jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shashin Pavaskar | Riccardo Penco | Ira Z. Rothstein
Source: SciPost Physics (preprint, 2022-05-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An effective field theory of magneto-elasticity We utilize the coset construction to derive the effective field theory of magnon-phonon interactions in (anti)-ferromagnetic and ferrimagnetic insulating materials. The action is used to calculate the equations of motion which generalize the Landau-Lifshitz and stress equations to allow for magneto-acoustic couplings to all orders in the fields at lowest order in the derivative expansion. We also include the symmetry breaking effects due to Zeeman, and Dzyaloshinsky-Moriya interactions. This effective theory is a toolbox for the study of magneto-elastic phenomena from first principles. As an example we use this theory to calculate the leading order contribution to the magnon decay width due to its the decay into phonons. Magnon ; Effective field theory ; Physics ; Condensed matter physics ; Landau theory ; Magneto ; Elasticity (physics) ; Phonon ; Ferrimagnetism ; Ferromagnetism ; Magnetic field ; Quantum electrodynamics ; Quantum mechanics ; Magnet ; Magnetization ; Phase transition Magnetic Properties and Applications ; Magnetic properties of thin films ; High voltage insulation and dielectric phenomena
Authors: Hiroshi Kontani | Rina Tazai | Youichi Yamakawa | Seiichiro Onari
Source: arXiv (Cornell University) (preprint, 2022-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unconventional density waves and superconductivities in Fe-based superconductors and other strongly correlated electron systems To seek high-$T_c$ pairing mechanism, many scientists have focused on the mysterious spontaneous rotational symmetry breaking above Tc, such as nematic order at $q=0$ and smectic order at $q\ne0$. Such exotic correlation-driven symmetry breaking in metals has become a central issue in condensed matter physics. We demonstrate the emergence of the nematic and smectic orders due to orbital polarization ($n_{xz}\ne n_{yz}$) and the symmetry breaking in the correlated intersite hopping (= bond order $δt_{i,j}$) in Fe-based and cuprate superconductors. In addition, we discuss exotic spontaneous loop current orders driven by the pure imaginary $δt_{i,j}$. These interesting ``unconventional density-waves'' originate from the quantum interference between different spin fluctuations that is described by the vertex correction (VC) in the field theory. In the next stage, we discuss electron-correlation driven superconductivity due to the fluctuations of unconventional density-waves. For this purpose, we suggest the beyond-Migdal-Eliashberg gap equation by including the VCs into the equation. In Fe-based superconductors, high-$T_c$ $s$-wave superconductivity can be mediated by nematic and smectic fluctuations because the pairing interaction is magnified by the VCs. We also discuss the multipolar fluctuation pairing mechanism in heavy fermion systems, owing to the cooperation between the strong spin-orbit interaction and the strong electron correlation. To summarize, we suggest that the quantum interference mechanism described by the VCs is the key ingredients to explain not only various unconventional density-waves, but also exotic superconducting states in many strongly correlated metals. We finally discuss some interesting future issues with respect to the quantum interference mechanism. Pairing ; Physics ; Superconductivity ; Condensed matter physics ; Electron ; Cuprate ; Liquid crystal ; Symmetry breaking ; Electron pair ; Electronic correlation ; Fermion ; Quantum mechanics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Rare-earth and actinide compounds
Authors: Сергій Сергійович Курєннов | Kostiantyn Barakhov | Oleksii Vambol | Володимир Миколайович Степаненко
Source: Aerospace technic and technology (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

Напружено-деформований стан пластини, яка послаблена круговим вирізом та посилена двома круглими накладками The problem of axisymmetric deformation of a structure was solved in this article. This structure consists of a plate that was weakened by a circular cutout and at the same time reinforced by two concentric round patches. The patches are glued overlapping on both sides of the plate, in the place of the cutout. The patches are joined to the main plate by a thin adhesive layer and perceive shearing and tearing forces. To solve the problem, some hypotheses were used, namely: it was assumed that the stresses were distributed uniformly over the thickness of the adhesive layer. For patches, the Kirchhoff-Love hypothesis is adopted. The adhesive layer is considered an elastic Winkler base. An axisymmetric problem is considered. It is assumed that the displacement of the layers depends only on the radial coordinate and doesn’t depend on the angular coordinate. The main plate is not subjected to bending due to the symmetry of the structure. This problem is a generalization of the classic model of the stress state of the adhesive joint for the rods to an area with radial symmetry. These assumptions have allowed obtaining a solution to the problem in an analytical form. The problem is considered separately in the adhesive joint area and as well as the outside of the adhesive joint area. In the area of gluing, the problem is reduced to a seventh-order differential equation concerning shear stresses. The solution to this problem is presented as an expansion into a functional series in terms of modified Bessel functions of the second kind. The obtained shear stresses make it possible to obtain normal stresses, as well as radial and transverse displacements of the layers in the adhesive joint zone. The displacements outside the adhesive joint zone were obtained from the well-known differential equations for the deformation of round plates in the absence of shear forces. The unknown coefficients in both cases are found from the boundary and conjugation conditions. The model task was solved. A finite element model of an adhesive joint has been developed. The largest size of the adhesive layer element is chosen to be sufficiently small and equal to half the thickness of the adhesive layer because of the enormous stress gradients in the adhesive layer. The results of the analytical model were compared with those results of the finite element model. The comparison showed the high accuracy of the proposed model. Rotational symmetry ; Axial symmetry ; Adhesive ; Shearing (physics) ; Bessel function ; Geometry ; Bending of plates ; Joint (building) ; Mathematics ; Mathematical analysis ; Materials science ; Layer (electronics) ; Bending ; Structural engineering ; Composite material ; Engineering Structural mechanics and materials ; Material Properties and Applications ; Environmental and Industrial Safety
Authors: Chenru Duan | Daniel B. K. Chu | Aditya Nandy | Heather J. Kulik
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-01-14)
Field: Materials Science — Materials Chemistry

Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy Zip file for machine learning models built in arXiv:2201.04243. Models are all `tf.keras.model` object as .h5 file. Character (mathematics) ; Property (philosophy) ; Adiabatic process ; Computer science ; Ionization ; Molecule ; Transfer of learning ; Artificial intelligence ; Biological system ; Physics ; Machine learning ; Chemistry ; Mathematics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yanyan Chang | Li Guo
Source: Science of Advanced Materials (article, 2022-06-01)
Field: Chemistry — Organic Chemistry

Application Values of Coumarin Compound Against Pediatric Bronchopneumonia and Autonomous Prediction of Novel Structures with Machine Learning In this research, a new coumarin compound was obtained, and then its crystal architecture was determined. This complex’s application value against pediatric bronchopneumonia was identified, and the specific mechanism was simultaneously discussed. First, real time RT-PCR was conducted, and the suppressive activity on the relative expression of Streptococcus pneumonia survival gene was measured. Next, the inflammatory cytokines released into the alveolar lavage fluid was tested using ELISA. Through reinforcement learning and molecular docking simulation, several novel molecules with low binding affinity energy and high quantitative estimate of drug-likeness and synthetic accessibility scores were predicted, and their biological activities were evaluated due to the multiple hydrogen bonds with the given receptor. Coumarin ; Bronchopneumonia ; Hydrogen bond ; Docking (animal) ; Pneumonia ; Molecule ; Pharmacology ; Receptor ; Materials science ; Biology ; Medicine ; Biochemistry ; Veterinary medicine ; Chemistry ; Internal medicine ; Virology ; Organic chemistry Synthesis and Biological Evaluation ; Antimicrobial Peptides and Activities ; Synthesis and biological activity
Authors: Nikolaos N. Vlassis | Puhan Zhao | Ran Ma | Thomas D. Sewell | WaiChing Sun
Source: International Journal for Numerical Methods in Engineering (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

Featured Cover The cover image is based on the Original Article Molecular dynamics inferred transfer learning models for finite-strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints by Nikolaos Vlassis et al., https://doi.org/10.1002/nme.6992. Cover (algebra) ; Hyperelastic material ; Monoclinic crystal system ; Sobolev space ; Strain (injury) ; Mathematics ; Image (mathematics) ; Pure mathematics ; Calculus (dental) ; Computer science ; Crystallography ; Finite element method ; Physics ; Artificial intelligence ; Chemistry ; Biology ; Engineering ; Medicine ; Anatomy ; Mechanical engineering ; Thermodynamics ; Orthodontics Machine Learning in Materials Science
Authors: Nikolaj Rønne | Mads-Peter Verner Christiansen | Andreas Møller Slavensky | Zeyuan Tang | Florian Brix | Mikkel Elkjær Pedersen | Malthe Kjær Bisbo | Bjørk Hammer
Source: arXiv (Cornell University) (preprint, 2022-08-19)
Field: Materials Science — Materials Chemistry

Atomistic structure search using local surrogate mode We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based on a the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch $k$-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches. Formalism (music) ; Statistical physics ; Local search (optimization) ; Gaussian ; Computer science ; Local structure ; Mathematical optimization ; Algorithm ; Physics ; Mathematics ; Chemical physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Jesse J. Lutz | Daniel S. Jensen | Joshua A. Hubbard | Judit Zádor
Source:  (article, 2022-08-01)
Field: Chemistry — Inorganic Chemistry

Hydrolysis of uranium hexafluoride: mechanism, spectroscopy, and thermochemical kinetics. Abstract not provided. Uranium hexafluoride ; Kinetics ; Sulfur hexafluoride ; Uranium ; Mechanism (biology) ; Hydrolysis ; Spectroscopy ; Chemistry ; Materials science ; Chemical engineering ; Nuclear engineering ; Organic chemistry ; Engineering ; Physics Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Mihály Váradi | Damian Bertoni | Paulyna Magaña | Urmila Paramval | Ivanna Pidruchna | Malarvizhi Radhakrishnan | Maxim Tsenkov | Sreenath Nair | Milot Mirdita | Jingi Yeo | Oleg Kovalevskiy | Kathryn Tunyasuvunakool | Agata Laydon | Augustin Žídek | Hamish Tomlinson | Dhavanthi Hariharan | Josh Abrahamson | Tim Green | John Jumper | Ewan Birney | Martin Steinegger | Demis Hassabis | Sameer Velankar
Source: Nucleic Acids Research (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB. Biology ; Protein structure ; Computational biology ; Database ; Genetics ; Biochemistry ; Computer science Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Amil Merchant | Simon Batzner | Samuel S. Schoenholz | Muratahan Aykol | Gowoon Cheon | Ekin D. Cubuk
Source: Nature (article, 2023-11-29)
Field: Materials Science — Materials Chemistry

Scaling deep learning for materials discovery Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing<sup>1-11</sup>. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation<sup>12-14</sup>. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies<sup>15-17</sup>, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity. Computer science ; Deep learning ; Convex hull ; Scaling ; Artificial intelligence ; Nanotechnology ; Regular polygon ; Materials science ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Photocatalysis Techniques
Authors: Jon Agirre | Mihaela Atanasova | Haroldas Bagdonas | Charles B. Ballard | Arnaud Baslé | James Beilsten‐Edmands | Rafael J. Borges | D. Brown | J. Javier Burgos-Mármol | John M. Berrisford | Paul S. Bond | Iracema Caballero | Lucrezia Catapano | Grzegorz Chojnowski | Atlanta G. Cook | Kevin Cowtan | Tristan I. Croll | J.E. Debreczeni | N. E. Devenish | E.J. Dodson | Tarik R. Drevon | Paul Emsley | Gwyndaf Evans | Phil Evans | Maria Fando | James Foadi | Luis Fuentes‐Montero | Elspeth F. Garman | Markus Gerstel | Richard J. Gildea | Kaushik Hatti | Maarten L. Hekkelman | Philipp Heuser | Soon Wen Hoh | Michael A. Hough | Huw T. Jenkins | Elisabet Jiménez | Robbie P. Joosten | Ronan M. Keegan | N.H. Keep | Eugene Krissinel | Petr Kolenko | Oleg Kovalevskiy | Victor S. Lamzin | David M. Lawson | Andrey A. Lebedev | Andrew G. W. Leslie | Bernhard Lohkamp | Fei Long | Martin Malý | Airlie J. McCoy | Stuart McNicholas | Ana Medina | Claudia Millán | James W. Murray | Garib N. Murshudov | Robert A. Nicholls | M.E.M. Noble | Robert D. Oeffner | Neesh Pannu | James M. Parkhurst | Nicholas M. Pearce | Joana Pereira | Anastassis Perrakis | Harold R. Powell | Randy J. Read | Daniel J. Rigden | William Rochira | Massimo Sammito | Filomeno Sánchez Rodríguez | George M. Sheldrick | Kathryn L. Shelley | Felix Šimkovic | Adam J. Simpkin | Pavol Skubák | E. V. Sobolev | Roberto A. Steiner | Kyle Stevenson | Ivo Tews | Jens M. H. Thomas | Andrea Thorn | Josep Triviño | Ville Uski | Isabel Usón | Alexei A. Vagin | Sameer Velankar | M. Vollmar | Helen Walden | David G. Waterman | Keith S. Wilson | Martyn Winn | Graeme Winter | Marcin Wojdyr | Keitaro Yamashita
Source: Acta Crystallographica Section D Structural Biology (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

The <i>CCP</i>4 suite: integrative software for macromolecular crystallography The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world. Suite ; Software ; Computer science ; Software engineering ; Software suite ; Set (abstract data type) ; Citation ; Data science ; Programming language ; World Wide Web ; Political science Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Nathan J. Szymanski | Bernardus Rendy | Yuxing Fei | Rishi E. Kumar | Tanjin He | David Milsted | Matthew J. McDermott | Max C. Gallant | Ekin D. Cubuk | Amil Merchant | Haegyeom Kim | Anubhav Jain | Christopher J. Bartel | Kristin A. Persson | Yan Zeng | Gerbrand Ceder
Source: Nature (article, 2023-11-29)
Field: Materials Science — Materials Chemistry

An autonomous laboratory for the accelerated synthesis of inorganic materials To close the gap between the rates of computational screening and experimental realization of novel materials<sup>1,2</sup>, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics. Artificial intelligence ; Realization (probability) ; Computer science ; Robotics ; Machine learning ; Stability (learning theory) ; Variety (cybernetics) ; Computation ; Plan (archaeology) ; Set (abstract data type) ; Robot ; Programming language Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Daniil A. Boiko | Robert MacKnight | Ben Kline | Gabriel dos Passos Gomes
Source: Nature (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Autonomous chemical research with large language models Transformer-based large language models are making significant strides in various fields, such as natural language processing<sup>1-5</sup>, biology<sup>6,7</sup>, chemistry<sup>8-10</sup> and computer programming<sup>11,12</sup>. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research. Computer science ; Documentation ; Automation ; Transformer ; Artificial intelligence ; The Internet ; Natural language ; Software engineering ; Natural language understanding ; Human–computer interaction ; Systems engineering ; Programming language ; World Wide Web ; Engineering Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Innovative Microfluidic and Catalytic Techniques Innovation