jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hendrik Jung | Roberto Covino | A. Arjun | Peter G. Bolhuis | Gerhard Hummer
Source: arXiv (Cornell University) (preprint, 2021-05-14)
Field: Materials Science — Materials Chemistry

Autonomous artificial intelligence discovers mechanisms of molecular self-organization in virtual experiments Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Understanding the physical mechanisms that govern the formation of molecular complexes and crystals is key to controlling the assembly of nanomachines and new materials. We present an artificial intelligence (AI) agent that uses deep reinforcement learning and transition path theory to discover the mechanism of molecular self-organization phenomena from computer simulations. The agent adaptively learns how to sample complex molecular events and, on the fly, constructs quantitative mechanistic models. By using the mechanistic understanding for AI-driven sampling, the agent closes the learning cycle and overcomes time-scale gaps of many orders of magnitude. Symbolic regression condenses the mechanism into a human-interpretable form. Applied to ion association in solution, gas-hydrate crystal formation, and membrane-protein assembly, the AI agent identifies the many-body solvent motions governing the assembly process, discovers the variables of classical nucleation theory, and reveals competing assembly pathways. The mechanistic descriptions produced by the agent are predictive and transferable to close thermodynamic states and similar systems. Autonomous AI sampling has the power to discover assembly and reaction mechanisms from materials science to biology. Mechanism (biology) ; Artificial intelligence ; Computer science ; Artificial life ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Shidang Xu | Jiali Li | Pengfei Cai | Xiaoli Liu | Bin Liu | Xiaonan Wang
Source: ChemRxiv (preprint, 2021-06-10)
Field: Materials Science — Materials Chemistry

A Self-Improving Photosensitizer Discovery System via Bayesian Optimization and Quantum Chemical Calculation Artificial intelligence (AI) based self-learning or self-improving material discovery system is the holy grail of next-generation material discovery and materials science. Herein, we demonstrate how to combine accurate prediction of material performance via quantum chemical calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PS). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean average error of 0.09 eV for singlet-triplet spitting) and high-performance PS search ability, realizing the efficient discovery of PS. From a molecular space with more than 7 million molecules, 5950 potential high-performance PSs were discovered. Bayesian optimization ; Photosensitizer ; Computer science ; Holy Grail ; Bayesian probability ; Singlet state ; Quantum dot ; Nanotechnology ; Materials science ; Artificial intelligence ; Physics ; Quantum mechanics ; Excited state Machine Learning in Materials Science
Authors: André Colliard-Granero | Mariah Batool | Jasna Janković | Jenia Jitsev | Michael Eikerling | Kourosh Malek | Mohammad J. Eslamibidgoli
Source: ChemRxiv (preprint, 2021-09-30)
Field: Materials Science — Materials Chemistry

Deep Learning for the Automation of Particle Analysisin Catalyst Layers for Polymer Electrolyte Fuel Cells The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often \textit{ad hoc}, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. Our approach employs training an instance segmentation model, called StarDist [Schmidt et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018, Lecture Notes in Computer Science, vol 11071. Springer, Cham], which resolves the main challenge in the pixel-wise localization of nanoparticles in TEM images: the overlapping particles. The segmentation maps outperform models reported in the literature, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost. Computer science ; Segmentation ; Deep learning ; Pixel ; Particle (ecology) ; Automation ; Preprocessor ; Nanoparticle ; Artificial intelligence ; Throughput ; Nanotechnology ; Materials science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques
Authors: Aryan Deshwal | Cory M. Simon | Janardhan Rao Doppa
Source: ChemRxiv (preprint, 2021-07-11)
Field: Materials Science — Materials Chemistry

Bayesian optimization of nanoporous materials Nanoporous materials (NPMs) could be used to store, capture, and sense many different gases. Given an adsorption task, we often wish to search a library of NPMs for the one with the optimal adsorption property. The high cost of NPM synthesis and gas adsorption measurements, whether these experiments are in the lab or in a simulation, often precludes exhaustive search. We explain, demonstrate, and advocate Bayesian optimization (BO) to actively search for the optimal NPM in a library of NPMs-- and find it using the fewest experiments. The two ingredients of BO are a surrogate model and an acquisition function. The surrogate model is a probabilistic model reflecting our beliefs about the NPM-structure--property relationship based on observations from past experiments. The acquisition function uses the surrogate model to score each NPM according to the utility of picking it for the next experiment. It balances two competing goals: (a) exploitation of our current approximation of the structure-property relationship to pick the highest-performing NPM, and (b) exploration of blind spots in the NPM space to pick an NPM we are uncertain about, to improve our approximation of the structure-property relationship. We demonstrate BO by searching an open database of ~70,000 hypothetical covalent organic frameworks (COFs) for the COF with the highest simulated methane deliverable capacity. BO finds the optimal COF and acquires 30% of the top 100 highest-ranked COFs after evaluating only ~120 COFs. More, BO searches more efficiently than evolutionary and one-shot supervised machine learning approaches. Nanoporous ; Property (philosophy) ; Bayesian optimization ; Computer science ; Probabilistic logic ; Bayesian probability ; Function (biology) ; Tabu search ; Surrogate model ; Space (punctuation) ; Task (project management) ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics ; Materials science ; Nanotechnology ; Systems engineering ; Engineering Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Neelanshi Wadhwa | S Sarath | Sapan Shah | Sreedhar Reddy | Pritwish Mitra | Deepak Jain | Beena Rai
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Device Fabrication Knowledge Extraction from Materials Science Literature Devices like solar cells, batteries etc. often comprise of a host of material types including organic, inorganic and hybrid materials. The fabrication procedures for these devices involve screening or designing the right set of materials and then subjecting them to a sequence of operations under very specific conditions. The performance characteristics of a device critically depend on the materials used in its fabrication, the specific operations carried out, their operating conditions and the specific sequence in which they are carried out. The space of potential materials, operations and operating conditions is vast, and selecting the right combination thereof to achieve the desired characteristics is a knowledge intensive activity. A large amount of such device fabrication knowledge is available in the form of publications, patents, company reports and so on. In this paper, we present a system that systematically extracts this knowledge from materials science literature. The extracted knowledge is represented as knowledge graphs conforming to an ontology that can be queried to make informed decisions in device fabrication procedures. The system first identifies the set of relevant paragraphs that contain fabrication knowledge. It then employs state of the art entity and relation extraction models to identify instances of operations, methods, materials, etc. and relations between them. The system then applies an unsupervised algorithm to identify sequences of operations representing fabrication procedures. We applied our system on solar cell fabrication knowledge extraction and achieved good performance. We believe our results provide much needed impetus for further work in this area. Fabrication ; Computer science ; Set (abstract data type) ; Sequence (biology) ; Relation (database) ; Ontology ; Information retrieval ; Data mining Machine Learning in Materials Science ; Software Engineering Research ; Green IT and Sustainability
Authors: Nathan Rampelotto Bressa | Vinícius Rodrigues Oviedo | Aline Machado Bessow Machado | Willians Lopes de Almeida | Tiago Moreno Volkmer | Luís Alberto dos Santos | Michele Rorato Sagrillo | Luiz Fernando Rodrigues
Source: Polímeros (article, 2021-01-01)
Field: Materials Science — Biomaterials

Incorporation of astrocaryum vulgare (tucuma) oil into PCL electrospun fibers The aim of this study was to incorporate tucuma oil (Astrocaryum vulgare) into PolyCaprolactone (PCL) electrospun fibers and evaluate its physicochemical properties and cell viability. FTIR and DRX confirmed that tucuma oil (TO) does not affect the chemical properties of PCL and that the oil was loaded into the PCL microstructure, while TGA analysis showed that the oil increased the thermal stability of the polymeric fibers. SEM showed that the addition of the oil modified fibers structure by reducing the average fiber size from 5.5 μm to 1.7 μm for TO loaded samples. Cell viability assay demonstrated an increment on cell proliferation from 80% of pure PCL to 100% for samples containing TO. Therefore, it can be concluded that tucuma oil can be incorporated into PCL to form fibers by electrospinning, without meaningful changes in its physicochemical properties and increasing its biocompatibility. Electrospinning ; Biocompatibility ; Viability assay ; Polycaprolactone ; Fiber ; Thermal stability ; Materials science ; Chemical engineering ; Composite material ; Biology ; Polymer ; Cell ; Biochemistry Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications
Authors: Chillu Naresh | R. Jayaganthan | R. Sarathi | M. G. Danikas
Source: Journal of Engineering Science and Technology Review (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Charge Trapping Phenomenon in Al-Filled Epoxy Nanocomposite Materials: Current Status and Challenges for Insulation Structures In the present work, a criticalreview is made on methods of surface potential measurements adopted on various polymeric materials, mechanisms of charge decay and trap distribution associated with the respective materials. Surface potential measurements and trapping characteristics of polymeric materials reported in the literature are critically assessed to provide glimpses of its growing demand for the improved performance of insulation structures in power system networks. The present review ensues with the preamble of the importance of corona discharge of various insulators and subsequently it dwells on particularly on the development of polymeric insulating materials in the high voltage engineering. The importance and method of surface charge measurements of polymeric insulating materials are presented. The needle plane configuration setup employed to investigate the surface charge decay characteristics are described. It is chosen due to its non-contact, easy to use and adaptability to desired experimental conditions. After the charging process, the various routes of charge decay and different detrapping processes involved in the charge neutralization, along with their importance on underlying experimental conditions, are elaborated further. The development of the different type of traps in the material and their role in accumulating the charge in the vicinity of the defective states are systematically evaluated. Various important parameters such as initial potential, decay rate, trap depth and trap charge density are used in elucidating the charge trapping behaviour of the polymeric materials. The variation in the surface potential as well as the respective trap distribution due to the Aluminium (Al) nanofiller addition, under different voltage profiles and different irradiated environments are elucidated. Controlled addition of Al nanoparticles to base epoxy matric has shown an increased decay rate. On the other hand, increased rise time of the applied transient voltage has resulted in the deep trap sites in the nanocomposite material. The induced trap energy states due to the gamma irradiation in the epoxy matrix are nullified by the addition of Al filler content. Epoxy ; Trapping ; Materials science ; Nanocomposite ; Composite material ; Current (fluid) ; Charge (physics) ; Electrical engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Malek Hassanpour
Source: Petroleum & Petrochemical Engineering Journal (review, 2021-01-01)
Field: Chemistry — Analytical Chemistry

A Review of Industrial Paraffin Production Technologies Based on Recent Developments Lubrication or tribology, which is the science of facilitating the relative motion of surfaces in contact with each other, is one of the most important disciplines in science, technical knowledge, and engineering operations today. Regarding the vast application of Industrial Paraffin (IP) in daily life and the existence of numerous oil wells, the current review discussed IP production technologies and applications of IP in many human demands in the framework of a project and its public involvements. It was summarized the recent technologies posed in IP production from valid and available databases to demystify the existing alternatives. The information of assessed projects in the Environmental Impact Assessment (EIA) plan was offered to access the framework of the initial assessment of the evaluator team in the screening step of project identification. The review of developed technologies surged the interests of decision-maker for pushing the industrial projects towards decision theory to make the matrix of data for further assessment depend on the quality and quantity of the requested product. The conclusion of the current review ended up attracting decision-maker opinion to mitigate the hazardous and health effects of introduced technologies by selecting the best alternative in IP production operation. Production (economics) ; Product (mathematics) ; Emerging technologies ; Quality (philosophy) ; Plan (archaeology) ; Hazardous waste ; Computer science ; Identification (biology) ; Engineering management ; Risk analysis (engineering) ; Engineering ; Business ; Artificial intelligence Petroleum Processing and Analysis ; Oil and Gas Production Techniques ; Drilling and Well Engineering
Authors: Çağla Koşak Söz
Source: TURKISH JOURNAL OF CHEMISTRY (article, 2021-05-14)
Field: Materials Science — Surfaces, Coatings and Films

Hybrid paper sheets with improved barrier properties Hybrid paper sheets were prepared by applying a thin coating layer of cross-linked polydimethylsiloxane (PDMS) and inorganic particles onto Whatman Grade 1 filter paper substrates. Several coatings with different inorganic particle contents and types were applied onto the paper substrates to investigate the effect of the variation in the coating formulation on the (i) wetting, (ii) water barrier properties, (iii) air barrier properties, (iv) surface roughness, and (v) mechanical properties of the samples. It was revealed that the superhydrophobic hybrid paper sheets with significantly low air permeability and high water barrier properties could be prepared which is an indication that the method proposed can be used for the preparation of packaging materials. Polydimethylsiloxane ; Coating ; Wetting ; Chemistry ; Filter paper ; Air permeability specific surface ; Composite material ; Surface finish ; Chemical engineering ; Surface roughness ; Layer (electronics) ; Nanotechnology ; Materials science ; Chromatography ; Organic chemistry Surface Modification and Superhydrophobicity ; Material Properties and Processing ; RFID technology advancements
Authors: Wei Xu | Lihui Xu | Hong Pan | Liming Wang | Yong Shen | Yangchun Liu | Tianyang Li | Keting Li | Jun Li
Source: Research Square (preprint, 2021-11-10)
Field: Materials Science — Materials Chemistry

Robust ZnO/HNTs-Based Superhydrophobic Cotton Fabrics With UV Shielding, Self-Cleaning, Photocatalysis, and Oil/Water Separation Abstract In this work, robust superhydrophobic cotton fabrics with UV shielding, self-cleaning, photocatalysis, and oil/water separation were successfully prepared based on micro/nano hierarchical ZnO/HNTs (halloysite nanotubes) hybrid particles and silicone elastomer polydimethylsiloxane (PDMS). ZnO/HNTs hybrid particles were prepared by in-situ growth of ZnO nanoparticles on the surface of halloysite nanotubes (HNTs). ZnO/HNTs hybrid particles and PDMS were used to successively coat cotton fabric by dip-coating approach. The coated cotton fabric displayed excellent superhydrophobicity with a water contact angle of 162.5 ± 1° and photocatalytic degradation of methylene blue solution under UV irradiation owing to the roughness and photocatalytic performance provided by micro/nano hierarchical ZnO/HNTs hybrid particles and low surface energy achieved by PDMS. The as-prepared fabric also displayed outstanding self-cleaning and antifouling properties. In addition, due to its both superhydrophobic and superoleophilic characteristics, the as-prepared cotton fabric can be used to separate several oil/water mixtures and showed good recoverability. The superhydrophobic cotton fabric also exhibited excellent UV shielding performance with a large UV protection factor of 1643.28 due to strong ultraviolet-absorption, light scattering and frequent light reflection of ZnO nanoparticles in ZnO/HNTs composites coated on cotton fabric. Importantly, the as-prepared fabric retained superhydrophobic performance after 2000 cycles rubbing, 90h UV illumination, and immersing in acidic and alkali solutions with different pH values ranging from 1 to 14 for 1 h. These characteristics make multifunctional cotton fabrics a satisfactory candidate in various promising fields. Materials science ; Photocatalysis ; Halloysite ; Composite material ; Contact angle ; Polydimethylsiloxane ; Silane ; Nanoparticle ; Coating ; Dip-coating ; Chemical engineering ; Biofouling ; Nanotechnology ; Catalysis ; Organic chemistry Pickering emulsions and particle stabilization
Authors: Yasir Butt | Salman Amin | Muhammad Rameez Sohail | Muhammad Bin Zubaid Ramay
Source: Pakistan Journal of Engineering and Technology (article, 2021-09-22)
Field: Materials Science — Materials Chemistry

Parametric change in Insulation Resistance and Breakdown Voltage of Underground PVC Cables under Accelerated Multistress Aging Conditions PVC is widely used as an electrical insulation for low-voltage indoor wiring and underground cabling. These cables are attacked by contaminants and chemicals present in the soil. The elevated temperatures and pressures inside the soil also adds up to stresses and deteriorates their insulations and sheaths and this eventually goes on damaging the conductors. So their performance in the contaminated service environment should be investigated considering extreme conditions in order to predict and ensure reliable service life. In this research 4 different PVC cable samples (2 core 10 mm2, 4 core 10 mm2, 4 core 120 mm2 and 4 core 240 mm2) were subjected to mechanical (via small punches), thermal, chemical and electrical stress over each core of the cable. Electrical stress was applied with 1 kV AC transformer. These samples were aged for 1000 hours under International Electrotechnical Commission (IEC) 1000 hours multistress aging conditions. Visual inspection and electrical characterization was done by Breakdown Voltage and Insulation Resistance Test for insulation and sheath at different intervals to investigate early stage degradation and reduction in lifetime. The results were compared with the characterizations already reported in the literature. Physical appearance and chemical degradations were apparent from the visual inspection of end aged samples. Quantitatively the results also indicated the continual decline in the breakdown voltage and insulation resistance over the aging time. Our findings inferred that the depreciation in quality of PVC cables (insulations and sheaths) is likely due to the chemical stresses which also instigates all other stresses Accelerated aging ; Materials science ; Composite material ; Service life ; Breakdown voltage ; Voltage ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Infrastructure Maintenance and Monitoring
Authors: Esther Heid | William H. Green
Source:  (preprint, 2021-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction The estimation of chemical reaction properties such as activation energies, rates or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction, and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark datasets for future work. All datasets and the developed reaction GCNN model are available online, free of charge and open-source. Cheminformatics ; Computer science ; Embedding ; Convolutional neural network ; Benchmark (surveying) ; Graph ; Artificial intelligence ; Molecular graph ; Machine learning ; Theoretical computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks