jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shengbo Wang | David García‐Seisdedos | Ananth Prakash | Deepti J Kundu | Andrew Collins | Nancy George | Silvie Fexová | Pablo Moreno | Irene Papatheodorou | Andrew R. Jones | Juan Antonio Vizcaíno
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-21)
Field: Chemistry — Spectroscopy

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues Abstract The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination. Author summary We have reanalysed 23 baseline mass spectrometry-based public proteomics datasets stored in the PRIDE database. Overall, the aggregated dataset contained 211 samples, coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. We analysed the distribution of protein expression across organs in both species. We also studied how protein abundances compared across different datasets and organs for both species. Then we performed gene ontology and pathway enrichment analyses to identify enriched biological processes and pathways across organs. We also carried out a comparative analysis of baseline protein expression across mouse, rat and human tissues, observing a high level of expression correlation among orthologs in all three species, in brain, kidney, heart and liver samples. To disseminate these findings, we have integrated the protein expression results into the resource Expression Atlas. Proteomics ; Human Protein Atlas ; Protein expression ; Biology ; Computational biology ; Kidney ; Molecular biology ; Bioinformatics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Endoplasmic Reticulum Stress and Disease
Authors: Yifan Wang | Tai-Ying Chen | Dionisios G. Vlachos
Source: ChemRxiv (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. Bayesian optimization ; Interfacing ; Computer science ; Engineering optimization ; Python (programming language) ; Multidisciplinary design optimization ; Bayesian probability ; Visualization ; Optimization problem ; Artificial intelligence ; Machine learning ; Programming language ; Algorithm Machine Learning in Materials Science ; Machine Learning and Algorithms ; Process Optimization and Integration
Authors: Robbert van Putten
Source: Data Archiving and Networked Services (DANS) (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Catalysis, chemistry, and automation: Addressing complexity to explore practical limits of homogeneous Mn catalysis Catalysis is a critical enabling technology that directly contributes to higher standards of living. This is a remarkable feat for a technology that most consumers have no direct contact with and perhaps only vaguely know from their car’s catalytic converter. Even many technical users regard catalysts as ideal substances that promote a target transformation without being consumed in the reaction. Reality, however, is much more complex because catalysts can also produce undesirable side-products or stop working before the target reaction is complete. This dissertation explores such complexity.&lt;br/&gt;The aim of this work was to study real catalytic systems under relevant reaction conditions. This challenge was approached from two different directions, and the dissertation is therefore divided into two parts. Part I describes our study of non-noble Mn complexes as catalysts for a variety of reduction chemistries. Particular attention was given to the study of deactivation phenomena to understand why some catalysts stop operating early in their lifetime. This work led to catalytic methods that enable improved operation at significantly lower catalyst loading. The second part of this dissertation focuses on the development and application of automation and data-rich experimentation methods for catalysis R&amp;amp;D. An automated reaction analysis platform was developed that simultaneously produces kinetic data and several representative data streams of operando spectroscopy. These tools were used to study and optimise the performance of a variety of chemistries, and are expected to provide the required dense data for contemporary data-driven methods. Variety (cybernetics) ; Catalysis ; Automation ; Chemistry ; Work (physics) ; Homogeneous ; Process engineering ; Computer science ; Nanotechnology ; Biochemical engineering ; Engineering ; Materials science ; Artificial intelligence ; Organic chemistry ; Mechanical engineering ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Zhi‐Yuan Gu | Shi‐Shu Yang | Ming Xu | Xiang‐Da Zhang | Chunhai Fan | Jinghong Li
Source: Pure and Applied Chemistry (article, 2021-07-27)
Field: Materials Science — Materials Chemistry

Research frontiers of chemical detection and measurements Abstract Over the past decade, the global research agenda focused on finding paths to the future development of chemistry. Chemical detection and measurement is an important and fundamental discipline of chemistry and it keeps playing a significant role in chemical research in the next 10–15 years. The goal of chemical detection is to overcome the state-of-art temporospatial resolution limit. Meanwhile, the application of modern chemical technology to serve human health is also highly concerned. We have summarized 10 aspects related to the field, including complex samples analysis, biomacromolecule structure and function, single-entity analysis, large-scale scientific instrument, rapid chemical and electron transfer processes, big data and artificial intelligence, bioimaging, in vivo real-time detection, biomolecule recognition and disease therapy, and public health screening. In this review, we outlined the history and the frontiers of chemical detection and measurements in enhancing the development of chemical sciences. Moreover, we also focused on the major challenges that should be solved in the future in which researchers should develop new techniques and analytical methods to drastically accelerate chemical sciences. Chemistry ; Nanotechnology ; Data science ; Human health ; Biochemical engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Jamshid Sourati | James A. Evans
Source: arXiv (Cornell University) (preprint, 2021-04-12)
Field: Materials Science — Materials Chemistry

Accelerating science with human versus alien artificial intelligences Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance, such as the discovery of novel materials with desired properties and the targeted invention of new therapies and vaccines. These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention. As a result, AI hypotheses are designed to substitute for human experts, failing to complement them for punctuated collective advance. Here we show that incorporating the distribution of human expertise into self-supervised models by training on inferences cognitively available to experts dramatically improves AI prediction of future human discoveries and inventions. Including expert-awareness into models that propose (a) valuable energy-relevant materials increases the precision of materials predictions by ~100%, (b) repurposing thousands of drugs to treat new diseases increases precision by 43%, and (c) COVID-19 vaccine candidates examined in clinical trials by 260%. These models succeed by predicting human predictions and the scientists who will make them. By tuning AI to avoid the crowd, however, it generates scientifically promising "alien" hypotheses unlikely to be imagined or pursued without intervention, not only accelerating but punctuating scientific advance. By identifying and correcting for collective human bias, these models also suggest opportunities to improve human prediction by reformulating science education for discovery. Repurposing ; Data science ; Computer science ; Artificial intelligence ; Human intelligence ; Machine learning ; Engineering Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Misinformation and Its Impacts
Authors: Peng Song | Zhufeng Hou | Pedro Baptista de Castro | Kousuke Nakano | Kenta Hongo | Yoshihiko Takano | Ryo Maezono
Source: arXiv (Cornell University) (preprint, 2021-02-27)
Field: Materials Science — Materials Chemistry

High-$T_c$ ternary metal hydrides, YKH$_{12}$ and LaKH$_{12}$, discovered by machine learning The search for hydride compounds that exhibit high $T_c$ superconductivity has been extensively studied. Within the range of binary hydride compounds, the studies have been developed well including data-driven searches as a topic of interest. Toward the search for the ternary systems, the number of possible combinations grows rapidly, and hence the power of data-driven search gets more prominent. In this study, we constructed various regression models to predict $T_c$ for ternary hydride compounds and found the extreme gradient boosting (XGBoost) regression giving the best performance. The best performed regression predicts new promising candidates realizing higher $T_c$, for which we further identified their possible crystal structures. Confirming their lattice and thermodynamical stabilities, we finally predicted new ternary hydride superconductors, YKH$_{12}$ [$C2/m$ (No.12), $T_c$=143.2 K at 240 GPa] and LaKH$_{12}$ [$R\bar{3}m$ (No.166), $T_c$=99.2 K at 140 GPa] from first principles. Ternary operation ; Hydride ; Crystallography ; Lattice (music) ; Crystal structure prediction ; Crystal structure ; Chemistry ; Materials science ; Metal ; Physics ; Computer science ; Metallurgy Machine Learning in Materials Science ; Superconducting Materials and Applications ; Rare-earth and actinide compounds
Authors: Mohamad Izmir Farhan Mohamad Radzi | Mohamad Radzi Ahmad | N. H. Nik Ali | Azrul Mohd Ariffin
Source:  (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Development of Partial Discharge Detection Device for PD Detection on Medium Voltage XLPE Cable Partial Discharge (PD) is a natural phenomenon that occurs inevitably in high voltage equipment mainly due to the degradation of insulation materials. PD emits high frequency signals above 40 kHz which can be easily detected by Acoustic Emission (AE) method. This paper presents the processes of designing a PD detection device using ultrasonic sensor. The finished design is known as Partial Discharge Detection (PDD) device and its function is to detect PD on a medium voltage cross-linked polyethylene (XLPE) cable. Outcomes of the design processes show the complete design of the PDD device, the fabricated PDD device, and the functionality of the PDD device. The end results show that the PDD device is capable to detect PD much like a HFCT sensor does. The prototype device will be very beneficial in the upcoming future as the physical dimension of the device itself is relatively small, portable and cost efficient to manufacture when compared to the commercial PD sensing devices. Further advancement of the PD device, such as to incorporate Artificial Intelligence (AI), can be an added feature to the device moving forward. In addition, the prototype can also be converted into Flexible Printed Circuit Board (FPCB) form factor for ease of implementation and robustness. Such finding will be of utmost help to the power industries in monitoring PD activities on high voltage equipments using an affordable and efficient device in an effort to reduce the repair and maintenance costs. Partial discharge ; Robustness (evolution) ; Voltage ; High voltage ; Computer science ; Materials science ; Acoustic emission ; Electrical engineering ; Electronic engineering ; Engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Underwater Vehicles and Communication Systems
Authors: Kubra Ulucan‐Altuntas | Fatih İlhan | Caner Kasar | Mustafa Gönüllü
Source: Journal of Water Chemistry and Technology (article, 2021-05-01)
Field: Chemistry — Analytical Chemistry

Implementation of Fuzzy Logic Model on Textile Wastewater Treatment by Electrocoagulation Proсess A high concentration of colour, chemical oxidation demand (COD) and low biodegradability make textile wastewater a major source of pollution. While physicochemical processes, adsorption and chemical coagulation were the most commonly applied treatment methods for textile wastewater, the electrocoagulation process as compared to these conventional methods, produces less sludge with high colour removal efficiency. In this study, textile wastewater obtained from industry was treated by electrocoagulation process and the results were evaluated with fuzzy logic, an artificial intelligence-based modelling, which is used for advanced control and is employed in many environmental engineering applications, from wastewater treatment to air pollution estimation. Current density, initial pH and electrolysis time were selected as variables and utilized to predict COD, total organic carbon (TOC) and colour removal efficiencies. The experimental studies were applied to the developed fuzzy logic model to demonstrate the model accuracy. Using fuzzy logic model, surface maps were prepared for COD, TOC and colour removal. According to the obtained results, while Colour can be removed by 90%, COD and TOC can be removed by approximately 55 and 80%, respectively. While low pH was more efficient in COD and TOC removal, high pH values were shown to be efficient in colour removal. Time was the most efficient parameter for the electrocoagulation treatment and 15 minutes of reaction time was obtained as optimum value. Moreover, the results were evaluated with the multiple regression model. The obtained equations were maximized to determine the optimum conditions. These optimum conditions are 60 mA/cm2 of current density, pH 5 and 20 min of reaction time. When these parameters were examined, colour, TOC and COD removal efficiencies were attained as 89, 86 and 61%, respectively. Electrocoagulation ; Wastewater ; Pulp and paper industry ; Fuzzy logic ; Textile ; Sewage treatment ; Chemical oxygen demand ; Adsorption ; Environmental science ; Environmental engineering ; Chemistry ; Materials science ; Computer science ; Engineering ; Artificial intelligence ; Composite material Dye analysis and toxicity
Authors: Leroy Cronin
Source: Bulletin of Japan Society of Coordination Chemistry (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Exploring the Hidden Constraints that Control the Self-Assembly of Nanomolecular Inorganic Clusters The mechanism that controls the self-assembly of complex polyoxometalate inorganic clusters in solution is a challenge because the clusters can contain anywhere from 6 to 368 metal ions in a single molecule and are commonly assembled under ʻone-potʼ reaction conditions. Furthermore, harnessing the ʻself-assemblyʼ processes allows synthesis on a length-scale and with a degree of complexity which is not easily accessed by more traditional ʻstep-wiseʼ synthesis routes. This class of molecules are important as they can be thought of as ʻmolecular metal oxidesʼ and rival the size of proteins yet are based upon simple MOx units (where M is Mo, W, V and sometimes Nb and x can be 4, 5, 6 or 7). Here we review the structures of the molybdenum-based clusters and then show how it is possible to not only control the architectures but explore the mechanism of formation invoking mechanisms that are normally found in biological systems. We describe how a simple inorganic salt can spontaneously form ʻinformation-richʼ, autocatalytic sets of replicating inorganic molecules that work via molecular recognition based on the [PMo12O40]3–, {PMo12} Keggin ion, and [Mo36O112(H2O)16]8–, {Mo36} cluster. These small clusters are involved in an autocatalytic network, where the assembly of gigantic molybdenum blue wheels [Mo154O462H14(H2O)70]14– {Mo154}, [Mo132O372(H2O)72(CH3CO2)30]42–, {Mo132} ball containing 154 and 132 molybdenum clusters are templated by the smaller clusters which are themselves able to catalyze their own formation. Finally, We introduce assembly theory and show how assembly theory can be used to describe the constraints that need to be present to facilitate the assembly of the large clusters and explain how information theoretic arguments might be useful for designing highly functional and unsymmetrical inorganic molecules. Polyoxometalate ; Cluster (spacecraft) ; Self-assembly ; Molybdenum ; Chemistry ; Molecule ; Autocatalysis ; Nanotechnology ; Chemical physics ; Materials science ; Catalysis ; Inorganic chemistry ; Computer science ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Daisuke Ohori | Min-Hui Chuang | Asahi Sato | Sou Takeuchi | Masayuki Murata | Atsushi Yamamoto | Ming-Yi Lee | Kazuhiko Endo | Yiming Li | Jenn‐Hwan Tarng | Yao‐Jen Lee | Seiji Samukawa
Source: IEEE Open Journal of Nanotechnology (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Management of Phonon Transport in Lateral Direction for Gap-Controlled Si Nanopillar/SiGe Interlayer Composite Materials The phonon transport in the lateral direction for gap-controlled Si nanopillar/SiGe interlayer composite materials was investigated to eliminate heat generation in the channel area for advanced MOS transistors. The gap-controlled Si NP/SiGe composite layer showed 1/100 times lower thermal conductivity than Si bulk. Then, the phonon transport behavior in lateral direction could be predicted by the combination between 3-omega measurement method for thermal conductivity and Landauer approach for phonon transport in Si NP/Si0.7Ge0.3 interlayer composite structure. We found that the NP structure could regulate the phonon transport in the lateral direction by changing the NP gaps. As such, this structure achieves the first step toward phonon transport management in the same electron transportation direction of planar-type MOSFETs and represents a promising solution to heat generation for advanced CMOS devices. Nanopillar ; Materials science ; Phonon ; Thermal conductivity ; Composite number ; Condensed matter physics ; Transistor ; Optoelectronics ; Nanotechnology ; Composite material ; Nanostructure ; Electrical engineering Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Silicon Carbide Semiconductor Technologies
Authors: Kendra K. Noneman | Christopher L. Muhich | Kevin D. Ausman | Mike Henry | Eric Jankowski
Source: The Journal of Computational Science Education (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Molecular Simulations for Understanding the Stabilization of Fullerenes in Water Making materials out of buckminsterfullerene is challenging, because it requires first dispersing the molecules in a solvent, and then getting the molecules to assemble in the desired arrangements. In this computational work, we focus on the dispersion challenge: How can we conveniently solubilize buckminsterfullerene? Water is a desirable solvent because of its ubiquity and biocompatibility, but its polarity makes the dispersion of nonpolar fullerenes challenging. We perform molecular dynamics simulations of fullerenes in the presence of fullerene oxides in implicit water to elucidate the role of interactions (van der Waals and Coulombic) on the self-assembly and structure of these aqueous mixtures. Seven coarse-grained fullerene models are characterized over a range of temperatures and interaction strengths using HOOMD-Blue on high performance computing clusters. We find that dispersions of fullerenes stabilized by fullerene oxides are observable in models where the net attraction among fullerenes is about 1.5 times larger than the attractions between oxide molecules. We demonstrate that simplified models are sufficient for qualitatively modeling micellization of these fullerenes and provide an efficient starting point for investigating how structural details and phase behavior depend upon the inclusion of more detailed physics. Fullerene ; Materials science ; Nanotechnology ; Chemical physics ; Chemistry ; Environmental science ; Organic chemistry Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Graphene research and applications