jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Denis Beslic | Georg Tscheuschner | Bernhard Y. Renard | Michael G. Weller | Thilo Muth
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-22)
Field: Chemistry — Spectroscopy

Current state, existing challenges, and promising progress for <i>de novo</i> sequencing and assembly of monoclonal antibodies Abstract Monoclonal antibodies (mAbs) are biotechnologically produced proteins with various applications in research, therapeutics, and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a useful method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. Deep learning-based approaches have been developed and applied more frequently to increase the accuracy of de novo sequencing. In this study, we evaluated five recently developed de novo sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, and PointNovo) in their ability to identify and assemble antibody sequences. The deep learning-based tools PointNovo and SMSNet showed an increased peptide recall across different enzymes and datasets compared to spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra, and peptides of various lengths. We achieved a sequence coverage of 93.15% to 99.07% on the light chains of three different antibody datasets using the de Bruijn assembler ALPS and the predictions from PointNovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies, and hybrid approaches such as homology search to achieve high accuracy on long protein sequences. Computational biology ; Sequence assembly ; Monoclonal antibody ; Sequence (biology) ; Consensus sequence ; Peptide sequence ; Biology ; Computer science ; Antibody ; Biochemistry ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Mass Spectrometry Techniques and Applications
Authors: Shivesh Pathak | I. Ema | Alex J. Lee | William P. Bricker | R. López | Susi Lehtola | Joshua A. Rackers
Source: arXiv (Cornell University) (preprint, 2022-07-07)
Field: Materials Science — Materials Chemistry

Accurate Hellmann-Feynman forces from density functional calculations with augmented Gaussian basis sets The Hellmann-Feynman (HF) theorem provides a way to compute forces directly from the electron density, enabling efficient force calculations for large systems through machine learning (ML) models for the electron density. The main issue holding back the general acceptance of the HF approach for atom-centered basis sets is the well-known Pulay force which, if naively discarded, typically constitutes an error upwards of 10 eV/Ang in forces. In this work, we demonstrate that if a suitably augmented Gaussian basis set is used for density functional calculations, the Pulay force can be suppressed and HF forces can be computed as accurately as analytical forces with state-of-the-art basis sets, allowing geometry optimization and molecular dynamics to be reliably performed with HF forces. Our results pave a clear path forwards for the accurate and efficient simulation of large systems using ML densities and the HF theorem. Basis (linear algebra) ; Gaussian ; Statistical physics ; Feynman diagram ; Basis set ; Physics ; Density functional theory ; Set (abstract data type) ; Work (physics) ; Atom (system on chip) ; Path integral formulation ; Classical mechanics ; Quantum mechanics ; Mathematics ; Computer science ; Quantum ; Geometry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Xiaoting Zhong | Nestor J. Zaluzec | Yu Lin | Jiadong Gong
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy Journal Article Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy Get access Xiaoting Zhong, Xiaoting Zhong Materials Science Department, Lawrence Livermore National Laboratory, Livermore, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Nestor J Zaluzec, Nestor J Zaluzec Photon Science Directorate, Argonne National Laboratory, Lemont, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Yu Lin, Yu Lin QuesTek Innovations LLC, Evanston, IL, USA Corresponding author: ylin@questek.com Search for other works by this author on: Oxford Academic Google Scholar Jiadong Gong Jiadong Gong QuesTek Innovations LLC, Evanston, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3138–3140, https://doi.org/10.1017/S1431927622011679 Published: 01 August 2022 National laboratory ; Library science ; Computer science ; Engineering physics ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Beibei Wang | Shane Jackson | Aiichiro Nakano | Ken‐ichi Nomura | Priya Vashishta | Rajiv K. Kalia | Mark J. Stevens
Source: Journal of Chemical Information and Modeling (review, 2022-07-05)
Field: Materials Science — Materials Chemistry

Neural Network for Principle of Least Action The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager-Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the "ground-truth" molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ<sub>38</sub> from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications. Principle of least action ; Action (physics) ; Transformation (genetics) ; Artificial neural network ; Trajectory ; Statistical physics ; Phase space ; Molecular dynamics ; Classical mechanics ; Potential energy ; Computer science ; Observable ; Physics ; Quantum mechanics ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Cheng Wu | Yen Chun Chuan Sun | Rishabh Kushwaha | Piyush Bajpai | Shao Chang Cheng
Source:  (article, 2022-05-02)
Field: Materials Science — Surfaces, Coatings and Films

3D NAND vertical channel defect inspection and classification solution on a DL-based e-beam system : DI : Defect Inspection and Reduction With data storage capacity increasing, more memory cell stacks for three-dimensional NAND (3D NAND) devices are developed. When stacking more thin-film layers, the capability to form uniform high aspect ratio (HAR) structures becomes a key 3D NAND process step. Therefore, in 3D NAND manufacturing, etch process control is especially important. Etch processes generate HAR structures and defects are usually buried in the deep trenches or holes, which become inspection challenges. Defect control is important for semiconductor manufacturing to ensure device quality. In this study, a high landing energy (HiLE) e-beam defect inspection system with a wide landing energy operation range is utilized to compare scanning electron microscopy (SEM) images of different landing energy to get the best signal for defects of interest (DOI) that are buried in the deep vertical channel (VC) holes. A landing energy of 30KeV was determined to provide best DOI imaging. In addition, to reduce the burden of manual defect classification (MDC) and improve traditional algorithm limitations, a deep learning (DL)-based algorithm methodology is implemented that successfully demonstrates detection of DOI at ~6 &#x03BC;m depth within the VC holes of a 96-layer 3D NAND device, while also achieving auto defect classification (ADC) with &#x003E;90% purity by each VC row. NAND gate ; Stacking ; Materials science ; Process (computing) ; Computer science ; Channel (broadcasting) ; Energy (signal processing) ; Semiconductor device fabrication ; Artificial intelligence ; Optoelectronics ; Computer hardware ; Electronic engineering ; Engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Ge Zhang | Hongyi Xiao | Entao Yang | Robert Ivancic | Ridout, Sean A. | Robert A. Riggleman | D. J. Durian | Andrea J. Liu
Source: arXiv (Cornell University) (preprint, 2022-05-18)
Field: Materials Science — Materials Chemistry

Structuro-elasto-plasticity (StEP) model for plasticity in disordered solids Elastoplastic lattice models for the response of solids to deformation typically incorporate structure only implicitly via a local yield strain that is assigned to each site. However, the local yield strain can change in response to a nearby or even distant plastic event in the system. This interplay is key to understanding phenomena such as avalanches in which one plastic event can trigger another, leading to a cascade of events, but typically is neglected in elastoplastic models. To include the interplay one could calculate the local yield strain for a given particulate system and follow its evolution, but this is expensive and requires knowledge of particle interactions, which is often hard to extract from experiments. Instead, we introduce a structural quantity, "softness," obtained using machine learning to correlate with imminent plastic rearrangements. We show that softness also correlates with local yield strain. We incorporate softness to construct a "structuro-elasto-plasticity" model that reproduces particle simulation results quantitatively for several observable quantities, confirming that we capture the influence of the interplay of local structure, plasticity, and elasticity on material response. Plasticity ; Observable ; Statistical physics ; Cascade ; Biological system ; Yield (engineering) ; Mechanics ; Materials science ; Lattice (music) ; Computer science ; Physics ; Engineering ; Composite material Microstructure and mechanical properties ; Composite Material Mechanics ; Rock Mechanics and Modeling
Authors: Ravinder Bhattoo | Suresh Bishnoi | Mohd Zaki | N. M. Anoop Krishnan
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Understanding the Compositional Control on Electrical, Mechanical, Optical, and Physical Properties of Inorganic Glasses with Interpretable Machine Learning Control (management) ; Materials science ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Glass properties and applications ; Pigment Synthesis and Properties
Authors: Yu Han | Junjie Wang | Chi Ding | Hao Gao | Shuning Pan | Qiuhan Jia | Jian Sun
Source: arXiv (Cornell University) (preprint, 2022-12-22)
Field: Materials Science — Materials Chemistry

Prediction of surface reconstructions using MAGUS In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher (MAGUS). In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common. To validate this newly developed module, we tested it with studies on the surface reconstructions of Si (100), Si (111) and 4H-SiC(1-102)-c(2x2), respectively. We successfully gave the known ground states as well as a new SiC surface model in an extremely Si-rich environment. Surface (topology) ; Lattice (music) ; Surface reconstruction ; Cluster (spacecraft) ; Population ; Materials science ; Statistical physics ; Graph ; Potential energy surface ; Computer science ; Algorithm ; Computational science ; Chemical physics ; Geometry ; Theoretical computer science ; Physics ; Mathematics ; Molecule ; Quantum mechanics Graphene research and applications ; Silicon Carbide Semiconductor Technologies ; Machine Learning in Materials Science
Authors: Nicholas Stiles Wilkins | Kasturi Nagesh Pai | Arvind Rajendran
Source: ChemRxiv (preprint, 2022-03-10)
Field: Chemistry — Inorganic Chemistry

Optimization of pressure-vacuum swing adsorption processes for nitrogen rejection from natural gas streams using a nitrogen selective metal organic framework A vanadium(II/III) metal-organic organic framework (MOF), V2Cl2.8(btdd), that is selective to CH4 over N2 was recently discovered. Process optimizations were performed to determine the performance of this MOF to reach the pipeline transport purity of 96 mol% CH4. Two cycles were considered: the basic 3-step cycle and the Skarstrom cycle. First, the 3-step cycle was considered with a wide range of operating conditions. Three inlet compositions (55/45, 80/20 and 92/8 mol% CH4/N}), three process temperatures (30, 40 and 50C) and a range of adsorption pressures (100 to 500 kPa) were considered. A detailed process model in tandem with machine learning-aided optimization was employed to determine the optimal set of operating conditions. The 3-step cycle was unable to meet the 96 mol% CH4 purity requirement in most cases studied. However, the Skarstrom cycle was able to meet the 96 mol% CH4 purity requirement in all cases studied. The maximum recovery, at a purity of 96 mol%, was at 84.2% for the Skarstrom cycle with a methane feed composition of 80 mol% at 50C and an adsorption pressure of 100 kPa. For the Skarstrom cycle, at a feed temperature of 50C, an adsorption pressure of 100 kPa and a feed methane composition of 92 mol%, the productivity could be as high as 21.18 tonnes per day CH4 m-3 at a recovery of 73%. The achievable recovery-productivity values were comparable to a carbon molecular sieve process reported in the literature at similar operating conditions. Methane ; Adsorption ; Pressure swing adsorption ; Nitrogen ; Chemistry ; Natural gas ; Carbon fibers ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Shuna Yang | Weichen Zhu | Linbin Zhu | Xue Ma | Tongan Yan | Nengcui Gu | Youshi Lan | Yi Huang | Mingyuan Yuan | Minman Tong
Source:  (preprint, 2022-08-09)
Field: Materials Science — Materials Chemistry

Machine learning guided design of covalent organic frameworks for CO2 capture in wet flue gas Discovery of remarkable porous materials for CO2 capture from wet flue gas is of great significance to reduce the CO2 emissions, but elucidating the most critical structure features for boosting CO2 capture capabilities remains a great challenge. Here, machine learning assisted computational screening on 516 experimental covalent organic frameworks (COFs) identify the superior secondary building units (SBUs) for wet flue gas separation, which are tetraphenylporphyrin unit in sql-type COFs and functional groups. Accordingly, 1233 COFs are assembled using the superior SBUs. Combined with DFT calculations, the “electron-donating induced vdW interaction” effect is discovered to design better-performing COFs with superior CO2 uptake, which can achieve 4.4 mmol·g-1 with CO2/N2 selectivity of 104.8; while the “electron-withdrawing induced vdW+electrostatic coupling interaction” effect is proposed to design better-performing COFs with superior CO2/N2 selectivity, which can arrive 277.6 with CO2 uptake of 2.2 mmol·g-1, in this case, H2O contributes to improving the CO2/N2 selectivity. Flue gas ; SBus ; Covalent bond ; Selectivity ; Covalent organic framework ; Chemistry ; Computer science ; Chemical engineering ; Nanotechnology ; Materials science ; Engineering ; Adsorption ; Metal-organic framework ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; CO2 Reduction Techniques and Catalysts
Authors: André R. Petterson | Vanderlei Aparecido de Lima | Fauze Jacó Anaissi
Source: Química Nova (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

CLOROFILA EXTRAÍDA DE RESÍDUO INDUSTRIAL DA ERVA-MATE (Ilex paraguaiensis) UMA POSSIBILIDADE DE ECONOMIA CIRCULAR CHLOROPHYLL EXTRACTED FROM INDUSTRIAL WASTE FROM MATE-HERB (Ilex paraguaiensis) A POSSIBILITY OF CIRCULAR ECONOMY. Chlorophyll is the most abundant green pigment on the planet, it is unstable and decomposes naturally. Mate-herb is a traditional native plant in the southern region of South America, and its tea is part of the local culture and extractive agriculture. The mate-herb industry generates as a by-product a resinous material rich in chlorophyll whose use is proposed to be a natural pigment. The chlorophyll extraction method uses ethanol as a solvent; its structural modification leads to the obtainment of derivatives like pheophytin, Co-pheophytin, Ni-pheophytin, and Zn-pheophytin. The samples were analyzed by electronic (VISIBLE) and vibrational (FTIR) spectroscopy, and energy-dispersive X-ray fluorescence spectrometry (EDXRF). Color analysis (CIE L*a*b*) was performed by decomposition in grayscale and the classification of the samples by the supervised machine learning (ML) algorithm Random Forest (RF), showing adequate accuracy for this type of matrix. The pigments were dispersed in synthetic enamel paint to study color stability when exposed to lighting. It was verified that Co-pheophytin and Ni-pheophytin are the most stable. The chlorophyll extracted from the resin presents an economically viable use for residues from mate-herb industries, whose family farmers benefit, with the bias of environmental and social sustainability, within the circular economy. Pheophytin ; Chlorophyll ; Chemistry ; Herb ; Botany ; Materials science ; Organic chemistry ; Biology ; Medicinal herbs ; Traditional medicine ; Photosynthesis Heavy Metals in Plants ; Pigment Synthesis and Properties
Authors: Mohammad Saraireh | A. M. Maqableh | Manar Jaradat | Omar A. Saraereh
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A Novel Method for Thermoelectric Generator Based on Neural Network The growing need for renewable energy and zero carbon dioxide emissions has fueled the development of thermoelectric generators with improved power generating capability. Along with the endeavor to develop thermoelectric materials with greater figures of merit, the geometrical and structural optimization of thermoelectric generators is equally critical for maximum power output and efficiency. Green energy strategies that are constantly updated are a viable option for addressing the global energy issue while also protecting the environment. There have been significant focuses on the development of thermoelectric modules for a range of solar, automotive, military, and aerospace applications in recent years due to various advantages including as low vibration, great reliability and durability, and the absence of moving components. In order to enhance the system performance of the thermoelectric generator, an artificial neural network (ANN) based algorithm is proposed. Furthermore, to achieve high efficiency and system stability, a buck converter is designed and deployed. Simulation and experimental findings demonstrate that the suggested method is viable and available, and that it is almost similar to the real value in the steady state with the least power losses, making it ideal for vehicle exhaust thermoelectric generator applications. Furthermore, the proposed hybrid algorithm has a high reference value for the development of a dependable and efficient car exhaust thermoelectric generating system. Thermoelectric generator ; Renewable energy ; Thermoelectric effect ; Automotive engineering ; Automotive industry ; Computer science ; Electricity generation ; Power (physics) ; Engineering ; Electrical engineering ; Aerospace engineering Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Innovative Energy Harvesting Technologies
Authors: Woon Hyung Cho | Jiseon Shin | Young Duck Kim | Jeil Jung
Source: Machine Learning Science and Technology (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Pixel-wise classification in graphene-detection with tree-based machine learning algorithms Abstract Mechanical exfoliation of graphene and its identification by optical inspection is one of the milestones in condensed matter physics that sparked the field of two-dimensional materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatization. We propose supervised pixel-wise classification methods showing a high performance even with a small number of training image datasets that require short computational time without GPU. We introduce four different tree-based machine learning (ML) algorithms—decision tree, random forest, extreme gradient boost, and light gradient boosting machine. We train them with five optical microscopy images of graphene, and evaluate their performances with multiple metrics and indices. We also discuss combinatorial ML models between the three single classifiers and assess their performances in identification and reliability. The code developed in this paper is open to the public and will be released at github.com/gjung-group/Graphene_segmentation . Artificial intelligence ; Computer science ; Machine learning ; Algorithm ; Gradient boosting ; Random forest ; Decision tree ; Pixel ; Identification (biology) ; Segmentation ; Tree (set theory) ; Pattern recognition (psychology) ; Mathematics Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Machine Learning and ELM