jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emmanuel Dávila-Santiago | Cheng Shi | Gouri Mahadwar | Bridgette Medeghini | Logan Insinga | Rebecca Hutchinson | Stephen P. Good | Gerrad D. Jones
Source: Environmental Science & Technology (article, 2022-03-17)
Field: Chemistry — Analytical Chemistry

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features─colloquially termed a chemical fingerprint─that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (<i>n</i> = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (<i>n</i> = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment. Source tracking ; Workflow ; Sample (material) ; Fingerprint (computing) ; Environmental science ; Computer science ; Artificial intelligence ; Chemistry ; Database Analytical chemistry methods development ; Forensic Fingerprint Detection Methods ; Advanced Chemical Sensor Technologies
Authors: Xining Zang | Yuan Dong | Cuiying Jian | Nicola Ferralis | Jeffrey C. Grossman
Source: Matter (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Upgrading carbonaceous materials: Coal, tar, pitch, and beyond Coal tar ; Carbon fibers ; tar (computing) ; Process engineering ; Coal ; Computer science ; Asset (computer security) ; Environmental science ; Engineering ; Waste management ; Composite number Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Thermal and Kinetic Analysis
Authors: Haiding Zhu | Zhuangzhuang Liang | Sensen Xue | Xuefeng Ren | Xingyou Liang | Wei Xiong | Liguo Gao | Anmin Liu
Source: Ceramics International (article, 2022-06-07)
Field: Materials Science — Materials Chemistry

DFT practice in MXene-based materials for electrocatalysis and energy storage: From basics to applications Electrocatalyst ; Materials science ; Energy storage ; Nanotechnology ; Electrochemical energy storage ; Engineering physics ; Physical chemistry ; Supercapacitor ; Electrochemistry ; Electrode ; Physics ; Thermodynamics ; Chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Advanced Memory and Neural Computing
Authors: Tongyu Liu | Katherine R. Johnson | Santa Jansone‐Popova | De‐en Jiang
Source: JACS Au (article, 2022-06-15)
Field: Chemistry — Inorganic Chemistry

Advancing Rare-Earth Separation by Machine Learning Constituting the bulk of rare-earth elements, lanthanides need to be separated to fully realize their potential as critical materials in many important technologies. The discovery of new ligands for improving rare-earth separations by solvent extraction, the most practical rare-earth separation process, is still largely based on trial and error, a low-throughput and inefficient approach. A predictive model that allows high-throughput screening of ligands is needed to identify suitable ligands to achieve enhanced separation performance. Here, we show that deep neural networks, trained on the available experimental data, can be used to predict accurate distribution coefficients for solvent extraction of lanthanide ions, thereby opening the door to high-throughput screening of ligands for rare-earth separations. One innovative approach that we employed is a combined representation of ligands with both molecular physicochemical descriptors and atomic extended-connectivity fingerprints, which greatly boosts the accuracy of the trained model. More importantly, we synthesized four new ligands and found that the predicted distribution coefficients from our trained machine-learning model match well with the measured values. Therefore, our machine-learning approach paves the way for accelerating the discovery of new ligands for rare-earth separations. Rare earth ; Lanthanide ; Computer science ; Throughput ; Extraction (chemistry) ; Artificial intelligence ; Artificial neural network ; Process (computing) ; Machine learning ; Solvent extraction ; Representation (politics) ; Biological system ; Chemistry ; Ion ; Chromatography Radioactive element chemistry and processing ; Extraction and Separation Processes ; Machine Learning in Materials Science
Authors: Arpana Parihar | Nishant Kumar Choudhary | Palak Sharma | Raju Khan
Source: Environmental Pollution (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

MXene-based aptasensor for the detection of aflatoxin in food and agricultural products Aptamer ; Biosensor ; MXenes ; Nanotechnology ; Biochemical engineering ; Aflatoxin ; Computer science ; Biotechnology ; Materials science ; Engineering ; Biology MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Graphene and Nanomaterials Applications
Authors: Mortaza Saeidi‐Javash | Ke Wang | Minxiang Zeng | Tengfei Luo | Alexander W. Dowling | Yanliang Zhang
Source: Energy & Environmental Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices The first machine learning-assisted ultrafast flash sintering of flexible silver–selenide TE devices. Bayesian optimization of flash sintering variables led to a PF of 2205 μW m −1 K −2 and a zT of 1.1 at room temperature realized with a sintering time less than 1.0 second. Sintering ; Selenide ; Flash (photography) ; Materials science ; Ultrashort pulse ; Thermoelectric effect ; Optoelectronics ; Bayesian optimization ; Metallurgy ; Composite material ; Computer science ; Optics ; Artificial intelligence ; Thermodynamics ; Laser ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Paul L. A. Popelier
Source: Journal of Molecular Modeling (article, 2022-08-25)
Field: Chemistry — Physical and Theoretical Chemistry

Non-covalent interactions from a Quantum Chemical Topology perspective About half a century after its little-known beginnings, the quantum topological approach called QTAIM has grown into a widespread, but still not mainstream, methodology of interpretational quantum chemistry. Although often confused in textbooks with yet another population analysis, be it perhaps an elegant but somewhat esoteric one, QTAIM has been enriched with about a dozen other research areas sharing its main mathematical language, such as Interacting Quantum Atoms (IQA) or Electron Localisation Function (ELF), to form an overarching approach called Quantum Chemical Topology (QCT). Instead of reviewing the latter's role in understanding non-covalent interactions, we propose a number of ideas emerging from the full consequences of the space-filling nature of topological atoms, and discuss how they (will) impact on interatomic interactions, including non-covalent ones. The architecture of a force field called FFLUX, which is based on these ideas, is outlined. A new method called Relative Energy Gradient (REG) is put forward, which is able, by computation, to detect which fragments of a given molecular assembly govern the energetic behaviour of this whole assembly. This method can offer insight into the typical balance of competing atomic energies both in covalent and non-covalent case studies. A brief discussion on so-called bond critical points is given, highlighting concerns about their meaning, mainly in the arena of non-covalent interactions. Covalent bond ; Quantum ; Meaning (existential) ; Topology (electrical circuits) ; Population ; Chemistry ; Theoretical physics ; Physics ; Computer science ; Nanotechnology ; Epistemology ; Quantum mechanics ; Sociology ; Mathematics ; Materials science ; Philosophy Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Various Chemistry Research Topics
Authors: Weixin Ye | Wei Xu | Tianying Yan | Jing‐Kun Yan | Pan Gao | Chu Zhang
Source: Foods (review, 2022-12-27)
Field: Chemistry — Analytical Chemistry

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI. Hyperspectral imaging ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Environmental science ; Computer vision Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Roger M. Pallares | Felix M. Mottaghy | Volkmar Schulz | Fabian Kießling | Twan Lammers
Source: Journal of Nuclear Medicine (review, 2022-10-27)
Field: Materials Science — Biomaterials

Nanoparticle Diagnostics and Theranostics in the Clinic Nanoparticles possess unique features that may be useful for disease diagnosis and therapy. Preclinically, many different nanodiagnostics have been explored, but only a few have made it to the market. We here provide an overview of nanoparticle-based imaging agents currently used and evaluated in the clinic and discuss preclinical progress and translational avenues for the use of nanoparticles for diagnostic and theranostic applications. Nanoparticle ; Medicine ; Medical physics ; Nanotechnology ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced Biosensing Techniques and Applications
Authors: Ludwig Sinn | Łukasz Szyrwiel | Justus Grossmann | Kate Lau | Katharina Faisst | Di Qin | Florian Mutschler | Luke Khoury | Andrew Leduc | Markus Ralser | Fabian Coscia | Matthias Selbach | Nikolai Slavov | Nagarjuna Nagaraj | Martin Steger | Vadim Demichev
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-10-31)
Field: Chemistry — Spectroscopy

Slice-PASEF: Maximising Ion Utilisation in LC-MS Proteomics Quantitative mass spectrometry (MS)-based proteomics has become a streamlined technology with a wide range of usage. Many emerging applications, such as single-cell proteomics, spatial proteomics of tissue sections and the profiling of low-abundant posttranslational modifications, require the analysis of minimal sample amounts and are thus constrained by the sensitivity of the workflow. Here, we present Slice-PASEF, a mass spectrometry technology that leverages trapped ion mobility separation of ions to attain the theoretical maximum of tandem MS sensitivity. We implement Slice-PASEF using a new module in our DIA-NN software and show that Slice-PASEF uniquely enables precise quantitative proteomics of low sample amounts. We further demonstrate its utility towards a range of applications, including single cell proteomics and degrader drug screens via ubiquitinomics. Mass spectrometry ; Proteomics ; Chromatography ; Ion-mobility spectrometry ; Peptide ; Chemistry ; Quantitative proteomics ; Wafer ; Analytical Chemistry (journal) ; Materials science ; Nanotechnology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Monica J. Mendoza‐Castro | Erika De Oliveira-Jardim | Nelcari-Trinidad Ramírez-Marquez | Carlos‐Alexander Trujillo | Noemi Linares | Javier García‐Martínez
Source: Journal of the American Chemical Society (article, 2022-03-10)
Field: Chemistry — Inorganic Chemistry

Hierarchical Catalysts Prepared by Interzeolite Transformation Interzeolite transformation has been used to produce a novel family of hierarchical catalysts featuring excellent textural properties, strong acidity, and superior catalytic performance for the Friedel-Crafts alkylation of indole with benzhydrol, the Claisen-Schmidt condensation of benzaldehyde and hydroxyacetophenone, and the cracking of polystyrene. Intermediate solids of the FAU interzeolite transformation into BEA display both increased accessibility─due to the development of mesoporosity─and strong acidity─caused by the presence of ultrasmall crystals or zeolitic fragments in their structure. The use of surfactants allows for the development of the hierarchical catalysts with very narrow pore size distribution. The properties of interzeolite transformation intermediates (ITIs) can be fine-tuned simply by stopping the interconversion at different times. Chemistry ; Benzaldehyde ; Catalysis ; Alkylation ; Transformation (genetics) ; Polystyrene ; Condensation ; Microporous material ; Indole test ; Chemical engineering ; Fluid catalytic cracking ; Organic chemistry ; Combinatorial chemistry ; Polymer ; Thermodynamics Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Nicholaus Prasetya | Kang Li
Source: Separation and Purification Technology (article, 2022-08-27)
Field: Chemistry — Inorganic Chemistry

Synthesis of defective MOF-801 via an environmentally benign approach for diclofenac removal from water streams Diclofenac is one of the most popular non-steroidal anti-inflammatory drugs (NSAIDs) which has been widely used worldwide. Despite its popularity, its accumulation in the environment poses danger to the aquatic lives and its removal from the environment is paramount important. Although some conventional adsorbents such as activated carbon can be readily used to address this issue, they usually suffer from low diclofenac adsorption capacity (around 200 mg g−1), resulting in bulky adsorption systems. To overcome this problem, high performance materials such as metal organic frameworks (MOFs) can be employed. Here, we report that we synthesised defective MOF-801 for enhanced diclofenac adsorption via a simple and environmentally benign approach. Differing from a conventional MOF synthesis that usually requires the use of organic solvents at high temperature, the defective MOF-801 could be synthesised at room temperature and by changing the reaction medium from dimethylformamide to water. In addition, we have also successfully shown in this study that the defect concentration in MOF-801 can be rationally tuned by adjusting the modulator concentration (formic acid) in the synthesis solution. The resulting defective MOF-801 can then be used for environmental remediation, which we have shown here by employing them as an adsorbent for diclofenac removal from water streams. The enhanced adsorption of defective MOF-801 in comparison to its non-defective counterpart is due to the pore enlargement of the defective MOF-801 which provides a better pathway to access the adsorption sites. The maximum diclofenac adsorption capacity in a highly defective MOF-801 can reach as high as 680 mg g−1, which is almost 4 times higher than its non-defective counterpart. This study then opens possibilities to engineer the MOF particles for environmental remediation. Adsorption ; Diclofenac ; Metal-organic framework ; Environmental remediation ; Chemistry ; Formic acid ; Chemical engineering ; Organic chemistry ; Contamination Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Jiechun Liang | Tingting Wu | Ziwei Wang | Yunduo Yu | Linfeng Hu | Huamei Li | Xiaohong Zhang | Xi Zhu | Yu Zhao
Source: Energy Materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence Perovskites are promising materials applied in new energy devices, from solar cells to battery electrodes. Under traditional experimental conditions in laboratories, the performance improvement of new energy devices is slow and limited. Artificial intelligence (AI) has recently drawn much attention in material properties prediction and new functional materials exploration. With the advent of the AI era, the methods of studying perovskites have been upgraded, thereby benefiting the energy industry. In this review, we summarize the application of AI in perovskite discovery and synthesis and its positive influence on new energy research. First, we list the advantages of AI in perovskite research and the steps of AI application in perovskite discovery, including data availability, the selection of training algorithms, and the interpretation of results. Second, we introduce a new synthesis method with high efficiency in cloud labs and explain how this platform can assist perovskite discovery. We review the use of perovskites in energy applications and illustrate that the efficiency of energy production in these fields can be significantly boosted due to the use of AI in the development process. This review aims to provide the future application prospects of AI in perovskite research and new energy generation. Perovskite (structure) ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Data science ; Nanotechnology ; Efficient energy use ; Machine learning ; Materials science ; Engineering ; Electrical engineering ; Physics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Rishikesh Magar | Yuyang Wang | Amir Barati Farimani
Source: npj Computational Materials (article, 2022-11-10)
Field: Materials Science — Materials Chemistry

Crystal twins: self-supervised learning for crystalline material property prediction Abstract Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self-Supervised Learning (SSL) frameworks capable of training ML models on unlabeled data mitigate this problem and demonstrate superior performance in computer vision and natural language processing. Drawing inspiration from the developments in SSL, we introduce Crystal Twins (CT): a generic SSL method for crystalline materials property prediction that can leverage large unlabeled datasets. CT adapts a twin Graph Neural Network (GNN) and learns representations by forcing graph latent embeddings of augmented instances obtained from the same crystalline system to be similar. We implement Barlow Twins and SimSiam frameworks in CT. By sharing the pre-trained weights when fine-tuning the GNN for downstream tasks, we significantly improve the performance of GNN on 14 challenging material property prediction benchmarks. Leverage (statistics) ; Computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Graph ; Artificial neural network ; Forcing (mathematics) ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Baisheng Sa | Rong Hu | Zhao Zheng | Rui Xiong | Yinggan Zhang | Cuilian Wen | Jian Zhou | Zhimei Sun
Source: Chemistry of Materials (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

High-Throughput Computational Screening and Machine Learning Modeling of Janus 2D III–VI van der Waals Heterostructures for Solar Energy Applications Two-dimensional Janus III–VI monolayers and corresponding van der Waals (vdW) heterostructures present immense application potential in the solar energy conversion areas. In this work, we present material screening and machine learning modeling to accelerate the discovery of promising photocatalytic and photovoltaic candidates in Janus III–VI vdW heterostructures. A comprehensive database with a total of 19926 heterostructures has been proposed according to the high-throughput first-principles calculations. It is highlighted that we develop an accurate machine learning model using only atoms and bonds as descriptors based on the crystal graph convolutional neural network framework. Besides, 1035 Janus III–VI vdW heterostructures have been screened out according to the essential criteria of stability. Moreover, we find 66 and 71 potential candidates for photocatalysis and solar cells, respectively, from further application-driven screening. Interestingly, the screened type-II SeInAlS/SeGaAlTe heterostructure with a band gap of 1.18 eV is highlighted as an internal electric field-driven asymmetrical photocatalyst. On the other hand, the type-II Al2STe/Al2SSe heterostructure solar cell presents power conversion efficiencies higher than 21% both from the microscopic and mesoscopic point of views. We believe that our study will provide a feasible strategy for the design of III–VI monolayers for solar energy applications. Heterojunction ; Janus ; van der Waals force ; Materials science ; Band gap ; Photovoltaic system ; Nanotechnology ; Solar cell ; Photocatalysis ; Monolayer ; Computer science ; Optoelectronics ; Physics ; Chemistry ; Engineering ; Electrical engineering ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films