jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengjie Liu | Alon Grinberg Dana | Matthew S. Johnson | Mark J. Goldman | Agnes Jocher | A. Mark Payne | Colin A. Grambow | Kehang Han | Nathan W. Yee | Emily Mazeau | Katrín Blöndal | Richard H. West | Franklin Goldsmith | William H. Green
Source: ChemRxiv (preprint, 2020-12-28)
Field: Materials Science — Materials Chemistry

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. One important change is that RMG v3.0 is now Python 3 compatible, which supports the most up-to-date versions of cheminformatics and machine learning packages that RMG depends on. Additionally, RMG can now generate heterogeneous catalysis models, in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release also includes parallelization for reaction generation and on-the-fly quantum calculations, and a new molecule isomorphism approach to improve computational performance. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems. Cheminformatics ; Python (programming language) ; Computer science ; Reaction mechanism ; Chemistry ; Computational science ; Computational chemistry ; Catalysis ; Programming language Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Marcos del Cueto | Xueyao Zhou | Linsen Zhou | Yaolong Zhang | Bin Jiang | Hua Guo
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

New Perspectives on CO₂–Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface CO oxidation on transition metal surfaces is not only a prototype for studying surface chemistry but also of critical importance in applications such as pollution control and fuel cells. The reverse process, the dissociation of CO₂, is also key in the sequestration of this greenhouse gas. However, our understanding of the dynamics involved in these processes is still incomplete. Theoretical studies of surface dynamics have so far been largely hindered by the high computational costs of on-the-fly calculations. To overcome this bottleneck, we report here a high-dimensional potential energy surface (PES) for both the dissociative chemisorption and recombinative desorption of CO₂ on Pt(111), by using a machine learning method. Trained with a large number of density functional points in a large configuration space, the multipurpose neural network PES accurately reproduces the geometry and energy of the stationary points along the CO₂ reaction path on Pt(111), as well as the dynamical results obtained using the ab initio molecular dynamics method. In addition, we propose a new perspective on the chemical shape of the surface, which reveals the site specificity of the chemical barrier. This approach opens the door to accurate studies of these relevant reactions on surfaces, with a low computational cost, granting access to a more in-depth description of the chemical processes taking place in these systems. Chemistry ; Chemisorption ; Bottleneck ; Potential energy surface ; Chemical physics ; Dissociation (chemistry) ; Reaction dynamics ; Desorption ; Chemical reaction ; Ab initio ; Density functional theory ; Molecular dynamics ; Artificial neural network ; Nanotechnology ; Computational chemistry ; Molecule ; Physical chemistry ; Artificial intelligence ; Computer science ; Adsorption ; Materials science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies
Authors: K. Zberecki
Source: Computational Materials Science (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

Structure prediction of two-dimensional materials based on neural network-driven evolutionary technique Simple (philosophy) ; Artificial neural network ; Computer science ; Biological system ; Substrate (aquarium) ; Network structure ; Point (geometry) ; Stoichiometry ; Artificial intelligence ; Evolutionary algorithm ; Algorithm ; Materials science ; Theoretical computer science ; Mathematics ; Chemistry ; Biology ; Geometry ; Ecology 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Marc Philipp Bahlke | Natnael Mogos | Jonny Proppe | Carmen Herrmann
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

Exchange Spin Coupling from Gaussian Process Regression Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. Kriging ; Extrapolation ; Artificial intelligence ; Machine learning ; Computer science ; Dipole ; Kernel method ; Determinantal point process ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics ; Support vector machine ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Annika Stuke | Christian Künkel | Dorothea Golze | Milica Todorović | Johannes T. Margraf | Karsten Reuter | Patrick Rinke | Harald Oberhofer
Source: Scientific Data (preprint, 2020-02-18)
Field: Materials Science — Materials Chemistry

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62 k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62 k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the G<sub>0</sub>W<sub>0</sub> approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))). Density functional theory ; Molecule ; van der Waals force ; Perturbation theory (quantum mechanics) ; Physics ; Quasiparticle ; Eigenvalues and eigenvectors ; Molecular orbital ; Statistical physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Conor D. Rankine | Marwah Madkhali | Thomas J. Penfold
Source: ChemRxiv (preprint, 2020-04-28)
Field: Materials Science — Materials Chemistry

A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra &lt;p&gt;X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. &lt;i&gt;in operando&lt;/i&gt; catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less &lt;/p&gt;&lt;p&gt;than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus.&lt;/p&gt; Artificial neural network ; Absorption (acoustics) ; Focus (optics) ; Enhanced Data Rates for GSM Evolution ; Computer science ; Spectral line ; Absorption spectroscopy ; Deep learning ; Artificial intelligence ; X-ray absorption spectroscopy ; Physics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Taishan Zhu | Sheng Gong | Tian Xie | Prashun Gorai | Jeffrey C. Grossman
Source: arXiv (Cornell University) (preprint, 2020-06-21)
Field: Materials Science — Materials Chemistry

Charting Lattice Thermal Conductivity of Inorganic Crystals Thermal conductivity is a fundamental material property but challenging to predict, with less than 5% out of about $10^5$ synthesized inorganic materials being documented. In this work, we extract the structural chemistry that governs lattice thermal conductivity, by combining graph neural networks and random forest approaches. We show that both mean and variation of unit-cell configurational properties, such as atomic volume and bond length, are the most important features, followed by mass and elemental electronegativity. We chart the structural chemistry of lattice thermal conductivity into extended van-Arkel triangles, and predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database. For the latter, we develop a transfer learning framework extendable for other applications. Thermal conductivity ; Crystal structure ; Lattice (music) ; Materials science ; Electronegativity ; Conductivity ; Thermal ; Chemical physics ; Thermodynamics ; Chemistry ; Crystallography ; Physical chemistry ; Physics ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Masashi Tsubaki | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

On the equivalence of molecular graph convolution and molecular wave function with poor basis set In this study, we demonstrate that the linear combination of atomic orbitals (LCAO), an approximation of quantum physics introduced by Pauling and Lennard-Jones in the 1920s, corresponds to graph convolutional networks (GCNs) for molecules. However, GCNs involve unnecessary nonlinearity and deep architecture. We also verify that molecular GCNs are based on a poor basis function set compared with the standard one used in theoretical calculations or quantum chemical simulations. From these observations, we describe the quantum deep field (QDF), a machine learning (ML) model based on an underlying quantum physics, in particular the density functional theory (DFT). We believe that the QDF model can be easily understood because it can be regarded as a single linear layer GCN. Moreover, it uses two vanilla feedforward neural networks to learn an energy functional and a Hohenberg--Kohn map that have nonlinearities inherent in quantum physics and the DFT. For molecular energy prediction tasks, we demonstrated the viability of an ``extrapolation,'' in which we trained a QDF model with small molecules, tested it with large molecules, and achieved high extrapolation performance. This will lead to reliable and practical applications for discovering effective materials. The implementation is available at https://github.com/masashitsubaki/QuantumDeepField_molecule. Extrapolation ; Basis set ; Atomic orbital ; Wave function ; Computer science ; Quantum ; Statistical physics ; Deep learning ; Density functional theory ; Theoretical computer science ; Quantum mechanics ; Physics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Manish Shetty | M. Alexander Ardagh | Yutong Pang | Omar Abdelrahman | Paul J. Dauenhauer
Source: ChemRxiv (preprint, 2020-04-16)
Field: Materials Science — Materials Chemistry

Electric-Field Assisted Modulation of Surface Thermochemistry Conventional catalyst design has enhanced reactivity and product selectivity through control of surface thermochemistry by tunable surface composition and the surrounding environment (e.g., pore structure). In this work, the prospect for electric field towards controlling product selectivity and reaction networks on the Pt(111) surface was evaluated with periodic density functional theory (DFT) calculations in concert with machine learning (ML) algorithms. Linear scaling relationships (LSRs) for adsorption energies of surface species in electric field were shown to: (i) be distinct as compared to zero-field LSRs across metals, and (ii) linearly correlate with adsorption energies of H* rather than the binding element. The slope of LSRs linearly correlated with the zero-field dipole moment. A random forest ML regression algorithm predicted field-dependent adsorption energies with a mean absolute error (0.12 eV) comparable to DFT. Overall, this study identifies the path forward for electric field-assisted catalysis, specifically towards catalyst poisoning, product selectivity, and control of reaction pathways. Thermochemistry ; Electric field ; Selectivity ; Dipole ; Adsorption ; Catalysis ; Field (mathematics) ; Density functional theory ; Chemistry ; Materials science ; Physical chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Harshdeep Singh | Nicholas McCarthy | Qurrat Ul Ain | J.F. Hayes
Source: arXiv (Cornell University) (preprint, 2020-09-29)
Field: Materials Science — Materials Chemistry

ChemoVerse: Manifold traversal of latent spaces for novel molecule discovery In order to design a more potent and effective chemical entity, it is essential to identify molecular structures with the desired chemical properties. Recent advances in generative models using neural networks and machine learning are being widely used by many emerging startups and researchers in this domain to design virtual libraries of drug-like compounds. Although these models can help a scientist to produce novel molecular structures rapidly, the challenge still exists in the intelligent exploration of the latent spaces of generative models, thereby reducing the randomness in the generative procedure. In this work we present a manifold traversal with heuristic search to explore the latent chemical space. Different heuristics and scores such as the Tanimoto coefficient, synthetic accessibility, binding activity, and QED drug-likeness can be incorporated to increase the validity and proximity for desired molecular properties of the generated molecules. For evaluating the manifold traversal exploration, we produce the latent chemical space using various generative models such as grammar variational autoencoders (with and without attention) as they deal with the randomized generation and validity of compounds. With this novel traversal method, we are able to find more unseen compounds and more specific regions to mine in the latent space. Finally, these components are brought together in a simple platform allowing users to perform search, visualization and selection of novel generated compounds. Tree traversal ; Chemical space ; Computer science ; Generative model ; Artificial intelligence ; Generative grammar ; Heuristic ; Theoretical computer science ; Machine learning ; Space (punctuation) ; Drug discovery ; Algorithm ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Madhava Gaikwad | Ashwini R Doke
Source:  (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Featureless approach for predicting Critical Temperature of Superconductors Superconductive materials holds key for fully functional extreme ultra reliable low latency communication. The cost effectiveness of latter technology depends upon ability to create and manufacture materials exhibiting superconductivity at high temperature. In this paper we estimate a data driven machine learning approach to predict critical temperature of superconductor without extracting the feature from superconductors chemical formula. Yet All other research is focusing on generating features from molecular formula and then using them for prediction. our approach is unique, straightforward, featureless and simple. Our approach demonstrates the accuracy of different machine learning algorithms applied on dataset which contains chemical formulas of superconductor. This approach only predicts the critical temperature of superconductor it will not give information about material is superconductor or not. We have provided source code so further experiments can be carried out. Superconductivity ; Key (lock) ; Computer science ; High-temperature superconductivity ; Latency (audio) ; Feature (linguistics) ; Code (set theory) ; Source code ; Simple (philosophy) ; Artificial intelligence ; Statistical physics ; Physics ; Algorithm ; Materials science ; Machine learning ; Condensed matter physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Benjamin A. Helfrecht | Rose K. Cersonsky | Guillaume Fraux | Michele Ceriotti
Source: Machine Learning Science and Technology (preprint, 2020-07-28)
Field: Materials Science — Materials Chemistry

Structure-property maps with Kernel principal covariates regression Abstract Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression and can be used conveniently to reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we provide a pedagogic overview of these data analysis schemes, including the use of the kernel trick to introduce an element of non-linearity while maintaining most of the convenience and the simplicity of linear approaches. We then introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials. Principal component regression ; Kernel (algebra) ; Covariate ; Principal component analysis ; Computer science ; Property (philosophy) ; Kernel principal component analysis ; Regression ; Simple (philosophy) ; Linear regression ; Principal (computer security) ; Simplicity ; Artificial intelligence ; Kernel method ; Machine learning ; Data mining ; Mathematics ; Statistics ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Riya Karelova
Source: IOP Conference Series Materials Science and Engineering (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Possibilities of an artificial neural network use to control oxygen consumption in a converter shop Abstract Converter steelmaking process uses high-performance processing plants and a high level of process automation. Complex multi-stage processes typically use many factors that have effect both on individual stages of the processes and on how a combination of the processes runs as a whole. This makes it necessary to select and/or develop control methods for the converter process having no full current data on process parameters and effects of random disturbances. Use of artificial neural networks is the most effective method available now for analyzing and predicting parameters of different processes (including converter process). This article deals with an algorithm for oxygen measurement based on chemical analysis of steel and process history through some period, using an artificial neural network and machine learning. Initial data were reviewed using correlation and regression analysis, the artificial neural network architecture chosen is reasoned as the most appropriate for the objective on hand and the results are analyzed. The element measurement accuracy achieved is satisfactory, which makes the resulting artificial neural network usable in an automation system for testing purposes. Network performance can be improved with the help of experts in the subject area, whose expertise can be used to correct the model. Artificial neural network ; USable ; Process (computing) ; Automation ; Computer science ; Artificial intelligence ; Steelmaking ; Machine learning ; Process control ; Basic oxygen steelmaking ; Control engineering ; Engineering Advanced Materials and Semiconductor Technologies ; Economic and Technological Systems Analysis ; Metal Extraction and Bioleaching