2D MXene-based aerogels Nanocellulose ; Aerogel ; MXenes ; Materials science ; Artificial neural network ; Compressive strength ; Support vector machine ; Compression (physics) ; Smart material ; Machine learning ; Elastic modulus ; Composite material ; Computer science ; Artificial intelligence ; Nanotechnology ; Engineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Electromagnetic wave absorption materials
Authors: Xuejiao J. Gao | Jun‐Min Yan | Jia‐Jia Zheng | Shengliang Zhong | Xingfa Gao
Source: Advanced Healthcare Materials (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Clear‐Box Machine Learning for Virtual Screening of 2D Nanozymes to Target Tumor Hydrogen Peroxide Targeting tumor hydrogen peroxide (H<sub>2</sub> O<sub>2</sub> ) with catalytic materials has provided a novel chemotherapy strategy against solid tumors. Because numerous materials have been fabricated so far, there is an urgent need for an efficient in silico method, which can automatically screen out appropriate candidates from materials libraries for further therapeutic evaluation. In this work, adsorption-energy-based descriptors and criteria are developed for the catalase-like activities of materials surfaces. The result enables a comprehensive prediction of H<sub>2</sub> O<sub>2</sub> -targeted catalytic activities of materials by density functional theory (DFT) calculations. To expedite the prediction, machine learning models, which efficiently calculate the adsorption energies for 2D materials without DFT, are further developed. The finally obtained method takes advantage of both interpretability of physics model and high efficiency of machine learning. It provides an efficient approach for in silico screening of 2D materials toward tumor catalytic therapy, and it will greatly promote the development of catalytic nanomaterials for medical applications. Hydrogen peroxide ; Interpretability ; Density functional theory ; Computer science ; Nanomaterials ; Catalysis ; In silico ; Materials science ; Adsorption ; Nanotechnology ; Machine learning ; Chemistry ; Computational chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Robert E. Simpson | Joel K. W. Yang | Juejun Hu
Source: Optical Materials Express (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Are phase change materials ideal for programmable photonics?: opinion The objective of this Opinion is to stimulate new research into materials that can meet the needs of tomorrow’s programmable photonics components. Herein, we argue that the inherent property portfolios of the common telluride phase change materials, which have been successfully applied in data storage technologies, are unsuitable for most emerging programmable photonics applications. We believe that newer PCMs with wider bandgaps, such as Sb 2 S 3 , Sb 2 Se 3 , and Ge 2 Sb 2 Se 4 Te (GSST), can be optimized to meet the demands of holographic displays, optical neural network memories, and beam steering devices. Photonics ; Materials science ; Holography ; Nanotechnology ; Computer science ; Optoelectronics ; Engineering physics ; Optics ; Engineering ; Physics Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Chalcogenide Semiconductor Thin Films
Authors: Imran Imran | Faiza Qayyum | Do‐Hyeun Kim | Seon-Jong Bong | Suyoung Chi | Yohan Choi
Source: Materials (review, 2022-02-15)
Field: Materials Science — Materials Chemistry

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments' challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature. Data science ; Computer science ; Benchmark (surveying) ; Big data ; Process (computing) ; Knowledge extraction ; Preprocessor ; Artificial intelligence ; Data pre-processing ; Machine learning ; Management science ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zhedong Wang | Chao Qian | Zhixiang Fan | Hongsheng Chen
Source: Advanced Science (article, 2022-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Arbitrary Polarization Readout with Dual‐Channel Neuro‐Metasurfaces Polarization, as a vector nature of the electromagnetic wave, plays a fundamental role in optics. Determining the polarization state of light is required by many applications, spanning from remote sensing and material analysis to biology and microscopy. To achieve this goal, conventional methods necessitate cascading of multiple optical components and consequential measurements to estimate the Stokes parameters, rendering the entire optical system bulky, complex, and sensitive. Here a brand-new strategy is introduced for direct polarization readout based on dual-channel neuro-metasurfaces. Neuro-metasurfaces can independently manipulate two orthogonal linearly-polarized waves that can synthesize arbitrary polarization waves with a linear combination. By judiciously designing the output focus points, a unique polarization atlas is created that allows one-to-one correspondence from intensity ratio to polarization state. To implement this, polarization-sensitive metasurfaces are designed and the spatial layout is optimized using a diffractive neural network. The feasibility of this strategy is validated by numerical simulation and microwave experiments. These results pave a new avenue in realizing integrated and multifunctional detectors and demonstrate the potential of neuro-metasurfaces as an add-on for discomposing and composing spatial basis. Polarization (electrochemistry) ; Stokes parameters ; Optics ; Physics ; Computer science ; Detector ; Dual-polarization interferometry ; Telecommunications ; Scattering Metamaterials and Metasurfaces Applications ; Optical Polarization and Ellipsometry ; Neural Networks and Reservoir Computing
Authors: Jeong Woo Han | Frank Hollmann | Rafael Luque | In Kyu Song | Giovanni Talarico | Takashi Tatsumi | Ning Yan
Source: Molecular Catalysis (article, 2022-03-25)
Field: Chemistry — Inorganic Chemistry

Molecular Catalysis for the Chemistry of the future: a perspective Perspective (graphical) ; Catalysis ; Chemistry ; Computational chemistry ; Biochemical engineering ; Organic chemistry ; Computer science ; Engineering Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation ; Nanomaterials for catalytic reactions
Authors: Rita M. F. Cardoso | Inês M. F. Cardoso | Luís Pinto da Silva | Joaquim C. G. Esteves da Silva
Source: Nanomaterials (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Copper(II)-Doped Carbon Dots as Catalyst for Ozone Degradation of Textile Dyes A catalytic ozonation advanced oxidation process (AOP) with a copper(II)-doped carbon dot as catalyst, Cu-CD (using L-cysteine and polyethylene glycol (PEG) as precursors and passivation agents), was developed for textile wastewater treatment (T = 25 °C and pH = 7). Four dyes were analyzed—Methyl Orange (MO), Orange II sodium salt (O-II), Reactive Black 5 (RB-5) and Remazol Brilliant Blue R (RBB-R), as well as a real effluent from the dying and printing industry. The Cu-CD, with marked catalytic ozonation properties, was successfully synthesized by one-pot hydrothermal procedure with a size of 4.0 nm, a charge of −3.7 mV and a fluorescent quantum yield of 31%. The discoloration of the aqueous dye solutions followed an apparent first-order kinetics with the following rate constants (kap in min−1): MO, 0.210; O-II, 0.133; RB-5, 0.177; RBB-R, 0.086. In the presence of Cu-CD, the following apparent first-order rate constants were obtained (kapc in min−1) with the corresponding increase in the rate constant without catalyst (%Inc): MO, 1.184 (464%); O-II, 1.002 (653%); RB-5, 0.709 (301%); RBB-R, 0.230 (167%). The presence of sodium chloride (at a concentration of 50 g/L) resulted in a marked increase of the discoloration rate of the dye solution due to generation of other radicals, such as chlorine and chlorine oxide, resulting from the reaction of ozone and chloride. Taking into consideration that the real textile effluent under research has a high carbonate concentration (>356 mg/L), which inhibits ozone decomposition, the discoloration first-order rate constants without and with Cu-CD (kap = 0.0097 min−1 and kapc = 0.012 min−1 (%Inc = 24%), respectively) were relatively small. Apparently, the Cu-CD, the surface of which is covered by a soft and highly hydrated caramelized PEG coating, accelerates the ozone decomposition and dye adsorption, increasing its degradation. Reaction rate constant ; Chlorine ; Chemistry ; Catalysis ; Nuclear chemistry ; Aqueous solution ; Ozone ; Chloride ; Methyl orange ; Sodium carbonate ; Inorganic chemistry ; Sodium ; Kinetics ; Organic chemistry ; Photocatalysis Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Andrew P. Justl | Francesco Ricci | Andrew Pike | Giacomo Cerretti | Sabah K. Bux | Geoffroy Hautier | Susan M. Kauzlarich
Source: Science Advances (article, 2022-09-07)
Field: Materials Science — Materials Chemistry

Unlocking the thermoelectric potential of the Ca <sub>14</sub> AlSb <sub>11</sub> structure type Yb<sub>14</sub>MnSb<sub>11</sub> and Yb<sub>14</sub>MgSb<sub>11</sub> are among the best p-type high-temperature (>1200 K) thermoelectric materials, yet other compounds of this Ca<sub>14</sub>AlSb<sub>11</sub> structure type have not matched their stability and efficiency. First-principles computations show that the features in the electronic structures that have been identified to lead to high thermoelectric performances are present in Yb<sub>14</sub>ZnSb<sub>11</sub>, which has been presumed to be a poor thermoelectric material. We show that the previously reported low power factor of Yb<sub>14</sub>ZnSb<sub>11</sub> is not intrinsic and is due to the presence of a Yb<sub>9</sub>Zn<sub>4+<i>x</i></sub>Sb<sub>9</sub> impurity uniquely present in the Zn system. Phase-pure Yb<sub>14</sub>ZnSb<sub>11</sub> synthesized through a route avoiding the impurity formation reveals its exceptional high-temperature thermoelectric properties, reaching a peak <i>zT</i> of 1.2 at 1175 K. Beyond Yb<sub>14</sub>ZnSb<sub>11</sub>, the favorable band structure features for thermoelectric performance are universal among the Ca<sub>14</sub>AlSb<sub>11</sub> structure type, opening the possibility for high-performance thermoelectric materials. Thermoelectric effect ; Thermoelectric materials ; Materials science ; Seebeck coefficient ; Impurity ; Electronic structure ; Condensed matter physics ; Phase (matter) ; Thermodynamics ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Rare-earth and actinide compounds ; Thermal properties of materials