jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammed Al‐Fahdi | Tao Ouyang | Ming Hu
Source: Journal of Materials Chemistry A (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning Novel carbon allotropes and ternary B–C–N structures with ultrahigh hardness were screened and proposed by high-throughput computation. Electronic-level insights into superhard materials were provided from machine learning. Ternary operation ; Computation ; Throughput ; Materials science ; Carbon fibers ; Computational science ; Nanotechnology ; Computer science ; Composite material ; Algorithm ; Composite number Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Hayley Weir | Keiran C. Thompson | Amelia Woodward | Benjamin Choi | Augustin Braun | Todd J. Martı́nez
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ∼600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks where each network casts one vote for the predicted molecule, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The ensemble model achieved an accuracy of 76% on hand-drawn hydrocarbons, increasing to 86% if the top 3 predictions were considered. Artificial intelligence ; Deep learning ; Computer science ; Artificial neural network ; Hydrocarbon ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Enhanced Oil Recovery Techniques
Authors: Daniele Dragoni | Jörg Behler | Marco Bernasconi
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material Elemental antimony has been recently proposed as a promising material for phase change memories with improved performances with respect to the most used ternary chalcogenide alloys. The compositional simplification prevents reliability problems due to demixing of the alloy during memory operation. This is made possible by the dramatic stabilization of the amorphous phase once Sb is confined in an ultrathin film 3-5 nm thick. In this work, we shed light on the microscopic origin of this effect by means of large scale molecular dynamics simulations based on an interatomic potential generated with a machine learning technique. The simulations suggest that the dramatic reduction of the crystal growth velocity in the film with respect to the bulk is due to the effect of nanoconfinement on the fast <i>β</i> relaxation dynamics while the slow <i>α</i> relaxation is essentially unaffected. Materials science ; Crystallization ; Amorphous solid ; Monatomic gas ; Phase (matter) ; Chemical physics ; Mechanism (biology) ; Phase change ; Atomic units ; Nanotechnology ; Chemical engineering ; Crystallography ; Chemistry ; Thermodynamics ; Physics Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Advanced Thermoelectric Materials and Devices
Authors: Tetiana Zubatiuk | Benjamin Nebgen | Nicholas Lubbers | Justin S. Smith | R.I. Zubatyuk | Guoqing Zhou | Christopher Koh | Kipton Barros | Olexandr Isayev | Sergei Tretiak
Source: The Journal of Chemical Physics (article, 2021-06-25)
Field: Materials Science — Materials Chemistry

Machine learned Hückel theory: Interfacing physics and deep neural networks The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Overall, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models. Artificial neural network ; Interpretability ; Interfacing ; Statistical physics ; Parameterized complexity ; Simple (philosophy) ; Computer science ; Simplicity ; Hamiltonian (control theory) ; Physics ; Artificial intelligence ; Theoretical physics ; Mathematics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hidetoshi Miyazaki | Tomoyuki Tamura | Masashi Mikami | Kosuke Watanabe | Naoki Ide | Osman Murat Özkendir | Yoichi Nishino
Source: Scientific Reports (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials. Thermal conductivity ; Materials science ; Lattice (music) ; Crystal structure ; Thermoelectric materials ; Thermoelectric effect ; Condensed matter physics ; Atomic mass ; Atomic radius ; Thermal conduction ; Thermodynamics ; Chemistry ; Physics ; Crystallography ; Atomic physics ; Composite material ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Rahmad Syah | Al-Khowarizmi Al-Khowarizmi | Marischa Elveny | Afrasyab Khan
Source: Environmental Technology & Innovation (article, 2021-07-20)
Field: Materials Science — Materials Chemistry

Machine learning based simulation of water treatment using LDH/MOF nanocomposites Adsorption ; Nanocomposite ; Aqueous solution ; Thallium ; Materials science ; Volume (thermodynamics) ; Artificial neural network ; Work (physics) ; Hydroxide ; Chemical engineering ; Process engineering ; Computer science ; Chemistry ; Thermodynamics ; Nanotechnology ; Artificial intelligence ; Engineering ; Organic chemistry ; Physics Magnesium Oxide Properties and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Thallium and Germanium Studies
Authors: Brandon J. Yik | Amber J. Dood | Daniel Cruz‐Ramírez de Arellano | Kimberly B. Fields | Jeffrey R. Raker
Source: Chemistry Education Research and Practice (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Development of a machine learning-based tool to evaluate correct Lewis acid–base model use in written responses to open-ended formative assessment items Acid–base chemistry is a key reaction motif taught in postsecondary organic chemistry courses. More specifically, concepts from the Lewis acid–base model are broadly applicable to understanding mechanistic ideas such as electron density, nucleophilicity, and electrophilicity; thus, the Lewis model is fundamental to explaining an array of reaction mechanisms taught in organic chemistry. Herein, we report the development of a generalized predictive model using machine learning techniques to assess students’ written responses for the correct use of the Lewis acid–base model for a variety ( N = 26) of open-ended formative assessment items. These items follow a general framework of prompts that ask: why a compound can act as (i) an acid, (ii) a base, or (iii) both an acid and a base ( i.e. , amphoteric)? Or, what is happening and why for aqueous proton-transfer reactions and reactions that can only be explained using the Lewis model. Our predictive scoring model was constructed from a large collection of responses ( N = 8520) using a machine learning technique, i.e. , support vector machine, and subsequently evaluated using a variety of validation procedures resulting in overall 84.5–88.9% accuracies. The predictive model underwent further scrutiny with a set of responses ( N = 2162) from different prompts not used in model construction along with a new prompt type: non-aqueous proton-transfer reactions. Model validation with these data achieved 92.7% accuracy. Our results suggest that machine learning techniques can be used to construct generalized predictive models for the evaluation of acid–base reaction mechanisms and their properties. Links to open-access files are provided that allow instructors to conduct their own analyses on written, open-ended formative assessment items to evaluate correct Lewis model use. Lewis acids and bases ; Formative assessment ; Artificial intelligence ; Chemistry ; Computer science ; Machine learning ; Mathematics education ; Organic chemistry ; Mathematics Various Chemistry Research Topics ; Machine Learning in Materials Science ; Educational Strategies and Epistemologies
Authors: Xinxiao Chen | Yi Zhuo | Yiyu Zhou | Peixi Guo | Saeid Gholami Farkoush | Hossein Niroumandi
Source: Energy Reports (article, 2021-06-11)
Field: Materials Science — Materials Chemistry

Artificial neural network modeling and optimization of the Solid Oxide Fuel Cell parameters using grey wolf optimizer Using Green and carbon-free energy sources is a new concept in the energy conversion, power generation, and energy management framework. Since there is a relatively small number of neural network applications in the field of fuel cells, especially in the case of solid oxide fuel cells, this work adopts the Artificial Neural Network model for modeling aims according to the empirical datasets. Besides, a new optimization method is applied to optimize the solid oxide fuel cell efficiency. The grey wolf optimizer with fast, robust, and simple features is applied to obtain the optimal operational variables of solid oxide fuel cells. The key operational parameters used for the optimization comprise the thickness of the anode support layer, the porosity of the anode layer, the thickness of the electrolyte layer, and the thickness of the cathode layer. The modeling results compared to the laboratory that confirms the ability of the artificial neural network model and optimization method in parameter identification. Two case study optimization procedure was assessed. Firstly, the variables optimized under the operational temperature of 800 °C and the values of 19 μm, 0.52 mm, 62.16 μm, and 75% are obtained for the electrolyte layer thickness, anode support layer thickness, cathode thickness, and anode support layer porosity, respectively. For the second case study, the power density based on the suggested method maximized up to 28% compared to the experimental results. Anode ; Cathode ; Electrolyte ; Artificial neural network ; Solid oxide fuel cell ; Materials science ; Oxide ; Power density ; Porosity ; Computer science ; Biological system ; Process engineering ; Layer (electronics) ; Power (physics) ; Chemical engineering ; Electrode ; Nanotechnology ; Composite material ; Engineering ; Electrical engineering ; Artificial intelligence ; Chemistry ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Yijia Sun | Nikolaos V. Sahinidis
Source: Current Opinion in Chemical Engineering (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

Computer-aided retrosynthetic design: fundamentals, tools, and outlook Retrosynthetic analysis ; Ranking (information retrieval) ; Computer science ; Computer-aided ; Engineering ; Artificial intelligence ; Machine learning ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Lei Liu | Zhaolun Zhang | Lihua Cao | Ziyi Xiong | Ying Tang | Yao Pan
Source: Sustainable Chemistry and Pharmacy (article, 2021-03-27)
Field: Materials Science — Materials Chemistry

Cytotoxicity of phytosynthesized silver nanoparticles: A meta-analysis by machine learning algorithms Silver nanoparticle ; Cytotoxicity ; Nanoparticle ; Algorithm ; Computer science ; Artificial intelligence ; Chemistry ; Nanotechnology ; Materials science ; In vitro ; Biochemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Lusann Yang | Joel A. Haber | Zan Armstrong | Samuel Yang | Kevin Kan | Lan Zhou | Matthias H. Richter | C. Roat | Nicholas Wagner | Marc Coram | Marc Berndl | Patrick Riley | John M. Gregoire
Source: Proceedings of the National Academy of Sciences (article, 2021-09-10)
Field: Materials Science — Materials Chemistry

Discovery of complex oxides via automated experiments and data science The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery. Workflow ; Oxide ; Transparency (behavior) ; Materials science ; Computer science ; Catalysis ; Phase (matter) ; Metal ; Nanotechnology ; Chemistry ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Weng‐I Katherine Chio | Huimin Xie | Yuewen Zhang | Yang Lan | Tung‐Chun Lee
Source: TrAC Trends in Analytical Chemistry (article, 2021-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS biosensors based on cucurbituril-mediated nanoaggregates for wastewater-based epidemiology Cucurbituril ; Nanotechnology ; Biosensor ; Context (archaeology) ; Materials science ; Molecule ; Chemistry ; Supramolecular chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Ayad A.H. Faisal | Zahraa Khalid Ramadhan | Nadhir Al‐Ansari | Gaurav Sharma | Mu. Naushad | Chinna Bathula
Source: Chemosphere (article, 2021-10-26)
Field: Materials Science — Materials Chemistry

Precipitation of (Mg/Fe-CTAB) - Layered double hydroxide nanoparticles onto sewage sludge for producing novel sorbent to remove Congo red and methylene blue dyes from aqueous environment Sorbent ; Sorption ; Hydrotalcite ; Aqueous solution ; Chemistry ; Nuclear chemistry ; Adsorption ; Precipitation ; Hydroxide ; Methylene blue ; Langmuir adsorption model ; Sewage sludge ; Congo red ; Langmuir ; Chromatography ; Inorganic chemistry ; Sewage treatment ; Photocatalysis ; Environmental engineering ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Phosphorus and nutrient management
Authors: Iyyakkannu Sivanesan | Judy Gopal | Manikandan Muthu | Juhyun Shin | Jae‐Wook Oh
Source: Polymers (review, 2021-07-16)
Field: Materials Science — Biomaterials

Reviewing Chitin/Chitosan Nanofibers and Associated Nanocomposites and Their Attained Medical Milestones Chitin/chitosan research is an expanding field with wide scope within polymer research. This topic is highly inviting as chitin/chitosan's are natural biopolymers that can be recovered from food waste and hold high potentials for medical applications. This review gives a brief overview of the chitin/chitosan based nanomaterials, their preparation methods and their biomedical applications. Chitin nanofibers and Chitosan nanofibers have been reviewed, their fabrication methods presented and their biomedical applications summarized. The chitin/chitosan based nanocomposites have also been discussed. Chitin and chitosan nanofibers and their binary and ternary composites are represented by scattered superficial reports. Delving deep into synergistic approaches, bringing up novel chitin/chitosan nanocomposites, could help diligently deliver medical expectations. This review highlights such lacunae and further lapses in chitin related inputs towards medical applications. The grey areas and future outlook for aligning chitin/chitosan nanofiber research are outlined as research directions for the future. Chitin ; Chitosan ; Nanofiber ; Nanocomposite ; Nanotechnology ; Materials science ; Polymer science ; Chemical engineering ; Engineering Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging ; Advanced Sensor and Energy Harvesting Materials
Authors: Li Cao | Lei Wang
Source: Protein Science (review, 2021-11-11)
Field: Chemistry — Organic Chemistry

New covalent bonding ability for proteins To expand protein's covalent bonding ability, latent bioreactive unnatural amino acids have been designed and genetically encoded into proteins, which react with specific natural amino acid residues through proximity-enabled bioreactivity. The resultant new covalent bonds can be selectively created within and between proteins in vitro, in cells, and in vivo. Offering diverse properties previously unattainable, these covalent linkages have been harnessed to enhance protein properties, to modulate protein function, to probe ligand-receptor binding, to identify elusive protein interactions, and to develop covalent protein drugs. Selective introduction of covalent bonds into proteins is affording novel avenues for biological studies, synthetic biology, and biotherapeutics. Covalent bond ; Chemistry ; Chemical biology ; Amino acid ; Biochemistry ; Ligand (biochemistry) ; Combinatorial chemistry ; Biophysics ; Biology ; Receptor ; Organic chemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Stelios Chatzimichail | Faraz Rahimi | Aliyah Saifuddin | Andrew J. Surman | Simon D. Taylor‐Robinson | Ali Salehi‐Reyhani
Source: Communications Chemistry (article, 2021-02-16)
Field: Chemistry — Spectroscopy

Hand-portable HPLC with broadband spectral detection enables analysis of complex polycyclic aromatic hydrocarbon mixtures Polycyclic aromatic hydrocarbons (PAHs) are considered priority hazardous substances due to their carcinogenic activity and risk to public health. Strict regulations are in place limiting their release into the environment, but enforcement is hampered by a lack of adequate field-testing procedure, instead relying on sending samples to centralised analytical facilities. Reliably monitoring levels of PAHs in the field is a challenge, owing to the lack of field-deployable analytical methods able to separate, identify, and quantify the complex mixtures in which PAHs are typically observed. Here, we report the development of a hand-portable system based on high-performance liquid chromatography incorporating a spectrally wide absorption detector, capable of fingerprinting PAHs based on their characteristic spectral absorption profiles: identifying 100% of the 24 PAHs tested, including full coverage of the United States Environmental Protection Agency priority pollutant list. We report unsupervised methods to exploit these new capabilities for feature detection and identification, robust enough to detect and classify co-eluting and hidden peaks. Identification is fully independent of their characteristic retention times, mitigating matrix effects which can preclude reliable determination of these analytes in challenging samples. We anticipate the platform to enable more sophisticated analytical measurements, supporting real-time decision making in the field. Hazardous air pollutants ; Identification (biology) ; Environmental science ; Hazardous waste ; Computer science ; Field (mathematics) ; Pollutant ; Environmental chemistry ; Process engineering ; Biochemical engineering ; Chemistry ; Waste management ; Engineering Analytical Chemistry and Chromatography ; Toxic Organic Pollutants Impact ; Advanced Chemical Sensor Technologies