jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Narak Choi | Keng Heng Lai | Arvind Sundaram | Navab Singh | Yuan Hsing Fu | Lennon Y. T. Lee
Source:  (article, 2023-03-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Model-based optical proximity correction for immersion lithography-based flat optics platform We introduce well-developed optical proximity correction (OPC) techniques to the metasurface-based flat optics manufacturing process. Flat optics, formed by subwavelength scale nanostructure pillar (nanopillar) array, so called metasurface, has become promising substitutes for conventional bulky optical components. For its manufacturing, photolithography is preferable rather than the electron beam lithography (EBL) technique because of its time and cost effectiveness for mass manufacturing. However, the required feature size and pitch of the metasurface for the visible light is approaching the process limit of the ArF immersion lithography. It results in critical dimension (CD) errors due to optical proximity effect and could result in efficiency degradation of the flat optics. In the semiconductor manufacturing industry, OPC based on process modelling and numerical computation has been developed for the last few decades to control the CD on the wafer. Here, a machine learning (ML) model is constructed to avoid the time consumption of the conventional OPC method without losing the accuracy. Various pitches of flat optics metalens, from 465 nm to 160 nm, has been studied for the implementation of the ML OPC. The root mean square (RMS) CD errors &lt; 1 nm and the CD accuracies &lt; 6 nm can be achieved. The CD error percentages over the pillar diameters &lt; 6 % is observed and the improvement of CD error and CD accuracy compared to rule based OPC in small pitches of metalens is demonstrated. Immersion lithography ; Photolithography ; Optical proximity correction ; Lithography ; Wafer ; Materials science ; Optics ; Photomask ; Computational lithography ; Critical dimension ; Semiconductor device fabrication ; Electron-beam lithography ; Next-generation lithography ; Nanopillar ; Optoelectronics ; Extreme ultraviolet lithography ; Nanostructure ; X-ray lithography ; Nanotechnology ; Physics ; Resist Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Plasmonic and Surface Plasmon Research
Authors: Min Li | Zhengyuan Shi | Qiuxia Lai | Sadaf Khan | Shaowei Cai | Qiang Xu
Source:  (article, 2023-07-09)
Field: Materials Science — Materials Chemistry

On EDA-Driven Learning for SAT Solving We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic ‘1’ as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions. Computer science ; Boolean satisfiability problem ; Theoretical computer science ; Satisfiability ; Generalization ; Boolean function ; Electronic design automation ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics Machine Learning in Materials Science ; Software Engineering Research
Authors: Georgios Makrygiorgos | Giovanni Maria Maggioni | Ali Mesbah
Source: Organic Process Research & Development (article, 2023-11-20)
Field: Chemistry — Analytical Chemistry

A Physics-Assisted Data-Driven Framework for Predicting Crystal Chord Length Distributions under Arbitrary Experimental Conditions Despite advances in process analytical technologies, the in situ measurement of particle size and shape distributions remains a major challenge in industrial crystallization. Focused beam reflectance measurement (FBRM) is widely used for in situ measurement of the chord length distribution (CLD). However, CLD measurements must be related to the particle size distribution (PSD) to facilitate process development and optimization. Towards addressing the inverse problem of inferring PSDs from CLD measurements, this paper presents a physics-assisted data-driven framework for predicting CLDs from PSDs under varying experimental conditions. The proposed framework relies on data generated via first-principles simulations of geometric CLDs, which are used to construct a convolutional neural network (CNN) that maps PSDs to geometric CLDs. By discretizing PSDs into finite bins, multidimensional PSDs can be effectively treated as “images”. After reducing the dimension of predicted geometric CLDs using a deep autoencoder, a Gaussian process model is used to correct for the discrepancy between the CNN predictions and measured CLDs while accounting for experimental parameters such as suspension density and mixing. The proposed framework is demonstrated on three different industrial crystallization systems that produce crystals with a needle-like morphology but with varying aspect ratios, colors, and transparencies, while the suspension properties are also different. We show that the framework can predict CLDs with industrially pragmatic accuracy while allowing for quantifying the uncertainty of predictions. The proposed framework relies on a small amount of system-specific measurements, which makes it particularly versatile and useful for process troubleshooting and optimization when a large amount of system-specific experimental data may not be accessible. Computer science ; Algorithm ; Process (computing) ; Biological system ; Materials science Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Image Retrieval and Classification Techniques
Authors: Maryam Irandoost | Beena Kumari | Tuyen Truong | Bhishma Karki | Md Rahimullah Miah
Source: Journal of Composites and Compounds (review, 2023-03-01)
Field: Chemistry — Electrochemistry

Nickel sulfide-based composite as electrodes in electrochemical sensors: A review Nickel sulfide (NiS) is an extremely a transition metal sulfide with great potential use as a sensor material because of its exceptional conductivity and stability. Herein, we present first, the all of synthesis of NiS into sensor and biosensor. Electrochemical sensor, Due to the fact that disposal to electrolyte during electrochemical impact can rapidly deform NiS, lowering its electroactivity and measurement repeatability, a method for effectively integrating NiS into sensors is crucial. Then, the main focus of this review is the recent advancements in sensor systems that utilize NiS and its composites. The article discusses the correlation between sensing performance and electrode construction strategies, and identifies shortcomings and limitations in the current applications of these sensors. Based on this analysis, the authors suggest potential future directions and areas for further research in the development of NiS-based sensors. This study focused on developments in NiS-based sensor systems and their composites throughout the past articles. The article investigates the correlation between the way electrodes are made and the effectiveness of the sensors they produce. On this basis, we discuss the scope for future of NiS-based sensors and offer additional directions. Nickel sulfide ; Sulfide ; Composite number ; Electrochemistry ; Nickel ; Electrode ; Materials science ; Metallurgy ; Chemistry ; Composite material Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Dean Miller | Kristen Abels | Jinyu Guo | Kindle Williams | Matthew Tingchi Liu | William A. Tarpeh
Source: ChemRxiv (preprint, 2023-04-05)
Field: Chemistry — Electrochemistry

Electrochemical wastewater refining: a vision for circular chemical manufacturing Wastewater is an underleveraged resource; it contains pollutants that can be transformed into valuable high-purity products. Innovations in chemistry and chemical engineering will play critical roles in valorizing wastewater to remediate environmental pollution, provide equitable access to chemical resources and services, and secure critical materials from diminishing feedstock availability. This perspective envisions electrochemical wastewater refining—the use of electrochemical processes to tune and recover specific products from wastewaters—as the necessary framework to accelerate wastewater-based electrochemistry to widespread practice. We define and prescribe a use-informed approach that simultaneously serves specific wastewater-pollutant-product triads and uncover mechanistic understanding generalizable to broad use cases. We use this approach to evaluate research needs in specific case studies of electrocatalysis, stoichiometric electrochemical conversions, and electrochemical separations. Finally, we provide rationale and guidance for intentionally expanding the electrochemical wastewater refining product portfolio. Wastewater refining will require a coordinated effort from multiple expertise areas to meet the urgent need of extracting maximal value from complex, variable, diverse, and abundant wastewater resources. Wastewater ; Refining (metallurgy) ; Sewage treatment ; Pollutant ; Environmental science ; Product (mathematics) ; Biochemical engineering ; Waste management ; Computer science ; Process engineering ; Environmental engineering ; Engineering ; Materials science ; Chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Yael Cytter | Aditya Nandy | Chenru Duan | Heather J. Kulik
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models Virtual high-throughput screening (VHTS) and machine learning (ML) with density functional theory (DFT) suffer from inaccuracies from the underlying density functional approximation (DFA). Many of these inaccuracies can be traced to the lack of derivative discontinuity that leads to a curvature in the energy with electron addition or removal. Over a dataset of nearly one thousand transition metal complexes typical of VHTS applications, we computed and analyzed the average curvature (<i>i.e.</i>, deviation from piecewise linearity) for 23 density functional approximations spanning multiple rungs of "Jacob's ladder". While we observe the expected dependence of the curvatures on Hartree-Fock exchange, we note limited correlation of curvature values between different rungs of "Jacob's ladder". We train ML models (<i>i.e.</i>, artificial neural networks or ANNs) to predict the curvature and the associated frontier orbital energies for each of these 23 functionals and then interpret differences in curvature among the different DFAs through analysis of the ML models. Notably, we observe spin to play a much more important role in determining the curvature of range-separated and double hybrids in comparison to semi-local functionals, explaining why curvature values are weakly correlated between these and other families of functionals. Over a space of 187.2k hypothetical compounds, we use our ANNs to pinpoint DFAs for which representative transition metal complexes have near-zero curvature with low uncertainty, demonstrating an approach to accelerate screening of complexes with targeted optical gaps. Curvature ; Piecewise ; Density functional theory ; Statistical physics ; Hybrid functional ; Algorithm ; Mathematics ; Physics ; Quantum mechanics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Halil Ekici
Source: Uluslararası Doğu Anadolu Fen Mühendislik ve Tasarım Dergisi (article, 2023-06-04)
Field: Materials Science — Materials Chemistry

Production Methods and Energy Applications of Borophene Two dimensional materials have unique physical, chemical and electrical properties. Many theoretical studies on borophene revealed important possible properties, such as metallicity, transparency, conductivity and chemical activity; due to vacancies present in its structure. It was first synthesized in 2015 and experimental studies on this new exciting 2D material is few in number. It was found that many of these studies still focus on expensive and sophisticated production methods such as Molecular Beam Epitaxy (MBE) and Chemical Vapor Deposition (CVD). Although graphene, first of these 2D materials, is being studied more than two decades, it could not do the expected impact on energy industry due to lack of production methods in industrial scale. In this review, it was intended to build a narrative on the experimental production methods and resulting structures of borophene rather than theoretical background. It was found that liquid phase exfoliation method to synthesize borophene sheets might be the most promising method to upscale borophene production. Theoretical, computational and experimental studies suggested that β12 and χ3 borophene structures were stable and could be produced by sonochemical exfoliation method. In addition, possible uses in energy applications and some future prospects were also discussed. Borophene produced this way, might be used in batteries, supercapacitors hydrogen evolution (HER) and oxygen evolution (OER) reactions. Borophene ; Materials science ; Nanotechnology ; Graphene ; Exfoliation joint ; Chemical vapor deposition Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Katja‐Sophia Csizi | Miguel Steiner | Markus Reiher
Source: ChemRxiv (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

Quantum Magnifying Glass for Chemistry at the Nanoscale Nanoscopic systems exhibit diverse molecular substructures by which they facilitate spe- cific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid mod- els come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mecha- nism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a quantum magnifying glass that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction ex- ploration. It represents a unique approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demon- strate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability. Quantum ; Parametrization (atmospheric modeling) ; Nanoscopic scale ; Computer science ; Quantum state ; Nanotechnology ; Statistical physics ; Physics ; Materials science ; Quantum mechanics Electronic and Structural Properties of Oxides ; Luminescence and Fluorescent Materials ; Machine Learning in Materials Science
Authors: Evgeny V. Podryabinkin | Kamil Garifullin | Alexander V. Shapeev | Ivan S. Novikov
Source: arXiv (Cornell University) (preprint, 2023-04-25)
Field: Materials Science — Materials Chemistry

MLIP-3: Active learning on atomic environments with Moment Tensor Potentials Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on sharing computer codes developed within the community. In the field of atomistic modeling these were software packages for classical atomistic modeling, later -- quantum-mechanical modeling, and now with the fast growth of the field of machine-learning potentials, the packages implementing such potentials. In this paper we present the MLIP-3 package for constructing moment tensor potentials and performing their active training. This package builds on the MLIP-2 package (Novikov et al. (2020), The MLIP package: moment tensor potentials with MPI and active learning. Machine Learning: Science and Technology, 2(2), 025002.), however with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation. Computer science ; Tensor (intrinsic definition) ; Moment (physics) ; Field (mathematics) ; Software package ; Software ; Computational science ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Oscar Ramírez | Sebastián Castillo | Sebastián Bonardd | César Saldías | James N. O’Shea | Christopher Philip Clive | David Díaz Díaz | Ángel Leiva
Source: Journal of environmental chemical engineering (article, 2023-09-16)
Field: Chemistry — Organic Chemistry

Marrying plasmonic earth-abundant metals with catalytic metals for visible-light-promoted hydrogen generation on biobased materials Bimetallic CuPt alloyed nanoparticles were conveniently synthesized on biohydrogels and were capable of carrying out hydrogen release from ammonium borane hydrolysis. The biohydrogel consisted of bead-shaped alginate chains crosslinked by calcium ions, which were used as support material to synthesize and stabilize the bimetallic nanoparticles, employing adsorption and coreduction strategy steps. The as-prepared nanoparticles exhibited light absorption in the visible range (580 nm) resulting from the surface plasmon resonance (SPR) phenomenon ascribed to the presence of copper in the alloyed system. On the other hand, the presence of platinum atoms in these nanoalloys endows them with a notable catalytic performance toward ammonia borane hydrolysis as a hydrogen release reaction, reaching kr values from 0.32 ×10-4 to 2.23 ×10-4 mol L-1 min-1 as the Pt content increases. Finally, by taking advantage of the SPR light absorption shown by CuPt 1:1, it was demonstrated that these entities could be successfully employed as photocatalysts for the hydrogen generation reaction, boosting its activity by almost 2.06 times compared to its performance in dark conditions. This catalytic enhancement was mainly ascribed to the light-harvesting properties promoted by plasmonic effects and the specimen's metallic composition. Ammonia borane ; Bimetallic strip ; Catalysis ; Nanoparticle ; Materials science ; Adsorption ; Hydrogen production ; Visible spectrum ; Hydrogen ; Inorganic chemistry ; Hydrolysis ; Photocatalysis ; Surface plasmon resonance ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry Nanomaterials for catalytic reactions ; Hydrogen Storage and Materials ; Advanced Photocatalysis Techniques
Authors: Daria M. Arkhipova | V. V. Ermolaev | G.R. Baembitova | Aida I. Samigullina | Anna P. Lyubina | Alexandra D. Voloshina
Source: Polymers (article, 2023-10-16)
Field: Chemistry — Organic Chemistry

Oxygen-Containing Quaternary Phosphonium Salts (oxy-QPSs): Synthesis, Properties, and Cellulose Dissolution In the present study, the synthesis of oxygen-containing quaternary phosphonium salts (oxy-QPSs) was described. Within this work, structure-property relationships of oxy-QPSs were estimated by systematic analysis of physical-chemical properties. The influence of the oxygen-containing substituent was examined by comparing the properties of oxy-QPSs in homology series as well as with phosphonium analog-included alkyl side chains. The crystal structure analysis showed that the oxygen introduction influences the conformation of the side chain of the oxy-QPS. It was found that oxy-QPSs, using an aprotic co-solvent, dimethylsulfoxide (DMSO), can dissolve microcrystalline cellulose. The cellulose dissolution in oxy-QPSs appeared to be dependent on the functional group in the cation and anion nature. For the selected conditions, dissolution of up to 5 wt% of cellulose was observed. The antimicrobial activity of oxy-QPSs under study was expected to be low. The biocompatibility of oxy-QPSs with fermentative microbes was tested on non-pathogenic <i>Saccharomyces cerevisiae</i>, <i>Lactobacillus plantarum</i>, and <i>Bacillus subtilis</i>. This reliably allows one to safely address the combined biomass destruction and enzyme hydrolysis processes in one pot. Phosphonium ; Chemistry ; Cellulose ; Dissolution ; Hydrolysis ; Microcrystalline cellulose ; Organic chemistry Antimicrobial agents and applications ; Chemical Synthesis and Reactions ; Chemical Synthesis and Characterization
Authors: Matthew S. Johnson | Maciej Gierada | Eric Hermes | David H. Bross | Khachik Sargsyan | Habib N. Najm | Judit Zádor
Source: ChemRxiv (preprint, 2023-05-29)
Field: Materials Science — Materials Chemistry

Pynta - An automated workflow for calculation of surface and gas-surface kinetics Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate and gas-phase species, many distinct reaction classes and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for inter-adsorbate group transfers and reactions on high index surfaces. Saddle point ; Saddle ; Computer science ; Computation ; Workflow ; Point (geometry) ; Surface (topology) ; Algorithm ; Computational science ; Chemistry ; Mathematical optimization ; Mathematics ; Geometry ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions