jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aditi Kalsh | Vijay Kumar Lamba
Source: Independent Journal of Management & Production (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Modelling and simulation of charge transport phenomena in graphene on SiO2 / Si substrate and graphene on complex oxide substrates Graphene and silicon are two prominent lithium-ion battery anode materials that have recently received a lot of attention. In this paper we have modelled and simulated the charge transport phenomena in Graphene on Si / SiO2 and SrTiO3 substrates. The Graphene monolayer's interface with the SrTiO3 (111) surface is analyzed using ab initio density-functional measurements. Both charge and heat flows are produced in solids, at the same time when an electrochemical potential is available, bringing about novel properties. The band structure and the electron dissolution process decide the Seebeck coefficient and electrical conductivity. It has been discovered that the interaction of Graphene with SiTiO3 accommodates electronic properties, Seebeck coefficient, and electronic conductivity. For the Graphene / SrTiO3 interface, the best values for the Seebeck coefficient were calculated. All the findings of this work suggest that the Graphene-SrTiO3 (111) and Graphene-Si structure could exhibit interesting quantum transport behavior. Graphene ; Materials science ; Seebeck coefficient ; Oxide ; Thermoelectric effect ; Bilayer graphene ; Graphene oxide paper ; Graphene nanoribbons ; Chemical physics ; Substrate (aquarium) ; Nanotechnology ; Condensed matter physics ; Thermal conductivity ; Chemistry ; Composite material ; Thermodynamics ; Physics Graphene research and applications ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Suvo Banik | Troy Loefller | Sukriti Manna | Srilok Srinivasan | Pierre Darancet | Henry Chan | Alexander Hexemer | Subramanian KRS Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2022-12-23)
Field: Materials Science — Materials Chemistry

A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. A challenge lies in the exorbitantly large structural and compositional space presented by the various elements and their combinations. Speed, accuracy, and scalability are three desirables for any inverse design tool to sample efficiently across such a vast space. While traditional global optimization approaches (e.g., evolutionary algorithm, random sampling based) have demonstrated the ability to predict new crystal structures that can be used as super-hard materials, semiconductors, and photovoltaic materials to name a few, it is highly desirable to develop approaches that converge faster to the solution, have better solution quality, and are scalable to high dimensionality. Reinforcement learning (RL) approaches are emerging as powerful design tools capable of addressing these issues but primarily operate in discrete action space. In this work, we introduce CASTING, which is an RL-based scalable framework for crystal structure, topology, and potentially microstructure prediction. CASTING employs an RL-based continuous search space decision tree (MCTS -Monte Carlo Tree Search) algorithm with three important modifications (i) a modified rewards scheme for improved search space exploration (ii) a 'windowing' or 'funneling' scheme for improved exploitation and (iii) adaptive sampling during playouts for efficient and scalable search. Using a set of representative examples ranging from metals such as Ag to covalent systems such as C and multicomponent systems (graphane, boron nitride, and complex correlated oxides), we demonstrate the accuracy, the speed of convergence, and the scalability of CASTING to discover new metastable crystal structures and phases that meet the target objective. Scalability ; Computer science ; Curse of dimensionality ; Tree (set theory) ; Sampling (signal processing) ; Mathematical optimization ; Algorithm ; Topology (electrical circuits) ; Theoretical computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Rubén Laplaza | Jan‐Grimo Sobez | Matthew D. Wodrich | Markus Reiher | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2022-03-29)
Field: Materials Science — Materials Chemistry

The (Not So) Simple Prediction of Enantioselectivity - A Pipeline for High-Fidelity Computations The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful mean to refine catalyst design strategies. Accurately establishing the selectivity of reactions facilitated by molecular catalysts, however, remains a challenging task for computational chemistry. The small free energy differences that lead to large variations in the enantiomeric ratio represent particularly tricky quantities to predict with sufficient accuracy to be helpful for prioritizing experi- ments. Further complicating this problem is the fact that standard approaches typically consider only one or a handful of conformers identified through human intuition as pars pro toto of the conformational space. Obviously, this assumption can potentially lead to dramatic failures should key energetic low-lying structures be missed. Here, we in- troduce a multi-level computational pipeline built upon the graph-based Molassembler library that combines conformer generation and tailored functionalization to facilitate high-throughput mechanistic investigations of chemical reactions. The capabilities of this approach are validated by examining a Rh(III) catalyzed asymmetric C-H activa- tion reaction and assessing the limitations associated with the underlying ligand design model. Specifically, the presence of remarkably flexible chiral Cp ligands, which induce the experimentally observed high level of selectivity, present a rich configurational landscape where multiple unexpected conformations contribute to the reported enan- tiomeric ratios (er). Using Molassembler, we show that considering about 20 transition state conformations per catalysts, which are generated with little human intervention and are not tied to “back of the envelope” models, accurately reproduces experimental er values with limited computational expense. Conformational isomerism ; Selectivity ; Computer science ; Chemistry ; Computation ; Intuition ; Pipeline (software) ; Computational chemistry ; Catalysis ; Combinatorial chemistry ; Molecule ; Algorithm ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Indranil Roy | Sandipp Krishnan Ravi | Subhrajit Roychowdhury | Bojun Feng | Sayan Ghosh | Christopher Reynolds | Rajnikant V. Umretiya | Raúl B. Rebak | Andrew Hoffman
Source: Research Square (preprint, 2022-09-29)
Field: Materials Science — Materials Chemistry

Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study Abstract Primary challenge of using FeCrAl in high temperature industrial setting is the formation of α’- precipitates that causes brittleness in the alloy, resulting in failure through fracture. The precipitation causes hardness change during thermal aging which is sensitive to both alloy composition and experimental condition (i.e., temperature and time of heat treatment). A Gaussian Process Regression (GPR) model is built on the hardness data collected at GE Research. Subsequently, for the first time, SHapley Additive exPlanations (SHAP) built upon the GPR is used as an Explainable Artificial Intelligence (XAI) tool to understand the effect of feature values in driving the hardness change. Several key insights are affirmed and obtained from a material science perspective. Precipitation ; Brittleness ; Alloy ; Feature (linguistics) ; Kriging ; Materials science ; Metallurgy ; Environmental science ; Computer science ; Machine learning ; Meteorology ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Machine Learning in Materials Science
Authors: Pranav Dave | Gaurav Dutta
Source: International Journal of Advanced Nano Computing and Analytics (article, 2022-02-10)
Field: Chemistry — Analytical Chemistry

Detection of Pesticides in Food by CDs Blue Luminescent Material with the Help of Fluorescence Quenching Study Environmental contamination has modified the approach to life of the folks in many ways all round the world. Food science suggests that the science of food which has food elements, their behaviour underneath completely different environmental conditions, method of harvest, milling, exposure to heat, cold, acid, alkalis etc. Today, the sphere of food science has progressed from basic physical, chemical and biological reactions that turn up throughout process to the fields of biotechnology, food engineering, packaging and its result on the patron. Luminescence and visual radiation phenomena have really fascinating ideas in technology. Light-weight materials have really various and distinctive properties, that are a magnet for various scientists to do and do the analysis throughout this field to implement in numerous sectors. Once the invention year in analysis of carbon dots in 2004, the study of carbon dots unreal, together with whole completely different fluorescent properties of carbon dots. It’s varied and valuable properties like water solubility, stable fluorescent property, exceptional photostability, high sensitivity with low toxicity, which can be apply in varied disciplines of research areas. This text explains the transient introduction of varied toxic materials and their effects. It explains the transient introduction and history of food science together with nutrition and digestions of food. It conjointly describes the various properties of food. The article is additionally mentioning the detection of various hazardous/poisonous materials by CDs with inherent literature survey. This text represents the transient introduction of pesticides and basic info of visible light extinction study. Through that, the venturesome material will be simply detected in varied food beverages. Nanotechnology ; Fluorescence ; Food packaging ; Quenching (fluorescence) ; Chemistry ; Materials science ; Food science ; Physics Dye analysis and toxicity
Authors: Yury Lysogorskiy | Anton Bochkarev | Matous Mrovec | Ralf Drautz
Source: arXiv (Cornell University) (preprint, 2022-12-16)
Field: Materials Science — Materials Chemistry

Active learning strategies for atomic cluster expansion models The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven interatomic potentials with a formally complete basis set. Since the development of any interatomic potential requires a careful selection of training data and thorough validation, an automation of the construction of the training dataset as well as an indication of a model's uncertainty are highly desirable. In this work, we compare the performance of two approaches for uncertainty indication of ACE models based on the D-optimality criterion and ensemble learning. While both approaches show comparable predictions, the extrapolation grade based on the D-optimality (MaxVol algorithm) is more computationally efficient. In addition, the extrapolation grade indicator enables an active exploration of new structures, opening the way to the automated discovery of rare-event configurations. We demonstrate that active learning is also applicable to explore local atomic environments from large-scale MD simulations. Extrapolation ; Computer science ; Set (abstract data type) ; Cluster (spacecraft) ; Basis (linear algebra) ; Automation ; Scale (ratio) ; Interatomic potential ; Class (philosophy) ; Selection (genetic algorithm) ; Machine learning ; Work (physics) ; Artificial intelligence ; Data mining ; Mathematics ; Physics ; Molecular dynamics ; Engineering ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Inorganic Chemistry and Materials
Authors: Tyler McGilvry-James | Bikash Timalsina | Marium Mostafiz Mou | Ridwan Sakidja
Source: MRS Advances (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Deep potential development of transition-metal-rich carbides Materials science ; Lattice constant ; Bulk modulus ; Thermodynamics ; Binary number ; Ground state ; Crystallite ; Thermal expansion ; Lattice (music) ; Condensed matter physics ; Atomic physics ; Physics ; Metallurgy ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Sameer Quazi | ShreeLaxmi Gavas | Bahareh Farasati Far | Javid Ahmad Malik | Maliha Rashid
Source: Preprints.org (preprint, 2022-04-18)
Field: Chemistry — Organic Chemistry

The Discovery of Novel Antimicrobial Agents by The Application of Isocyanide Based Multicomponent Reactions&lt;strong&gt; &lt;/strong&gt; Multicomponent reactions (MCR) have been used to synthesis a wide range of analogs from several classes of heterocyclic compounds, with multifaceted medicinal uses. The synthesis of highly functionalized molecules in a single pot is a unique property of MCR, allowing researchers to quickly assemble libraries of compounds of biological interest and uncover novel leads as possible therapeutic agents. Isocyanide-based multicomponent reactions have proven to be extremely effective at swiftly specifying members of compound libraries, particularly in discovery of drug . The understanding of structure-activity correlations that drive the development of new goods and technology, requires structural variety in these libraries. In current world, antibiotic resistance is a major ongoing problem which is developing a problematic scenario in public health. The implementation of isocyanide based multicomponent reactions uphold a significant potential in this regard. By utilizing such reactions, new antimicrobial compounds can be discovered and fight against such concerns. This study discusses recent developments in antimicrobial medication discovery using isocyanide-based multicomponent reactions (IMCRs). Furthermore, the article emphasizes the potential of IMCRs in the near future. Isocyanide ; Combinatorial chemistry ; Antimicrobial ; Drug discovery ; Chemistry ; Broad spectrum ; Nanotechnology ; Biochemical engineering ; Computational biology ; Biology ; Organic chemistry ; Materials science ; Engineering ; Biochemistry Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Laila M Break | Adil A. Gobouri | Wafa S Al-Hsrthi | Mohamed Hagar | Nadia S. Al‐Kaff | Musa A. Said
Source: TURKISH JOURNAL OF CHEMISTRY (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Design, synthesis, and greener pasture biological assessment of a novel nucleoside: 1-(α-D-ribofuranosyl)-6,7-difluoro-2-methyl-4-quinazolinone as an inhibitor of COVID-19 and Alzheimer's disease Synthesis of a new fluorinated nucleoside of 6,7-difluoro-2-methyl-4-quinazolinone was described. 2-Amino-4,5-difluorobenzoic acid <b>1</b> reacts with (CH<sub>3</sub>CO)<sub>2</sub>O followed by ammonia to form <i>(1H)-</i>6,7-difluoro-2-methyl-4-quinazolinone <b>3a</b>. Ribosylation of a silylated <b>4</b> with l-<i>O</i>-acety1-2,3-5-tri-<i>O</i>-benzoyl-α-D-ribofuranose <b>5</b> forms a protected nucleoside <b>6</b> then unprotected from <b>6</b> to give a free nucleoside <b>7</b>. Greener pasture biological docking of the cystine protease of COVID-19 [M<sup>pro</sup>, code 7BQY, PDB] by novel nucleoside and fluoroquinazoline compounds is presented. LIGPLOT (2D) representations calculated for the same ligands are shown. A superposition of remdesivir approved medicine, <b>N3</b> inhibitor, and our ligands docked together into the binding protein of 7BQY is also given for a fair comparison. The binding affinities of remdesivir, <b>N3</b> inhibitor, the nucleoside <b>7</b>, and fluoroquinazoline <b>3a</b>, <b>3b</b> compounds with <b>7</b>BQY calculated under the same conditions are -7.7, -7.4, -7.6, -6.1, and -6.1 kcal mol<sup>-1</sup>, respectively. The high values were due to the existence of many hydrophobic interactions and hydrogen bonds between the ligands and the active amino acid residues of the receptor, indicating a promising candidate as a COVID-19 inhibitor. Pro Tox -II server showed that compound <b>7</b> has a similar feature to the approved antiviral drug remdesivir for COVID-19. Additionally, a fascinating molecular modeling investigation showed that our nucleoside demonstrated good binding inhibition of AChE enzyme towards advancing an efficient medication against Alzheimer's disease. Finally, DFT has been conducted to illustrate the MD results in terms of the molecular descriptor-based structural activity relationship calculated from FMOs. Chemistry ; Nucleoside ; Quinazolinone ; Stereochemistry ; Docking (animal) ; Hydrogen bond ; Protein Data Bank (RCSB PDB) ; Organic chemistry ; Molecule Quinazolinone synthesis and applications ; Tuberculosis Research and Epidemiology ; Cancer therapeutics and mechanisms
Authors: Jack D. Evans
Source: ChemRxiv (preprint, 2022-05-23)
Field: Chemistry — Inorganic Chemistry

An approach for the pore-centred description of adsorption in hierarchical porous materials Analysis of pore size distributions based on crystalline representations of metal-organic frameworks (MOFs) featuring hierarchical pore systems, DUT-32, DUT-75, UMCM-1 and NU-1000, is presented here and leveraged to understand gas adsorption in these unique pore structures. Statistical analysis was used to effectively partition the pore space into distinct regions labelled by the pore size. This pore description was used to discover how adsorbates are located, with respect to the different pores, during simulations of argon adsorption at 87K. To further examine adsorption behaviour, a method of clustering the pore environments to locate the centre of the pore was developed. These pore centres were employed to observe the distribution of gas within the pore, describing adsorbate positions during filling events from the unique perspective of the pore centre. The methods presented here provide unsurpassed information about pore structure and adsorption properties that cannot be obtained with currently available methods and are now ready to apply to new materials to uncover new adsorption processes. Characterisation of pore space in soil ; Adsorption ; Porosity ; Porous medium ; Materials science ; Partition (number theory) ; Cluster analysis ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Physical chemistry ; Artificial intelligence ; Mathematics ; Engineering ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Catalytic Processes in Materials Science
Authors: Felix P. Kemeth | Sergio Alonso | Blas Echebarria | Ted Moldenhawer | Carsten Beta | Ioannis G. Kevrekidis
Source: arXiv (Cornell University) (preprint, 2022-07-08)
Field: Materials Science — Materials Chemistry

Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems We present a data-driven approach to learning surrogate models for amplitude equations, and illustrate its application to interfacial dynamics of phase field systems. In particular, we demonstrate learning effective partial differential equations describing the evolution of phase field interfaces from full phase field data. We illustrate this on a model phase field system, where analytical approximate equations for the dynamics of the phase field interface (a higher order eikonal equation and its approximation, the Kardar-Parisi-Zhang (KPZ) equation) are known. For this system, we discuss data-driven approaches for the identification of equations that accurately describe the front interface dynamics. When the analytical approximate models mentioned above become inaccurate, as we move beyond the region of validity of the underlying assumptions, the data-driven equations outperform them. In these regimes, going beyond black-box identification, we explore different approaches to learn data-driven corrections to the analytically approximate models, leading to effective gray box partial differential equations. Eikonal equation ; Amplitude ; Field (mathematics) ; Partial differential equation ; Differential equation ; Black box ; Phase (matter) ; Applied mathematics ; Computer science ; Mathematics ; Statistical physics ; Mathematical analysis ; Physics ; Artificial intelligence Solidification and crystal growth phenomena
Authors: Ivan Šoša
Source: Preprints.org (preprint, 2022-12-30)
Field: Chemistry — Spectroscopy

Mass Spectrometry-Based Techniques Towards OMICS 2.0 The literature was scoped to help place mass spectrometric (MS) platforms in the spotlight of the life sciences, as a tool of OMICS and OMICS 2.0 technologies. These methods were discussed, and their feasibility was highlighted in many reviews that have been periodically published, in addition to the most recently developed term OMICS 2.0. This author’s primary interest was the forensic application of MS, but since these interests got broadened, terms such as “biosensors” were also included. So, means of discovery of efficient biomarkers and clinical therapeutic targets were also reviewed. However, within the framework of this review, physical chemistry, bioinformatics, or specific software solutions were not specifically scoped. Aside from simply reviewing the research status and the future perspectives, this article tackles terms such as Society 5.0 and OMICS 2.0. Since the topic of generating permanent collections of digital content for retrospective analysis was reviewed, what requires loads of data – cloud storage is discussed in the context of MS. In this manner, the review has strengthened an in-depth understanding of the applications of MS. These large quantities of data are easier to operate with if generated by MS, rather than by any other commercially available assay. Omics ; Context (archaeology) ; Data science ; Computer science ; Cloud computing ; Bioinformatics ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications