jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rodrigo A. Vargas–Hernández | K. Jorner | R. Pollice | A. Aspuru-Guzik
Source: arXiv (Cornell University) (preprint, 2022-11-30)
Field: Materials Science — Materials Chemistry

Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation Semi-empirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semi-empirical model still in widespread use in chemistry is Hückel's $π$-electron molecular orbital theory. In this work, we implemented a Hückel program using differentiable programming with the JAX framework, based on limited modifications of a pre-existing NumPy version. The auto-differentiable Hückel code enabled efficient gradient-based optimization of model parameters tuned for excitation energies and molecular polarizabilities, respectively, based on as few as 100 data points from density functional theory simulations. In particular, the facile computation of the polarizability, a second-order derivative, via auto-differentiation shows the potential of differentiable programming to bypass the need for numeric differentiation or derivation of analytical expressions. Finally, we employ gradient-based optimization of atom identity for inverse design of organic electronic materials with targeted orbital energy gaps and polarizabilities. Optimized structures are obtained after as little as 15 iterations, using standard gradient-based optimization algorithms. Automatic differentiation ; Differentiable function ; Density functional theory ; Polarizability ; Computer science ; Inverse ; Computation ; Applied mathematics ; Conjugate gradient method ; Computational chemistry ; Mathematical optimization ; Chemistry ; Physics ; Algorithm ; Quantum mechanics ; Mathematics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; Advanced Chemical Physics Studies
Authors: Steven R. Spurgeon | Nicholas Lewis | Yicheng Jin | Xiuyu Tang | Vidit Shah | Christina Doty | Bethany E. Matthews | Sarah Akers
Source: Research Square (preprint, 2022-07-21)
Field: Materials Science — Surfaces, Coatings and Films

Forecasting of In Situ Electron Energy Loss Spectroscopy Electron energy loss spectroscopy ; In situ ; Spectroscopy ; Electron ; Energy (signal processing) ; Physics ; Nuclear physics ; Meteorology ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Yannick Ureel | Maarten R. Dobbelaere | Yi Ouyang | Kevin De Ras | Maarten K. Sabbe | Guy Marin | Kevin M. Van Geem
Source: ChemRxiv (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead! By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine learning algorithms are maturing, its applications are lacking behind. Three types of challenges faced by active machine learning are identified and ways to overcome them are discussed: the convincing of the experimental researcher, the flexibility of data creation, and the robustness of the active machine learning algorithms. A bright future lies ahead for active machine learning in chemical engineering thanks to increasing automation and more efficient algorithms to drive novel discoveries. Active learning (machine learning) ; Machine learning ; Artificial intelligence ; Computer science ; Robustness (evolution) ; Computational learning theory ; Hyper-heuristic ; Flexibility (engineering) ; Instance-based learning ; Learning classifier system ; Online machine learning ; Robot learning ; Unsupervised learning ; Robot ; Mathematics ; Chemistry Machine Learning in Materials Science
Authors: David Moss
Source: Preprints.org (preprint, 2022-12-20)
Field: Materials Science — Materials Chemistry

Graphene Oxide Thin Films for Integrated Electronics and Photonics As a derivative of graphene, graphene oxide (GO) was initially developed by chemists to emulate some of the key properties of graphene, but it was soon recognized as a unique material in its own right, addressing an application space that is not accessible to chemical vapor deposition based materials. Over the past decade, GO has emerged as a new frontier material with tremendous advances in its material fabrication and quality control over its properties. These in turn have led to rapid progress in GO based photonics, electronics, and optoelectronics concepts and devices, evoking new science and paving the way for many technological breakthroughs with exceptional performance. Here, we review the unique fundamental characteristics of GO, its thin film fabrication methods, patterning techniques, and mechanisms for tuning its material properties. This latter capability in particular has enabled novel advanced functional photonic, electronic, and optoelectronic devices. Understanding these insights is essential for designing and tailoring GO properties for state-of-the-art applications including solar energy harvesting, energy storage, medical diagnosis, image displays, and optical communications. We conclude by discussing the open challenges and exciting opportunities of this field, together with future prospects for major technological advancements and breakthroughs. Graphene ; Nanotechnology ; Photonics ; Electronics ; Materials science ; Fabrication ; Engineering physics ; Computer science ; Engineering ; Optoelectronics ; Electrical engineering Graphene research and applications ; Graphene and Nanomaterials Applications ; 2D Materials and Applications
Authors: N. F. Sadiyeva | С. А. Искендерова | Yu. P. Cherepnova | L.M. Afandiyeva L.M. Afandiyeva | S.G. Aliyeva
Source: Azerbaijan journal of Chemical News (article, 2022-12-06)
Field: Chemistry — Spectroscopy

SYNTHESIS AND STUDY OF MIXED PROPYLENE GLYCOL DIESTERS OF SYNTHETIC PETROLEUM- AND FATTY ACIDS The article presents a one-stage method for the synthesis of mixed diesters based on propylene glycol, synthetic petroleum- and aliphatic fatty acids (C6-C8) using a ZnO catalyst under optimal conditions - temperature -110-120°C, amount of catalyst - 1.3% wt. (by acid), molar ratio of components - acid:alcohol - 2:1.3. The properties of the synthesized mixed diesters were studied and their parameters were determined by analytical and spectral methods. In order to determine the plasticizing properties of the mixed diesters, compositions were prepared under laboratory conditions with the addition 100 m.p. of polyvinyl chloride (PVC) for 30-70 m.p. of synthesized diesters and compared them with an industrial plasticizer - dioctyl phthalate. Also, these diesters have been studied as antioxidants that improve the thermal-oxidative stability of diesel fuel. An analysis of the research results shows that the synthesized mixed diesters can be proposed as effective plasticizers for PVC and new antioxidants for diesel fuels. Keywords: propylene glycol, synthetic petroleum acids, mixed diesters, diesel fuel, antioxidant, polyvinyl chloride, plasticizer Plasticizer ; Polyvinyl alcohol ; Chemistry ; Polyvinyl chloride ; Diesel fuel ; Organic chemistry ; Phthalate ; Catalysis ; Nuclear chemistry Adsorption, diffusion, and thermodynamic properties of materials
Authors: Mikhail G. Andronov | Varvara Voinarovska | Natalia Andronova | Michael Wand | Dirk‐André Clevert | Jürgen Schmidhuber
Source: ChemRxiv (preprint, 2022-11-14)
Field: Materials Science — Materials Chemistry

Reagent Prediction with a Molecular Transformer Improves Reaction Data Quality Automated synthesis planning is key for efficient generative chemistry. Since reactions of given reactants may yield different products depending on conditions such as the chemical context imposed by specific reagents, computer-aided synthesis planning should benefit from recommendations of reaction conditions. Traditional synthesis planning software, however, typically proposes reactions without specifying such conditions, relying on human organic chemists who know the conditions to carry out suggested reactions. In particular, reagent prediction for arbitrary reactions, a crucial aspect of condition recommendation, has been largely overlooked in cheminformatics until lately. Here we employ the Molecular Transformer, a state-of-the-art model for reaction prediction and single-step retrosynthesis, to tackle this problem. We train the model on the US patents dataset (USPTO) and test it on Reaxys to demonstrate its out-of-distribution generalization capabilities. Our reagent prediction model also improves the quality of product prediction: the Molecular Transformer is able to substitute the reagents in the noisy USPTO data with reagents that enable product prediction models to outperform those trained on plain USPTO. This allows to improve upon the state-of-the-art in reaction product prediction on the USPTO MIT benchmark. Reagent ; Transformer ; Computer science ; Benchmark (surveying) ; Cheminformatics ; Generalization ; Context (archaeology) ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Fangcheng Lv | Xiuquan Lu | Jingxuan Song | Meiying Zhu | Shenghui Wang | Yuqin Xu | Xiaobin Chang
Source: Research Square (preprint, 2022-06-17)
Field: Materials Science — Materials Chemistry

Enhanced aramid/Al2O3 interfacial properties by PDDA-modification for the preparation of composite insulating Paper Abstract For further improve the electrical insulation properties of meta-aramid paper(PMIA), polydimethyldiallyl ammonium chloride (PDDA) was used for the modification of nano-Al 2 O 3 in this work. The modified products were characterized by means of transmission electron microscopy (TEM), Fourier transform infrared spectrometer (FTIR) and liquid Zeta potential. The original and PDDA modified nano-Al 2 O 3 was doped into the aramid fiber composite, and the effects of PDDA and filler mass fraction on several properties such as electrical conductivity, breakdown strength and surface charge dissipation rate of insulating paper were studied. Finally, the interfacial properties of the composites were investigated using molecular dynamics (MD). The results showed that PDDA was successfully coated on the surface of nano-Al 2 O 3 , and its surface potential was changed from negative to positive, which could improve the interface characteristics between filler and matrix and enhance the dielectric strength of aramid paper. Besides, saturation effect of PDDA modification was observed, and when the amount of PDDA was 10% and the content of modified nano-Al 2 O 3 was 3%, the breakdown strength of aramid paper was increased by 20%, and the volume conductivity was decreased by 68%. The simulation result revealed that PDDA could improve the insulation performance of aramid paper by reducing the interfacial binding energy between aramid and filler. Aramid ; Materials science ; Composite material ; Fourier transform infrared spectroscopy ; Surface modification ; Zeta potential ; Composite number ; Conductivity ; Dielectric ; Chemical engineering ; Fiber ; Nanoparticle ; Nanotechnology ; Chemistry High voltage insulation and dielectric phenomena ; Advanced Cellulose Research Studies ; Dielectric materials and actuators
Authors: Mikhail Vokhmyanin | R. L. Vesnin | Valery Avdonin
Source: Materiale Plastice (article, 2022-04-04)
Field: Materials Science — General Materials Science

The Use of Products of Microwave Aminolytic Destruction of Polyethylene Terephthalate in Vibration-absorbing Polymer Composite Materials The aim of this work is to study the possibility of using the products of aminolytic destruction of polyethylene terephthalate (PET) and their derivatives in polymer composite materials with vibration-absorbing properties, which provide effective damping of vibrations of technogenic and natural origin in a wide temperature and frequency range. The paper considers a modern method of recycling PET waste with a mixture of amino alcohols - monoethanolamine and triethanolamine, taken in two different ratios. As a result of the destruction reaction, terephthalic acid diamide (N, N -bis (2-hydroxyethyl) terephthalamide) is formed. To accelerate the destruction process, microwave radiation of various powers of 200, 540 and 700 watts was used. The optimal conditions for aminolytic decomposition of PET were determined: the time and power of microwave radiation with PET conversion up to 95% and the yield of the target product (terephthalic acid diamide) 80-85%. The destruction process was carried out according to a closed cycle of using reagents, without the use of catalysts and at atmospheric pressure, which to a certain extent reduces the energy consumption and increases the environmental friendliness of this method of PET decomposition. The aminolytic degradation product of PET (terephthalic acid diamide) was used as a monomer in the polycondensation reaction to obtain a new oligomer (terephthalic acid oligoesteramide). The degree of polymerisation (n) is in this case 7 to 11 (number of chain links). The obtained oligomer and the PET degradation product were investigated as new components in elastomeric compositions based on chloroprene rubber and in compositions based on thermoplastic elastomers. It has been demonstrated that the introduction of an oligomer based on a PET degradation product reduces the viscosity of elastomeric compositions by 25-35%. The study was supported by a grant from the Russian Science Foundation No. 21-79-00301, https://rscf.ru/project/21-79-00301/. Terephthalic acid ; Polyethylene terephthalate ; Materials science ; Polymer ; Chemical engineering ; Condensation polymer ; Monomer ; Decomposition ; Oligomer ; Polymer chemistry ; Polymerization ; Composite material ; Polyester ; Organic chemistry ; Chemistry Material Properties and Applications ; Polymer Synthesis and Characterization ; Railway Systems and Materials Science
Authors: Sunghwan Choi
Source: Research Square (preprint, 2022-09-29)
Field: Materials Science — Materials Chemistry

Prediction of Transition State Structures of General Chemical Reactions via Machine Learning Abstract The elucidation of transition state (TS) structures is ssential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite advances in computational approaches, TS searches remain still a challenging problem due to the difficulty of constructing an initial structure and heavy computational costs. Herein, a novel machine learning (ML) model for predicting TS structures of general organic reactions is proposed. The proposed model derives interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model shows excellent accuracy, particularly for the atomic pairs where bond formation or breakage occurs. The predicted TS structures result in a high success ratio (93.8%) of quantum chemical saddle-point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal/mol. Additionally, as a proof-of-concept, exploring multiple reaction paths of an organic reaction is demonstrated with the ML inferences. I envision that the proposed approach will aid the construction of initial geometry for TS optimization and reaction path explorations. Transition (genetics) ; State (computer science) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: D. Merten | Julian A. Singer | H.L. Fiedler | Stefan Tappertzhofen
Source: SN Applied Sciences (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

Concept of an efficient self-startup voltage converter with dynamic maximum power point tracking for microscale thermoelectric generators Abstract Microscale Thermoelectric Generators (microTEGs) have a high application potential for energy harvesting for autonomous microsystems. In contrast to conventional thermoelectric generators, microTEGs can only supply small output-voltages. Therefore, voltage converters are required to provide supply-voltages that are sufficiently high to power microelectronics. However, for high conversion efficiency, voltage converters need to be optimized for the limited input voltage range and the typically high internal resistance of microTEGs. To overcome the limitations of conventional voltage converters we present an optimized self-startup voltage converter with dynamic maximum power point tracking. The performance potential of our concept is theoretically and experimentally analyzed. The voltage conversion interface demonstrates energy harvesting from open-circuit voltages as low as 30.7 mV, and enables independent and full start-up from 131 mV. No additional external power supply is required at any time during operation. It can be operated with a wide range of internal resistances from 20.6 to − 4 kΩ with a conversation efficiency between η = 68–79%. Voltage ; Electrical engineering ; Converters ; Thermoelectric generator ; Maximum power point tracking ; Power (physics) ; Voltage reference ; Internal resistance ; Materials science ; Microscale chemistry ; Electronic engineering ; Thermoelectric effect ; Engineering ; Physics ; Battery (electricity) Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Thermal properties of materials
Authors: Asem A. Talaspaeva | Gulnar K. Ibraishina | Saule Nurbay
Source: Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales (article, 2022-12-01)
Field: Materials Science — Polymers and Plastics

The issue of the development of material package for protective cloth designing Introduction: The article presents the characteristics of existing materials of top, heat-insulating and lining materials which are used for the design of protective clothing. A number of comparative analyses of the studied materials for further use them as a package in overalls was carried out. Materials and Methods: When writing, various methods were used, such as the comparison method, the historical method, the market analysis method, and others. Results and Discussion: We have researched the various fabrics and non-fabrics produced and used in industry that fall under the category of protective fabrics. Conclusions: As a result of a comparative, bibliometric analysis, it was revealed that a wide variety of different special-purpose materials is currently on the market. Clothing ; Variety (cybernetics) ; Architectural engineering ; Computer science ; Engineering ; Forensic engineering ; Artificial intelligence Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: Pearl Raven Shannine D Avestru | Dhinniell D. Galvez | Ma. Esperanza Mae L Morong | Julie Jill R. Torino | Aubrey May A. Villegas | Erwin M. Faller
Source: International Journal of Research Publication and Reviews (review, 2022-07-05)
Field: Chemistry — Analytical Chemistry

A REVIEW ON THE TOXICOLOGICAL EFFECTS OF FOOD PRESERVATIVES USING ALLIUM CEPA L. AS TEST PLANT Food preservatives are predominant in preserving foods to steer clear of food deterioration, spoilage and poisoning. The effects of food preservatives have minor and major concerns in terms of health. Many researchers have reported that some of these preservatives can even cause death. With that, this review article focuses on summarizing the main toxicological findings of five certain preservatives of food to Allium Cepa L. as the test plant. The review article is the outturn of different literature reviews of all relevant papers from peer review and high impact journal s. In an effort to pique the reader’s interest in the selected food preservatives that were being tested in Allium cepa. Cytologic tests result in a Cytological abnormality that the mitotic index of the tested Allium cepa treatment group decreases as it was compared to the controlled groups. As indicated, there are Cytological abnormalities in which the deoxyribonucleic acid synthesis inhibited its activity that resulted in inhibition of plant growth and a drastic reduction of its protein content. Furthermore, food preservatives play a crucial role in preserving food shelf life and its physical appearance through time. Some studies have found that preservatives have high effects in genetics which are considered lethal and dangerous to any living species. As time progresses, further studies and research may be able to obtain a drastic change and a compelling result in the preservation of foods that affect every organism. Allium ; Preservative ; Food Preservatives ; Food spoilage ; Toxicology ; Biology ; Biotechnology ; Food science ; Horticulture ; Genetics Dye analysis and toxicity
Authors: Minjie Chen
Source:  (preprint, 2022-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Power Magnetics &lt;p&gt;This paper applies machine learning to modeling power magnetics. We first introduce an open-source database - MagNet - which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling core losses and B–H loops. Machine learning allows the many factors that may influence the magnetic characteristics being modeled in a unified framework, and provides insights to quantify the complexity of magnetic characteristics and reduce the size of the measurement data required to build a precise model. Neural network models are found effective in compressing the measurement data and predicting the behaviors of magnetic materials such as the core loss and the B–H loop. The behaviors of a typical power magnetic material (e.g., TDK N87) across a wide range of operating conditions (e.g., temperature, waveform, dc-bias) can be well described by a small-scale neural network (200 KB) which is 10,000 times smaller than the raw measured time-series data (2 GB), paving the way toward using neural networks as an interactive datasheet to assist magnetics design.&lt;/p&gt; Datasheet ; Artificial neural network ; Waveform ; Power (physics) ; Computer science ; Core (optical fiber) ; Magnet ; Electronic engineering ; Artificial intelligence ; Engineering ; Electrical engineering ; Voltage ; Physics Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Non-Destructive Testing Techniques