jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David A. Winkler | Tim Würger | Di Mei | Bahram Vaghefinazari | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Robert H. Meißner | Christian Feiler
Source: Figshare (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Exploring the Structure-Property Relationship of Magnesium Dissolution Modulators https://www.nature.com/articles/s41529-020-00148-zAbstract: Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals. Property (philosophy) ; Magnesium ; Dissolution ; Materials science ; Metallurgy ; Chemistry ; Epistemology ; Philosophy Magnesium Oxide Properties and Applications ; Mine drainage and remediation techniques ; Phosphorus and nutrient management
Authors: Daniel I. Glazer | Kevin Honz | Riju Banerjee | Anna Binion | Eric Hudson
Source: Bulletin of the American Physical Society (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Characterizing Atomic Scale Strain Variations in 2D Materials with a Convolutional Neural Network Convolutional neural network ; Atomic units ; Strain (injury) ; Scale (ratio) ; Biological system ; Materials science ; Computer science ; Physics ; Artificial intelligence ; Biology ; Quantum mechanics ; Anatomy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Nobuhiko Kato | Kazuki Mori | Masahiro Saito | Kenji Konashi
Source: The Japan Society of Applied Physics (article, 2021-01-26)
Field: Chemistry — Organic Chemistry

A neural network molecular dynamics study of the thermal properties of CeO 2 Materials science ; Molecular dynamics ; Thermal ; Dynamics (music) ; Artificial neural network ; Nanotechnology ; Artificial intelligence ; Computer science ; Psychology ; Thermodynamics ; Physics ; Quantum mechanics Chemical Thermodynamics and Molecular Structure ; Neural Networks and Applications ; Thermal and Kinetic Analysis
Authors: Rebecca Lindsey | Sorin Bastea | Nir Goldman | Laurence E. Fried
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Machine-Learning Improved Density Functional Tight Binding Models for Energetic Materials Tight binding ; Materials science ; Computer science ; Physics ; Condensed matter physics ; Electronic structure Machine Learning in Materials Science
Authors: Adam Liebendorfer
Source: Scilight (article, 2021-06-23)
Field: Materials Science — Materials Chemistry

Hierarchical machine learning framework pushes boundaries of multi-property material discovery A generative transfer learning approach allows the hierarchical correlation learning for multiproperty prediction framework to synthesize composition and element-property relationships at multiple scales. Generative grammar ; Property (philosophy) ; Computer science ; Artificial intelligence ; Generative model ; Transfer of learning ; Element (criminal law) ; Machine learning Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Andrea Gardin | Claudio Perego | Giovanni Doni | Giovanni M. Pavan
Source: arXiv (Cornell University) (preprint, 2021-12-15)
Field: Materials Science — Materials Chemistry

Classifying soft self-assembled materials via unsupervised machine learning of defects Unlike molecular crystals, soft self-assembled fibres, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers, but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven "defectometer" to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them. Supramolecular chemistry ; Metric (unit) ; Molecular dynamics ; Workflow ; Computer science ; Nanotechnology ; Materials science ; Cluster analysis ; Data-driven ; Biological system ; Micelle ; Statistical physics ; Artificial intelligence ; Physics ; Molecule ; Chemistry ; Computational chemistry ; Engineering ; Database Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Crystallography and molecular interactions
Authors: I. Miyaguchi | Miwa Sato | Akiko Kashima | Hiroyuki Nakagawa | Yuichi Kokabu | Biao Ma | Shigeyuki Matsumoto | Atsushi Tokuhisa | Masateru Ohta | Mitsunori Ikeguchi
Source: Research Square (preprint, 2021-07-09)
Field: Materials Science — Materials Chemistry

QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learning Abstract Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution. Quality (philosophy) ; Quality assessment ; Crystal (programming language) ; Computer science ; Artificial intelligence ; Protein crystallization ; Machine learning ; Chemistry ; Evaluation methods ; Engineering ; Reliability engineering ; Physics ; Crystallization Enzyme Structure and Function ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xinqiao Wang | Chuansheng Yao | Yun Zhang | Jiahui Yu | Haoran Qiao | Chengyun Zhang | Yejian Wu | Renren Bai | Hongliang Duan
Source: ChemRxiv (preprint, 2021-08-06)
Field: Materials Science — Materials Chemistry

From Theory to Experiment: Transformer-Based Generation Enables Rapid Discovery of Novel Reactions Deep learning methods have been proven their potential roles in the chemical field, such as reaction prediction and retrosynthesis analysis. However, the de novo generation of unreported reactions using artificial intelligence technology remains not be completely explored. Inspired by molecular generation, we proposed the task of novel reaction generation. In this work, we applied the Heck reactions to train the transformer model, state-of-art natural language process model and obtained 4717 generated reactions after sampling and processing. We then confirmed that 2253 novel Heck reactions by organizing chemists to judge the generated reactions, and adopted organic synthesis experiment to verify the feasibility of unreported reactions. In this process, it only took 15 days from Heck reaction generation to experimental verification, proving that our model learns reaction rules in-depth and can make great contributions in the novel reaction discovery. Transformer ; Computer science ; Retrosynthetic analysis ; Artificial intelligence ; Heck reaction ; Process (computing) ; Chemistry ; Engineering ; Programming language ; Palladium ; Organic chemistry ; Electrical engineering Machine Learning in Materials Science
Authors: L. Domingo | F. Borondo
Source: Communications in Nonlinear Science and Numerical Simulation (preprint, 2021-08-31)
Field: Materials Science — Materials Chemistry

Deep learning methods for the computation of vibrational wavefunctions Wave function ; Excited state ; Hamiltonian (control theory) ; Computation ; Quantum ; Artificial neural network ; Quantum chemistry ; Statistical physics ; Computer science ; Physics ; Quantum mechanics ; Artificial intelligence ; Theoretical physics ; Mathematics ; Molecule ; Algorithm ; Mathematical optimization Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Advanced Physical and Chemical Molecular Interactions
Authors: Max Veit | David M. Wilkins | Yang Yang | Robert A. DiStasio | Michele Ceriotti
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning the molecular dipole moment with atomic partial charges and atomic dipoles Dipole ; Bond dipole moment ; Moment (physics) ; Atomic charge ; Atomic physics ; Physics ; Partial charge ; Transition dipole moment ; Charge (physics) ; Quantum mechanics ; Molecule Various Chemistry Research Topics ; Molecular spectroscopy and chirality
Authors: Ilya Svetlizky | Seong-Soo Kim | Seong Ho Pahng | Agnese Curatolo | Michael P. Brenner | David A. Weitz | F. Spaepen
Source: Bulletin of the American Physical Society (article, 2021-03-19)
Field: Materials Science — Materials Chemistry

Large-scale visualization with machine learning of dislocation networks in colloidal single crystals Visualization ; Dislocation ; Scale (ratio) ; Materials science ; Computer science ; Colloidal crystal ; Colloid ; Crystallography ; Artificial intelligence ; Physics ; Chemistry ; Composite material ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science
Authors: Fatemah Mukadum | Quan Nguyen | Daniel Adrion | Gabriel Appleby | Rui Chen | Haley Dang | Remco Chang | Roman Garnett | Steven A. Lopez
Source: ChemRxiv (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

Efficient discovery of visible light-activated azoarene photoswitches with long half-livesusing active search Photoswitches are molecules that undergo a reversible, structural isomerization after exposure to different wavelengths of light. The dynamic control offered by molecular photoswitches is favorable for applications in materials chemistry, photopharmacology, and catalysis. Ideal photoswitches absorb visible light and have long-lived metastable isomers. We used high throughput virtual screening to predict the absorption maxima (λmax) of the E-isomer and half-lives (t1/2) of the Z-isomer. However, computing the photophysical and kinetic properties of each entry of a virtual molecular library containing 103–106 entries with density functional theory is prohibitively time-consuming. We applied active search, a machine learning technique to intelligently search a chemical search space of 255991 photoswitches based on 29 known azoarenes and their derivatives. We iteratively trained the active search algorithm based on whether a candidate absorbed visible light (λmax > 450 nm). Active search was found to triple the discovery rate compared to random search. Further, we projected 1962 photoswitches to 2D using the Uniform Manifold Approximation and Projection (umap) algorithm and found that λmax depends on the core, which is tunable with substituents. We then incorporated a second stage of screening with to predict the stabilities of the Z-isomers for the top 1% of candidates. We identified four ideal photoswitches that concurrently satisfy λmax > 450 nm and t1/2 > 2 hours; the range of λmax and t1/2 range from 465 to 531 nm and hours to years, respectively. Isomerization ; Virtual screening ; Spiropyran ; Cucurbituril ; Chemistry ; Photochromism ; Molecule ; Physics ; Computer science ; Photochemistry ; Computational chemistry ; Molecular dynamics ; Catalysis ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Advanced biosensing and bioanalysis techniques ; Machine Learning in Materials Science
Authors: Nikolaj Rørbæk Knøsgaard | Kristian S. Thygesen
Source: arXiv (Cornell University) (preprint, 2021-07-13)
Field: Materials Science — Materials Chemistry

Representing individual electronic states in crystals for machine learning quasiparticle band structures We address the problem of representing quantum states of electrons in a solid for the purpose of machine leaning state-specific electronic properties. Specifically, we construct a fingerprint based on energy decomposed operator matrix elements (ENDOME) and radially decomposed projected density of states (RAD-PDOS), which are both obtainable from a standard density functional theory (DFT) calculation. Using such fingerprints we train a gradient boosting model on a set of 46k G$_0$W$_0$ quasiparticle energies. The resulting model predicts the self-energy correction of states in materials not seen by the model with a mean absolute error of 0.14 eV. By including the material's calculated dielectric constant in the fingerprint the error can be further reduced by 30%, which we find is due to an enhanced ability to learn the correlation/screening part of the self-energy. Our work paves the way for accurate estimates of quasiparticle band structures at the cost of a standard DFT calculation. Quasiparticle ; Dielectric ; Density functional theory ; Statistical physics ; Physics ; Matrix (chemical analysis) ; Condensed matter physics ; Quantum mechanics ; Computational physics ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Masashi Tsubaki
Source: The Brain & Neural Networks (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Physically-informed Data-driven Deep Learning and Prospect for Transfer Learning in Materials Informatics 本稿では，マテリアルズ・インフォマティクスにおけるデータ駆動型機械学習の研究動向について解説する．特に，分子や結晶における物性と機能の問題，量子化学計算による物性データベース，分子や結晶の記述子，そして深層学習アプローチによる物性予測などについて解説する．そして筆者自身の研究も紹介しながら，今後より重要となる機能予測のため，物理化学と融合した転移学習について展望を述べたい． Transfer of learning ; Informatics ; Computer science ; Data science ; Artificial intelligence ; Engineering Machine Learning in Materials Science
Authors: Nikita Serov | Vladimir V. Vinogradov
Source: ChemRxiv (preprint, 2021-06-08)
Field: Materials Science — Materials Chemistry

Inverse Material Search and Synthesis Verification by Hand Drawings via Transfer Learning and Contour Detection Nanomaterials of various morphologies and chemistry have an extensive use as photonic devices, advanced catalysts, sorbents for water purification, agrochemicals, platforms for drug delivery as well as imaging systems to name a few. However, search for synthesis routes giving custom nanomaterials for particular needs with the desired structure, shape, and size remains a challenge and is often implemented by manual research articles screening. Here, we develop for the first time scanning and transmission electron microscopy (SEM/TEM) reverse image search and hand drawing-based search via transfer learning (TL), namely, VGG16 convolutional neural network (CNN) repurposing for image features extraction and subsequent image similarity determination. Moreover, we demonstrate case use of this platform on calcium carbonate system, where sufficient amount of data was acquired by random high throughput multiparametric synthesis, as well as on Au nanoparticles (NPs) data extracted from the articles. This approach can be not only used for advanced nanomaterials search and synthesis procedure verification, but also can be further combined with machine learning (ML) solutions to provide data-driven novel nanomaterials discovery. Computer science ; Convolutional neural network ; Nanomaterials ; Nanotechnology ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source:  (preprint, 2021-12-16)
Field: Materials Science — Materials Chemistry

Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI3 are well described by this approach. Octahedron ; Representation (politics) ; Multiferroics ; Irreducible representation ; Ideal (ethics) ; Crystal structure ; Perovskite (structure) ; Crystal (programming language) ; Face (sociological concept) ; Enhanced Data Rates for GSM Evolution ; Range (aeronautics) ; Symmetry (geometry) ; Crystallography ; Basis (linear algebra) ; Materials science ; Computer science ; Artificial intelligence ; Theoretical physics ; Mathematics ; Physics ; Chemistry ; Geometry ; Pure mathematics ; Sociology ; Ferroelectricity ; Optoelectronics Machine Learning in Materials Science ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2021-11-12)
Field: Materials Science — Materials Chemistry

Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI3 are well described by this approach. Octahedron ; Multiferroics ; Representation (politics) ; Irreducible representation ; Perovskite (structure) ; Ideal (ethics) ; Crystal structure ; Crystal (programming language) ; Face (sociological concept) ; Range (aeronautics) ; Symmetry (geometry) ; Enhanced Data Rates for GSM Evolution ; Basis (linear algebra) ; Crystallography ; Materials science ; Computer science ; Artificial intelligence ; Theoretical physics ; Mathematics ; Physics ; Geometry ; Chemistry ; Pure mathematics ; Sociology ; Ferroelectricity ; Optoelectronics Machine Learning in Materials Science ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: Ismaeel Ramzan | Linghan Kong | Richard A. Bryce | Neil A. Burton
Source: ChemRxiv (preprint, 2021-04-20)
Field: Materials Science — Materials Chemistry

Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces &lt;p&gt;We present a new chemically intuitive approach, pairF-Net, to directly predict the atomic forces in a molecule to quantum chemistry accuracy using machine learning techniques. A residual artificial neural network has been designed and trained with features and targets based on pairwise interatomic forces, to determine the Cartesian atomic forces suitable for use in molecular mechanics and dynamics calculations. The scheme implicitly maintains rotational and translational invariance and predicts Cartesian forces as a linear combination of a set of force components in an &lt;a&gt;interatomic basis.&lt;/a&gt; We show that the method can predict the reconstructed Cartesian atomic forces for a set of small organic molecules to less than 2 kcal mol&lt;sup&gt;-1&lt;/sup&gt; Å&lt;sup&gt;-1&lt;/sup&gt; from the reference force values obtained via density functional theory. The pairF-Net scheme utilises a simple and chemically intuitive route to furnish atomic forces at a quantum mechanical level but at a fraction of the cost, providing a step towards the efficient calculation of accurate thermodynamic properties. &lt;/p&gt; Cartesian coordinate system ; Chemistry ; Pairwise comparison ; Molecule ; Central force ; Physics ; Statistical physics ; Classical mechanics ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Computer science ; Geometry Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods