jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marc Stieffenhofer | Tristan Bereau | Michael Wand
Source: arXiv (Cornell University) (article, 2021-01-13)
Field: Materials Science — Materials Chemistry

Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging. In our previous study we have introduced deepBackmap: a deep neural-network-based approach to reverse-map equilibrated molecular structures for condensed-phase systems. Our method combines data-driven and physics-based aspects, leading to high-quality reconstructed structures. In this work, we expand the scope of our model and examine its chemical transferability. To this end, we train deepBackmap solely on homogeneous molecular liquids of small molecules, and apply it to a more challenging polymer melt. We augment the generator's objective with different force-field-based terms as prior to regularize the results. The best performing physical prior depends on whether we train for a specific chemistry, or transfer our model. Our local environment representation combined with the sequential reconstruction of fine-grained structures help reach transferability of the learned correlations. Transferability ; Scope (computer science) ; Benchmark (surveying) ; Computer science ; Representation (politics) ; Artificial neural network ; Phase (matter) ; Homogeneous ; Generator (circuit theory) ; Materials science ; Deep learning ; Force field (fiction) ; Artificial intelligence ; Field (mathematics) ; Nanotechnology ; Biological system ; Statistical physics ; Machine learning ; Physics ; Cartography ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Manuel Cordova | M. Balodis | Bruno Simões de Almeida | Michele Ceriotti | Lyndon Emsley
Source: Science Advances (article, 2021-11-26)
Field: Chemistry — Spectroscopy

Bayesian probabilistic assignment of chemical shifts in organic solids A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the <sup>13</sup>C and <sup>1</sup>H assignment of 11 molecular solids with experimental shifts and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases. Probabilistic logic ; Bayesian probability ; Computer science ; Artificial intelligence Advanced NMR Techniques and Applications ; Chemical Thermodynamics and Molecular Structure ; Lanthanide and Transition Metal Complexes
Authors: Chen Wang | Jiangtao Wu | Zezhu Zeng | Jan Peter Embs | Yanzhong Pei | Jie Ma | Yue Chen
Source: npj Computational Materials (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

Soft-mode dynamics in the ferroelectric phase transition of GeTe Abstract GeTe that exhibits a strong anharmonicity and a ferroelectric phase transition between the rhombohedral and cubic structures has emerged as one of the leading thermoelectric materials. Herein, combining molecular dynamics simulations and inelastic neutron scattering measurements, the lattice dynamics in GeTe have been investigated to reveal the soft-mode mechanisms across the phase transition. We have constructed a first-principles-based machine-learning interatomic potential, which successfully captures the dynamical ferroelectric phase transition of GeTe by adopting the neural network technique. Although the low-energy acoustic phonons remain relatively unaffected at elevated temperatures, the high-energy optical, and longitudinal acoustic phonons demonstrate strong renormalizations as evidenced from the vibrational phonon spectra, which are attributed to the large anharmonicity accompanying the phase transition. Furthermore, our results reveal a nonmonotonic temperature dependence of the soft-modes beyond the perturbative regime. The insight provided by this work into the soft-modes may pave the way for further phonon engineering of GeTe and the related thermoelectrics. Anharmonicity ; Soft modes ; Phonon ; Condensed matter physics ; Ferroelectricity ; Phase transition ; Thermoelectric materials ; Materials science ; Interatomic potential ; Thermoelectric effect ; Molecular dynamics ; Inelastic neutron scattering ; Scattering ; Neutron scattering ; Physics ; Optics ; Optoelectronics ; Thermodynamics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides ; Machine Learning in Materials Science
Authors: G. R. Arpitha | H. Mohit | K. N. Bharath | Shivakumar Neerakallu | Senthil Muthu Kumar Thiagamani | Mochamad Asrofi
Source: Journal of Industrial Textiles (article, 2021-12-30)
Field: Materials Science — Polymers and Plastics

An artificial neural network prediction on physical, mechanical, and thermal characteristics of giant reed fiber reinforced polyethylene terephthalate composite Plant fiber reinforced hybrid polymer composites have had broad applications recently because of their lower cost advantages, lower weight, and biodegradable nature. The present work studies the influence of reinforcing giant reed fiber concentration in polyethylene terephthalate (PET) polymer for their physical, mechanical, and thermal characteristics and determines the optimum loading of giant reed fiber using an artificial neural network (ANN) scheme. Giant reed fiber reinforced PET matrix laminates were manufactured from compression molding with different fiber loadings such as 5 wt.%, 10 wt.%, and 20 wt.%. The mechanical characteristics such as tensile and flexural strength and the laminate’s tensile and flexural modulus were appraised and examined. The maximum value of tensile strength, flexural strength, tensile modulus, and flexural modulus were 5.4 MPa, 26 MPa, 8343 MPa, and 6300 MPa, respectively, for PET2 (10 wt.% of giant reed fiber in PET polymer) composite. Fiber pullout, gaps, and fracture behavior were examined from a scanning electron microscope in the microstructural analysis. A machine learning technique has been recommended to combine artificial intelligence while designing giant reed fiber reinforced polymeric laminates. Using the suggested method, an ANN model has been generated to attain the targeted giant reed fiber concentration for PET composite while gratifying the necessary targeted characteristics. The developed method is very effective and decreases the effort and time of material characterization for huge specimens. It will support the researchers in designing their forthcoming test efficiently. Materials science ; Flexural strength ; Composite material ; Ultimate tensile strength ; Polyethylene terephthalate ; Fiber ; Flexural modulus ; Compression molding ; Composite number ; Tensile testing Natural Fiber Reinforced Composites ; Acoustic Wave Phenomena Research ; Innovations in Concrete and Construction Materials
Authors: Stylianos Tsopanidis | Shmuel Osovski
Source: Materials Characterization (article, 2021-11-03)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning in fractography: Evaluation and interpretation Fractography ; Fracture (geology) ; Artificial intelligence ; Materials science ; Pipeline (software) ; Machine learning ; Tungsten ; Process (computing) ; Interpretation (philosophy) ; Computer science ; Forensic engineering ; Metallurgy ; Composite material ; Engineering Machine Learning in Materials Science ; Mineral Processing and Grinding ; Material Properties and Failure Mechanisms
Authors: Li‐Cheng Yang | Xin Li | Shuo‐Qing Zhang | Xin Hong
Source: Organic Chemistry Frontiers (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization DFT-computed structure–activity relationship data and physical organic descriptors create accurate machine learning model for HAT barrier prediction in photoredox-mediated HAT catalysis. Chemistry ; Surface modification ; Hydrogen atom ; Reactivity (psychology) ; Photoredox catalysis ; Atom (system on chip) ; Photochemistry ; Combinatorial chemistry ; Organic chemistry ; Physical chemistry ; Group (periodic table) ; Photocatalysis ; Catalysis Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Machine Learning in Materials Science
Authors: Jejoon Yeon | Sanjib C. Chowdhury | Chaitanya M. Daksha | John W. Gillespie
Source: The Journal of Physical Chemistry C (article, 2021-08-11)
Field: Materials Science — Ceramics and Composites

Development of Mg/Al/Si/O ReaxFF Parameters for Magnesium Aluminosilicate Glass Using an Artificial Neural Network-Assisted Genetic Algorithm Commercial high-strength S-glass fiber used in structural composites mainly consists of SiO2, Al2O3, and MgO. There is no established reactive force field to characterize S-glass fiber. In this study, a newly developed artificial neural network (ANN)-assisted genetic algorithm (GA) is applied to optimize a new ReaxFF parameter set to describe Mg/Al/Si/O interactions in S-glass and other magnesium aluminosilicate (MAS) glass compositions. The training set includes the density functional theory data of the energy response of various Mg/Al/Si/O crystals during volumetric expansion and compression and Mg migration inside Mg/Al/Si/O crystals. Test molecular dynamics simulations showed the characteristics of tectosilicate MAS glasses. Different structural properties, including oxide coordination, density, structural factors, and mechanical properties, showed fair agreement with references from experiments and other simulations. A newly developed GA-ANN parametrization algorithm assisted the training process. This force field can be used for virtual composition mapping to develop new glass fiber materials. We also believe our force field would support computational studies of mechanical properties of amorphous materials used in geochemistry, construction, and protective material applications. ReaxFF ; Materials science ; Aluminosilicate ; Molecular dynamics ; Force field (fiction) ; Amorphous solid ; Composite material ; Artificial neural network ; Magnesium ; Genetic algorithm ; Calcium aluminosilicate ; Interatomic potential ; Computer science ; Machine learning ; Computational chemistry ; Metallurgy ; Artificial intelligence ; Chemistry ; Crystallography Glass properties and applications ; Advanced ceramic materials synthesis ; Ferroelectric and Piezoelectric Materials
Authors: Damian M. Wilary | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2021-09-15)
Field: Materials Science — Materials Chemistry

ReactionDataExtractor: A Tool for Automated Extraction of Information from Chemical Reaction Schemes Chemical reaction schemes are commonly used for visual encapsulation of chemical information. Figures of reaction schemes contain chemical transformations, the chemical species involved, as well as reaction conditions. From a data-mining point of view, they constitute rich sources, densely packed with knowledge. Yet, the challenge of automatically extracting data from them has remained largely untackled. This work presents ReactionDataExtractor, a software tool that can be used for the automatic extraction of information from multistep reaction schemes. Its capabilities include segmentation of reaction steps, regions containing reaction conditions, chemical diagrams, as well as optical character and structure recognition. A combination of rules and unsupervised machine-learning approaches is used, with bespoke detection algorithms that identify arrows, structures, labels, and conditions detection algorithms. It can be used as a low-maintenance tool for database generation capable of extracting data from large quantities of images supplied by the user. On assessment using a self-generated evaluation set, the tool achieved precision and recall metrics of between 67% and 91% in the six core areas of data extraction. The ReactionDataExtractor tool is released under the MIT license and is available to download from http://www.reactiondataextractor.org. Computer science ; Bespoke ; Data mining ; Software ; Artificial intelligence ; Information extraction ; Chemical database ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Soroush Ahmadi | Pradip Kumar Mondal | Yuanyi Wu | Weizhong Gong | Mahmoud Mirmehrabi | Sohrab Rohani
Source: Crystal Growth & Design (article, 2021-09-15)
Field: Chemistry — Physical and Theoretical Chemistry

Virtual Multicomponent Crystal Screening: Hydrogen Bonding Revisited Pharmaceutical cocrystals, salts, and multicomponent crystals, in general, have increasingly come under the spotlight in recent years. A fast and efficient a priori theoretical classifier to identify potential coformers is highly sought after to complement the experimental brute force screening methods. This research examines the qualitative approaches that are based on hydrogen bonding strength. First, molecular electrostatic potential (MEP) maps of 330 coformers were obtained from density functional theory simulations, using two geometries: experimentally determined crystal structures and gas-phase optimization. An in-depth comparison of MEPs revealed the potential pitfalls of these two geometries that are deliberated at length in the manuscript. Next, six APIs and their reported salts/cocrystals on the Cambridge Structural Database (CSD) were inversely predicted with MEP analysis. For two of these APIs, the prediction showed systematic errors that are resolved with suggestions provided in the manuscript. Subsequently, hydrogen bond energy (HBE) and hydrogen bond propensity (HBP) calculations were put to the test with two APIs and 52 organic coformers. Finally, multivariate logistic regression, a linear machine learning (ML) algorithm, showed how a combination of HBE and HBP can be a superior classifier, for which 18 out of 25 positive cases were uninterruptedly identified at the top of the list. Provided that a database of failed attempts of cocrystallization is compiled within the scientific community to supplement the existing positive results (multicomponent crystals in the CSD), the combination of chemistry-based parameters and ML can be a promising classifier for coformer selection. Hydrogen bond ; Crystal structure prediction ; Density functional theory ; Chemistry ; Classifier (UML) ; Computer science ; Virtual screening ; Crystal engineering ; Hydrogen ; Crystal structure ; Machine learning ; Artificial intelligence ; Molecule ; Nanotechnology ; Computational chemistry ; Materials science ; Crystallography ; Molecular dynamics ; Organic chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Yue Cui | Huck Beng Chew
Source: Acta Materialia (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction of Atomistic Stress along Grain Boundaries Grain boundary ; Materials science ; Cauchy stress tensor ; Stress (linguistics) ; Molecular dynamics ; Statistical physics ; High-resolution transmission electron microscopy ; Electron holography ; Transmission electron microscopy ; Classical mechanics ; Microstructure ; Computational chemistry ; Physics ; Nanotechnology ; Composite material Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Electron Microscopy Techniques and Applications
Authors: Julien Parinet
Source: Chemosphere (article, 2021-03-01)
Field: Chemistry — Spectroscopy

Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets Molecular descriptor ; Support vector machine ; Retention time ; Chromatography ; Chemistry ; Artificial intelligence ; Resolution (logic) ; Pattern recognition (psychology) ; Biological system ; Quantitative structure–activity relationship ; Machine learning ; Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Pesticide Residue Analysis and Safety
Authors: Xintong Zhao | Jane Greenberg | Yuan An | Xiaohua Hu
Source: 2021 IEEE International Conference on Big Data (Big Data) (article, 2021-12-15)
Field: Materials Science — Materials Chemistry

Fine-Tuning BERT Model for Materials Named Entity Recognition Scientific literature presents a wellspring of cutting-edge knowledge for materials science, including valuable data (e.g., numerical data from experiment results, material properties and structure). These data are critical for accelerating materials discovery by data-driven machine learning (ML) methods. The challenge is, it is impossible for humans to manually extract and retain this knowledge due to the extensive and growing volume of publications.To this end, we explore a fine-tuned BERT model for extracting knowledge. Our preliminary results show that our fine-tuned Bert model reaches an f-score of 85% for the materials named entity recognition task. The paper covers background, related work, methodology including tuning parameters, and our overall performance evaluation. Our discussion offers insights into our results, and points to directions for next steps. Computer science ; Task (project management) ; Enhanced Data Rates for GSM Evolution ; Named-entity recognition ; Volume (thermodynamics) ; Entity linking ; Artificial intelligence ; Knowledge extraction ; Data modeling ; Training set ; Machine learning ; Data science ; Information retrieval ; Natural language processing ; Knowledge base ; Database ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Minkai Xu | Shitong Luo | Yoshua Bengio | Jian Peng | Jian Tang
Source: arXiv (Cornell University) (preprint, 2021-02-20)
Field: Materials Science — Materials Chemistry

Learning Neural Generative Dynamics for Molecular Conformation Generation We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling. Computer science ; Molecular dynamics ; Generative grammar ; Probabilistic logic ; Graph ; Artificial intelligence ; Generative model ; Range (aeronautics) ; Machine learning ; Theoretical computer science ; Chemistry ; Computational chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: I. A. Balyakin | С. И. Садовников
Source: Computational Materials Science (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Deep learning potential for superionic phase of Ag2S Mean squared displacement ; Radial distribution function ; Diffusion ; Molecular dynamics ; Ab initio ; Phase (matter) ; Ionic bonding ; Pair distribution function ; Chemistry ; Chemical physics ; Molecular physics ; Materials science ; Statistical physics ; Thermodynamics ; Physics ; Computational chemistry ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Quantum Dots Synthesis And Properties
Authors: Leonardo Iannucci
Source: IEEE Instrumentation & Measurement Magazine (article, 2021-06-01)
Field: Chemistry — Analytical Chemistry

Chemometrics for Data Interpretation: Application of Principal Components Analysis (PCA) to Multivariate Spectroscopic Measurements Extracting relevant and useful information from measurements is an issue of paramount importance and it can be considered as complementary to the process of data acquisition. This is a crucial point especially in the field of chemical measurements, where data sets can consist of hundreds or even thousands of variables so their interpretation can require long time. Chemometrics try to tackle this issue by applying mathematical and statistical tools to data coming from chemical, biological or medical analyses. Among possible methods, Principal Components Analysis (PCA) has found wide application in the I&M field thanks to its ability to identify patterns in acquired measurements and classify data in different groups. Possible applications span from chemicals detection [1] to concentration estimation of compounds in a given system [2]. Actually, many studies demonstrated the possibility to use PCA to process different kinds of data [3], in some cases coupling PCA to other tools such as artificial neural networks to improve the processing performance [4]. Chemometrics ; Principal component analysis ; Data mining ; Computer science ; Field (mathematics) ; Data processing ; Artificial intelligence ; Artificial neural network ; Process (computing) ; Multivariate statistics ; Pattern recognition (psychology) ; Interpretation (philosophy) ; Principal (computer security) ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: B.S. Reddy | Kim Hong In | Bharat B. Panigrahi | Uma Maheshwera Reddy Paturi | K.K. Cho | N.S. Reddy
Source: Materials Today Communications (article, 2021-02-06)
Field: Materials Science — Biomaterials

Modeling tensile strength and suture retention of polycaprolactone electrospun nanofibrous scaffolds by artificial neural networks Electrospinning ; Polycaprolactone ; Materials science ; Ultimate tensile strength ; Composite material ; Artificial neural network ; Tissue engineering ; Polymer ; Scaffold ; Biomedical engineering ; Computer science ; Artificial intelligence Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Advanced Machining and Optimization Techniques
Authors: Serkan Öğüt | Hasan Kaya | Aykut Kentli | Mehmet Uçar
Source: The International Journal of Advanced Manufacturing Technology (article, 2021-07-24)
Field: Materials Science — Materials Chemistry

Applying hybrid equal channel angular pressing (HECAP) to pure copper using optimized Exp.-ECAP die Pressing ; Materials science ; Die (integrated circuit) ; Copper ; Microstructure ; Metallurgy ; Aluminium ; Composite material Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Advanced Welding Techniques Analysis