jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Philomena Schlexer | Tej S. Choksi | Verena Streibel | Frank Abild‐Pedersen
Source: arXiv (Cornell University) (preprint, 2020-05-05)
Field: Materials Science — Materials Chemistry

Artificial intelligence real-time prediction and physical interpretation\n of atomic binding energies in nano-scale metal clusters Single atomic sites often determine the functionality and performance of\nmaterials, such as catalysts, semi-conductors or enzymes. Computing and\nunderstanding the properties of such sites is therefore a crucial component of\nthe rational materials design process. Because of complex electronic effects at\nthe atomic level, atomic site properties are conventionally derived from\ncomputationally expensive first-principle calculations, as this level of theory\nis required to achieve relevant accuracy. In this study, we present a widely\napplicable machine learning (ML) approach to compute atomic site properties\nwith high accuracy in real time. The approach works well for complex\nnon-crystalline atomic structures and therefore opens up the possibility for\nhigh-throughput screenings of nano-materials, amorphous systems and materials\ninterfaces. Our approach includes a robust featurization scheme to transform\natomic structures into features which can be used by common machine learning\nmodels. Performing a genetic algorithm (GA) based feature selection, we show\nhow to establish an intuitive physical interpretation of the structure-property\nrelations implied by the ML models. With this approach, we compute atomic site\nstabilities of metal nanoparticles ranging from 3-55 atoms with mean absolute\nerrors in the range of 0.11-0.14 eV in real time. We also establish the\nchemical identity of the site as most important factor in determining atomic\nsite stabilities, followed by structural features like bond distances and\nangles. Both, the featurization and GA feature selection functionality are\npublished in open-source python modules. With this method, we enable the\nefficient rational design of highly specialized real-world nano-catalysts\nthrough data-driven materials screening.\n Atomic units ; Python (programming language) ; Computer science ; Atomic model ; Artificial intelligence ; Nanotechnology ; Materials science ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Rohit Batra | Hanjun Dai | Tran Doan Huan | Lihua Chen | Chiho Kim | Will R. Gutekunst | Le Song | Rampi Ramprasad
Source: arXiv (Cornell University) (preprint, 2020-11-04)
Field: Materials Science — Materials Chemistry

Polymers for Extreme Conditions Designed Using Syntax-Directed\n Variational Autoencoders The design/discovery of new materials is highly non-trivial owing to the\nnear-infinite possibilities of material candidates, and multiple required\nproperty/performance objectives. Thus, machine learning tools are now commonly\nemployed to virtually screen material candidates with desired properties by\nlearning a theoretical mapping from material-to-property space, referred to as\nthe \\emph{forward} problem. However, this approach is inefficient, and severely\nconstrained by the candidates that human imagination can conceive. Thus, in\nthis work on polymers, we tackle the materials discovery challenge by solving\nthe \\emph{inverse} problem: directly generating candidates that satisfy desired\nproperty/performance objectives. We utilize syntax-directed variational\nautoencoders (VAE) in tandem with Gaussian process regression (GPR) models to\ndiscover polymers expected to be robust under three extreme conditions: (1)\nhigh temperatures, (2) high electric field, and (3) high temperature \\emph{and}\nhigh electric field, useful for critical structural, electrical and energy\nstorage applications. This approach to learn from (and augment) human ingenuity\nis general, and can be extended to discover polymers with other targeted\nproperties and performance measures.\n Ingenuity ; Property (philosophy) ; Computer science ; Syntax ; Field (mathematics) ; Artificial intelligence ; Gaussian process ; Process (computing) ; Space (punctuation) ; Theoretical computer science ; Algorithm ; Machine learning ; Gaussian ; Mathematics ; Programming language Machine Learning in Materials Science ; Machine Learning and Algorithms
Authors: Ignacio Pickering | Adrián E. Roitberg
Source:  (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Predicting low-lying excited state energies of organic molecules with an atomistic deep neural network potential Excited state ; Lying ; Organic molecules ; Artificial neural network ; Deep neural networks ; Molecule ; Chemical physics ; Statistical physics ; Physics ; Atomic physics ; Materials science ; Artificial intelligence ; Computer science ; Quantum mechanics ; Medicine Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Dario Caramelli | Jarosław M. Granda | S. Hessam M. Mehr | Dario Cambié | Alon Henson | Leroy Cronin
Source: ChemRxiv (preprint, 2020-09-08)
Field: Materials Science — Materials Chemistry

A Reactivity First Approach to Autonomous Discovery of New Chemistry &lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;We present a robotic chemical discovery system capable of learning the generalized notion of reactivity using a neural network model that can abstract the reactivity from the identity of the reagents. The system is controlled using an algorithm that works in conjunction with this learned knowledge, the robot was able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown datasets, regardless the identity of the starting materials. The system identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, but also some unpredictable reactions that yielded new molecules. The search was done within a budget of 15 inputs combined in 1018 reactions, which allowed us not only to discover a new photochemical reaction, but also a new reactivity mode for a well-known reagent (&lt;i&gt;p&lt;/i&gt;-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of six equivalents of TosMIC in a ‘multi-step, single-substrate’ cascade reaction yielding a trimeric product in high yield (47% unoptimized) with formation of five new C-C bonds involving &lt;i&gt;sp&lt;/i&gt;-&lt;i&gt;sp&lt;sup&gt;2&lt;/sup&gt;&lt;/i&gt; and &lt;i&gt;sp&lt;/i&gt;-&lt;i&gt;sp&lt;sup&gt;3&lt;/sup&gt;&lt;/i&gt; carbon centres. Analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.&lt;/p&gt;&lt;br&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt; Reactivity (psychology) ; Chemistry ; Reagent ; Yield (engineering) ; Substrate (aquarium) ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry ; Biology ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Anjana Samarakoon | D. M. Tennant
Source: arXiv (Cornell University) (preprint, 2020-11-11)
Field: Materials Science — Materials Chemistry

Machine Learning for Magnetic Phase Diagrams and Inverse Scattering\n Problems Machine learning promises to deliver powerful new approaches to neutron\nscattering from magnetic materials. Large scale simulations provide the means\nto realise this with approaches including spin-wave, Landau Lifshitz, and Monte\nCarlo methods. These approaches are shown to be effective at simulating\nmagnetic structures and dynamics in a wide range of materials. Using large\nnumbers of simulations the effectiveness of machine learning approaches are\nassessed. Principal component analysis and nonlinear autoencoders are\nconsidered with the latter found to provide a high degree of compression and to\nbe highly suited to neutron scattering problems. Agglomerative heirarchical\nclustering in the latent space is shown to be effective at extracting phase\ndiagrams of behavior and features in an automated way that aid understanding\nand interpretation. The autoencoders are also well suited to optimizing model\nparameters and were found to be highly advantageous over conventional fitting\napproaches including being tolerant of artifacts in untreated data. The\npotential of machine learning to automate complex data analysis tasks including\nthe inversion of neutron scattering data into models and the processing of\nlarge volumes of multidimensional data is assessed. Directions for future\ndevelopments are considered and machine learning argued to have high potential\nfor impact on neutron science generally.\n Neutron scattering ; Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Monte Carlo method ; Scattering ; Cluster analysis ; Physics ; Mathematics ; Optics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Topic Modeling
Authors: N.A. Koritsky | A.K. Fedorov | S.V. Solov'yov | A.K. Zvezdin
Source:  (article, 2020-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learning phase transitions in the ferrimagnetic GdFeCo alloy We present results on the identification of phase transitions in the ferrimagnetic GdFeCo alloy using machine learning. The approach for finding phase transitions in the system is based on the `learning by confusion' scheme, which allows one to characterize phase transitions using universal W -shape. By applying the `learning by confusion' scheme, we obtain 2D W -shaped surface that characterizes a triple phase transition point of the GdFeCo alloy. We demonstrate that our results are in the perfect agreement with the procedure of the numerical minimization of the thermodynamical potential, yet our machine-learning-based scheme has a potential to provide a speedup in the task of the phase transition identification. Ferrimagnetism ; Phase transition ; Phase (matter) ; Alloy ; Point (geometry) ; Minification Magnetic Properties and Applications ; Machine Learning in Materials Science ; Magnetic properties of thin films
Authors: Andrew C. Meng | Ke-Bin Low | Alexandre C. Foucher | Yuejin Li | Ivan Petrović | Eric A. Stach
Source: ChemRxiv (preprint, 2020-10-14)
Field: Materials Science — Materials Chemistry

Anomalous Metal Vaporization from Pt/Pd/Al2O3 Under Redox Conditions &lt;p&gt;&lt;a&gt;Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;-supported Pt/Pd bimetallic catalysts were studied using &lt;i&gt;in-situ&lt;/i&gt; atmospheric pressure and &lt;i&gt;ex-situ&lt;/i&gt; transmission electron microscopy.&lt;/a&gt; Real-time observation during separate oxidation and reduction processes provides nanometer-scale structural details – both morphology and chemistry – of supported Pt/Pd particles at intermediate states not observable through typical &lt;i&gt;ex-situ&lt;/i&gt; experiments. Significant metal vaporization was observed at temperatures above 600°C, both in pure oxygen and in air. This behavior implies that material transport through the vapor during typical catalyst aging processes for oxidation can play a more significant role in catalyst structural evolution than previously thought. Concomitantly, Pd diffusion away from metallic nanoparticles on the surface of Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; can also contribute to the disappearance of metal particles. Electron micrographs from &lt;i&gt;in-situ&lt;/i&gt; oxidation experiments were mined for data, including particle number, size, and aspect ratio using machine learning image segmentation. Under oxidizing conditions, we observe not only a decrease in the number of metal particles but also a decrease in the surface area to volume ratio. Some of the metal that diffuses away from particles on the oxide support can be regenerated and reappears back on the catalyst support surface under reducing conditions. These observations represent a first step towards understanding how rapid cycling between oxidative and reductive catalytic operating conditions affects catalyst structure.&lt;/p&gt; Bimetallic strip ; Catalysis ; Metal ; Oxidizing agent ; Chemistry ; Redox ; Transmission electron microscopy ; Vaporization ; Materials science ; Nanotechnology ; Chemical engineering ; Inorganic chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Farhad Gharagheizi | Dai Tang | David S. Sholl
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals Industrial separations of near-azeotropic chemicals, species with very similar boiling points, are energy- and capital-intensive. Adsorption-based processes can energy-efficiently separate near-azeotropic mixtures provided suitable adsorbent materials can be found. Among the full diversity of industry-relevant molecules, millions of these mixtures exist, meaning that discovery of mixture-specific adsorbents by direct experiment is infeasible. We show that vast numbers of adsorbents and adsorbing molecules can be explored in a powerful way by coupling atomistic simulations with machine learning. This concept is demonstrated by describing the adsorption of ∼54 000 industry-relevant chemicals in an experimentally derived set of thousands of metal–organic framework materials. Our results identify thousands of near-azeotropic mixtures that can be efficiently separated using adsorption and open possibilities for creating adsorption processes for complex mixtures with many components. Adsorption ; Chemistry ; Boiling ; Boiling point ; Set (abstract data type) ; Process engineering ; Biochemical engineering ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Michael G. Taylor | Tzuhsiung Yang | Sean Lin | Aditya Nandy | Jon Paul Janet | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-02-20)
Field: Materials Science — Materials Chemistry

Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions Determination of ground-state spins of open-shell transition metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2,000 unique mononuclear Fe(II)/Fe(III) transition metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally-observed temperature-dependent geometric structure changes, by correctly assigning almost all (> 95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model. Spins ; Ground state ; Heuristics ; Electronic structure ; Density functional theory ; Artificial neural network ; Spin states ; Bond length ; Chemistry ; Spin (aerodynamics) ; Artificial intelligence ; Computer science ; Machine learning ; Statistical physics ; Materials science ; Physics ; Condensed matter physics ; Computational chemistry ; Atomic physics ; Thermodynamics ; Crystallography ; Crystal structure Machine Learning in Materials Science ; Magnetism in coordination complexes ; Electrocatalysts for Energy Conversion
Authors: Pavan Inguva | Lachlan Mason | Indranil Pan | Miselle Hengardi | Omar K. Matar
Source: Data-Centric Engineering (preprint, 2020-01-01)
Field: Materials Science — Materials Chemistry

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation Abstract Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy. Cluster analysis ; Component (thermodynamics) ; Principal component analysis ; Computer science ; Dimensionality reduction ; Autoencoder ; Artificial intelligence ; Polymer ; Machine learning ; Materials science ; Biological system ; Artificial neural network Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Data Visualization and Analytics
Authors: Victorien Delannée | Marc C. Nicklaus
Source: Research Square (preprint, 2020-09-28)
Field: Materials Science — Materials Chemistry

ReactionCode: Format for Reaction Searching, Analysis, Classification, Transform, and Encoding/Decoding Abstract In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts ha ve been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language. Decoding methods ; Encoding (memory) ; Computer science ; Information retrieval ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Yury Suleymanov
Source: Science (article, 2020-06-11)
Field: Materials Science — Materials Chemistry

Learning macro from micro Machine Learning Determining atomic structural correlations in condensed-phase systems is crucial for understanding material properties and their behavior at the macroscale. It represents one of the central challenges in modern statistical mechanics because of the complex collective behavior emerging from microscopic many-body interactions. Using two classical condensed-phase models, a Lennard-Jones system and a hard-sphere fluid, Craven et al. show that machine learning methods trained on a set of optimally short molecular dynamics simulations can predict radial distribution functions with increased accuracy by an order of magnitude or even greater compared with traditional analytical approaches. The proposed methodology is general and could be applied more broadly across diverse condensed-phase systems. J. Phys. Chem. Lett. 10.1021/acs.jpclett.0c00627 (2020). Macro ; Computer science ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Olexandr Isayev | Corey Oses | Eric Gossett | Alexander Tropsha | Cormac Toher | Stefano Curtarolo
Source: UNC Libraries (article, 2020-04-18)
Field: Materials Science — Materials Chemistry

Universal fragment descriptors for predicting properties of inorganic crystals Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules. Fragment (logic) ; Chemistry ; Crystallography ; Materials science ; Biological system ; Mineralogy ; Mathematics ; Algorithm ; Biology X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Victorien Delannée | Marc C. Nicklaus
Source: ChemRxiv (preprint, 2020-04-03)
Field: Chemistry — Spectroscopy

ReactionCode: a new versatile format for searching, analysis, classification, transform, and encoding/decoding of reactions In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language. Identifier ; Computer science ; Decoding methods ; Context (archaeology) ; ENCODE ; Encoding (memory) ; Representation (politics) ; Graph ; Information retrieval ; Theoretical computer science ; Artificial intelligence ; Programming language ; Algorithm ; Chemistry Analytical Chemistry and Chromatography ; Machine Learning in Materials Science ; Computational Drug Discovery Methods