jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Somesh Mohapatra | Joyce An | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2021-03-03)
Field: Materials Science — Materials Chemistry

GLAMOUR: Graph Learning over Macromolecule Representations The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed GLAMOUR, a framework for chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Macromolecule ; Representation (politics) ; Graph ; Monomer ; Similarity (geometry) ; Computer science ; Variety (cybernetics) ; Artificial intelligence ; Range (aeronautics) ; Chemistry ; Polymer ; Polymer science ; Theoretical computer science ; Materials science ; Biochemistry ; Organic chemistry ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Hyunsoo Park | Yeonghun Kang | Wonyoung Choe | Jihan Kim
Source: arXiv (Cornell University) (preprint, 2021-08-31)
Field: Materials Science — Materials Chemistry

Mining Insights on Metal-Organic Framework Synthesis from Scientific Literature Texts Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. Trial-and-error approach that relies on a chemist's intuition and knowledge has limitations in efficiency due to the large MOF synthesis space. To this end, 47,187 number of MOF were data mined using our in-house developed code to extract their synthesis information in 28,565 MOF papers. The joint machine learning/rule-based algorithm yields an average F1 score of 90.3 % across different synthesis parameters (i.e. metal precursors, organic precursors, solvents, temperature, time, composition). From this data set, a PU learning algorithm was developed to predict synthesis of a given MOF material using synthesis conditions as inputs, and this algorithm successfully predicted successful synthesis in 83.1 % of the synthesized data in the test set. Finally, our model correctly predicted three amorphous MOFs (with their representative experimental synthesis condition) as having low synthesizability scores while the counterpart crystalline MOFs showed high synthesizability scores. Our results show that big data extracted from the texts of MOF papers can be used to rationally predict synthesis conditions for these materials, which can accelerate the speed in which new MOFs are synthesized. Bottleneck ; Intuition ; Computer science ; Metal-organic framework ; Organic synthesis ; Chemical space ; Algorithm ; Artificial intelligence ; Data mining ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Maxime Ducamp | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2021-11-17)
Field: Chemistry — Inorganic Chemistry

Prediction of Thermal Properties of Zeolites through Machine Learning The use of machine learning for the prediction of physical and chemical properties of crystals based on their structure alone is currently an area of intense research in computational materials science. In this work, we studied the possibility of using machine learning-trained algorithms in order to calculate the thermal properties of siliceous zeolite frameworks. We used as training data the thermal properties of 134 zeolites, calculated at the DFT level, in the quasi-harmonic approximation. We compared the statistical accuracy of trained models (based on the gradient boosting regression technique) using different types of descriptors, including ad hoc geometrical features, topology, pore space, and general geometric descriptors. While geometric descriptors were found to perform best, we also identified limitations on the accuracy of the predictions, especially for a small group of materials with very highly negative thermal expansion coefficients. We then studied the generalizability of the technique, demonstrating that the predictions were not sensitive to the refinement of framework structures at a high level of theory. Therefore, the models are suitable for the exploration and screening of large-scale databases of hypothetical frameworks, which we illustrate on the PCOD2 database of zeolites containing around 600,000 hypothetical structures. Generalizability theory ; Machine learning ; Computer science ; Artificial intelligence ; Boosting (machine learning) ; Gradient boosting ; Harmonic mean ; Thermal ; Algorithm ; Mathematics ; Geometry ; Thermodynamics ; Physics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jesus Sanchez-Juarez | Marissa Granados-Baez | Alberto A. Aguilar‐Lasserre | Jaime Cárdenas
Source: Conference on Lasers and Electro-Optics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Intelligent Decision Support System (IDSS) to Optimize 2D Materials Detection Using Digital Image Processing and Deep Learning We develop intelligent algorithm to detect Monolayers of WSe 2 , MoS 2 and h-BN autonomously using Digital Image Processing and Deep Learning with 100% accuracy, avoiding any additional characterization techniques such as photoluminescence or Raman. Computer science ; Artificial intelligence ; Image processing ; Digital image processing ; Deep learning ; Characterization (materials science) ; Computer vision ; Digital image ; Digital signal processing ; Image (mathematics) ; Materials science ; Computer hardware ; Nanotechnology 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Pin-Wen Guan
Source: arXiv (Cornell University) (preprint, 2021-02-21)
Field: Materials Science — Materials Chemistry

Differentiable thermodynamic modeling A new framework of thermodynamic modeling is proposed by introducing the concept of differentiable programming, where all the thermodynamic observables including both thermochemical quantities and phase equilibria can be differentiated with respect to the underlying model parameters, thus allowing the models learned by gradient-based optimization. It is shown that thermodynamic modeling and deep learning can be seamlessly integrated and unified within this framework. A preliminary successful application is demonstrated for the Cu-Rh system. It is expected that thermodynamic modeling in a deep learning style can increase prediction power of models, and provide more effective guidance for design, synthesis and optimization of multi-component materials with complex chemistry via learning various types of data. Differentiable function ; Component (thermodynamics) ; Computer science ; Observable ; Automatic differentiation ; Thermodynamic system ; Artificial intelligence ; Thermodynamics ; Mathematics ; Algorithm ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Xiang Li | C. Fan | Weiluo Ren | Ji Chen
Source: arXiv (Cornell University) (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Fermionic Neural Network with Effective Core Potential Deep learning techniques have opened a new venue for electronic structure theory in recent years. In contrast to traditional methods, deep neural networks provide much more expressive and flexible wave function ansatz, resulting in better accuracy and time scaling behavior. In order to study larger systems while retaining sufficient accuracy, we integrate a powerful neural-network based model (FermiNet) with the effective core potential method, which helps to reduce the complexity of the problem by replacing inner core electrons with additional semi-local potential terms in Hamiltonian. In this work, we calculate the ground state energy of 3d transition metal atoms and their monoxide which are quite challenging for original FermiNet work, and the results are in good consistency with both experimental data and other state-of-the-art computational methods. Our development is an important step for a broader application of deep learning in the electronic structure calculation of molecules and materials. Ansatz ; Artificial neural network ; Computer science ; Hamiltonian (control theory) ; Deep learning ; Artificial intelligence ; Electronic structure ; Wave function ; Core (optical fiber) ; Statistical physics ; Theoretical computer science ; Physics ; Mathematical optimization ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Advanced Chemical Physics Studies
Authors: Elif Sorkun | Qi Zhang | Abhishek Khetan | murat cihan sorkun | Süleyman Er
Source: ChemRxiv (preprint, 2021-04-12)
Field: Chemistry — Electrochemistry

RedDB, a Computational Database of Electroactive Molecules for Aqueous Redox Flow Batteries An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,677 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: i)chemical library generation, ii) molecular property prediction based on quantum chemical calculations, iii) aqueous solubility prediction using machine learning, and iv) data processing and database creation, have been described. Chemical space ; Redox ; Computer science ; Virtual screening ; Quantum chemical ; Aqueous solution ; Database ; Property (philosophy) ; Molecule ; Chemical database ; Chemistry ; Combinatorial chemistry ; Drug discovery ; Organic chemistry Electrochemical Analysis and Applications ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Omprakash Yadav | Ankit Chhonkar | Rahul Sharma | Sandeep Chauhan | Ashish Bansal | Navneet Singh Baghel | Himanshu Sharma | Devesh K. Sharma
Source: Smart innovation, systems and technologies (review, 2021-08-30)
Field: Materials Science — Materials Chemistry

Experimental Study of Parameter Affecting the Thermal Conductivity of Composite Materials and Alloy: A Review Thermal conductivity ; Materials science ; Composite material ; Composite number ; Alloy ; Particle (ecology) ; Work (physics) ; Mechanical engineering Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Heat Transfer and Optimization
Authors: Tom Young | Tristan Johnston-Wood | Volker L. Deringer | Fernanda Duarte
Source: ChemRxiv (preprint, 2021-02-16)
Field: Materials Science — Materials Chemistry

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields &lt;p&gt;Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (S&lt;sub&gt;N&lt;/sub&gt;2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems. &lt;/p&gt; Force field (fiction) ; Molecular dynamics ; Metric (unit) ; Computer science ; Statistical physics ; Field (mathematics) ; Gaussian ; Potential energy ; Gaussian process ; Chemistry ; Chemical physics ; Nanotechnology ; Artificial intelligence ; Physics ; Computational chemistry ; Materials science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Shree Hari Sureshbabu | Manas Sajjan | Sangchul Oh | Sabre Kais
Source: Journal of Chemical Information and Modeling (preprint, 2021-06-16)
Field: Materials Science — Materials Chemistry

Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices Quantum machine learning algorithms, the extensions of machine learning to quantum regimes, are believed to be more powerful as they leverage the power of quantum properties. Quantum machine learning methods have been employed to solve quantum many-body systems and have demonstrated accurate electronic structure calculations of lattice models, molecular systems, and recently periodic systems. A hybrid approach using restricted Boltzmann machines and a quantum algorithm to obtain the probability distribution that can be optimized classically is a promising method due to its efficiency and ease of implementation. Here, we implement the benchmark test of the hybrid quantum machine learning on the IBM-Q quantum computer to calculate the electronic structure of typical two-dimensional crystal structures: hexagonal-boron nitride and graphene. The band structures of these systems calculated using the hybrid quantum machine learning approach are in good agreement with those obtained by the conventional electronic structure calculations. This benchmark result implies that the hybrid quantum machine learning method, empowered by quantum computers, could provide a new way of calculating the electronic structures of quantum many-body systems. Quantum algorithm ; Quantum computer ; Computer science ; Quantum ; Quantum machine learning ; Electronic structure ; Quantum simulator ; Quantum network ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: C. Huy Pham | Rebecca Lindsey | Laurence E. Fried | Nir Goldman
Source: ChemRxiv (preprint, 2021-11-12)
Field: Materials Science — Materials Chemistry

High Accuracy Semi-Empirical Quantum Models Based on a Reduced Training Set There exists a great need for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories while exhibiting a wide degree of transferability. In this regard, we have leveraged a machine-learned force field based on Chebyshev polynomials to determine Density Functional Tight Binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential (ANI- 1x). In addition, the total number of data points in our training set is less than one half of that used for a recent DFTB-neural network model (trained on a separate dataset). Validation tests of our DFTB model against energy and vibrational data for gas-phase molecules for additional quantum datasets shows strong agreement with reference data from either hybrid density-functional theory, coupled-cluster calculations, or experiments. Preliminary testing on graphite and diamond successfully reproduce condensed phase structures. The models developed in this work, in principle, can retain most of the accuracy of quantum-based methods at any level of theory with relatively small training sets. Our efforts can thus allow for high throughput physical and chemical predictions with up to coupled-cluster accuracy for materials that are computationally intractable with standard approaches. Computer science ; Quantum ; Density functional theory ; Set (abstract data type) ; Range (aeronautics) ; Coupled cluster ; Cluster (spacecraft) ; Algorithm ; Statistical physics ; Data mining ; Physics ; Quantum mechanics ; Molecule ; Materials science Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum, superfluid, helium dynamics
Authors: Jan Weinreich | Martín Leandro Paleico | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2021-03-25)
Field: Materials Science — Materials Chemistry

Properties of {\alpha}-Brass Nanoparticles II: Structure and Composition Nanoparticles have become increasingly interesting for a wide range of\napplications, because in principle it is possible to tailor their properties by\ncontrolling size, shape and composition. One of these applications is\nheterogeneous catalysis, and a fundamental understanding of the structural\ndetails of the nanoparticles is essential for any knowledge-based improvement\nof reactivity and selectivity. In this work we investigate the atomic structure\nof brass nanoparticles containing up to 5000 atoms as a typical example for a\nbinary alloy consisting of Cu and Zn. As systems of this size are too large for\nelectronic structure calculations, in our simulations we use a recently\nparametrized machine learning potential providing close to density functional\ntheory accuracy. This potential is employed for a structural characterization\nas a function of chemical composition by various types of simulations like\nMonte Carlo in the Semi-Grand Canonical Ensemble and simulated annealing\nmolecular dynamics. Our analysis reveals that the distribution of both elements\nin the nanoparticles is inhomogeneous, and zinc accumulates in the outermost\nlayer, while the first subsurface layer shows an enrichment of copper. Only for\nhigh zinc concentrations alloying can be found in the interior of the\nnanoparticles, and regular patterns corresponding to crystalline bulk phases of\n$\\alpha$-brass can then be observed. The surfaces of the investigated clusters\nexhibit well-ordered single-crystal facets, which can give rise to grain\nboundaries inside the clusters. The melting temperature of the nanoparticles is\nfound to decrease with increasing zinc-atom fraction, a trend which is\nwell-known also for the bulk phase diagram of brass.\n Nanoparticle ; Materials science ; Brass ; Molecular dynamics ; Annealing (glass) ; Chemical physics ; Alloy ; Copper ; Nanotechnology ; Computational chemistry ; Chemistry ; Metallurgy Nanoporous metals and alloys ; Quasicrystal Structures and Properties ; nanoparticles nucleation surface interactions
Authors: Jialong Guo | Zhiwei Liu | Zongguo Wang | Yuhang Hu | Jue Wang | Yangang Wang
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

A Genetic Algorithm-Based Artificial Network Method for Material Feature Recombination With the implementation of the material genome initiative and continuous improvement of computing power, the accumulation of material data promotes the development of the fourth paradigm of scientific research. At the same time, the machine learning method to accelerate material design has also been widely used in material science. For the application of machine learning in materials science, feature description and model selection are two important aspects to be taken seriously, and to get the appropriate features and models is not only the hot point but also the difficulty of research in recent years. Obtaining optimal feature description by simple inputs will be a useful work for material research. In this work, a feature recombination method is proposed. The input feature will be reorganized by combining neural network and genetic algorithm, and the reasonable feature description to improve the accuracy of prediction will be obtained. The structure-bandgap relationship of ABX3 perovskite structure is predicted by the feature recombination method proposed in this study. The results show that the feature recombination method improves the prediction ability and accuracy of the model. The R2 score in test set has been improved by 0.085 in the experiment. Feature (linguistics) ; Feature selection ; Computer science ; Genetic algorithm ; Artificial neural network ; Artificial intelligence ; Machine learning ; Algorithm ; Data mining ; Pattern recognition (psychology) Machine Learning in Materials Science ; Big Data and Digital Economy ; Advanced Memory and Neural Computing
Authors: Kai Zhou | Gergely Endrődi | Long-Gang Pang | Horst Stoecker
Source:  (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Generative Model Study for 1+1d-Complex Scalar Field Theory We reported a recent work that applies modern Deep Learning (convolutional neural network) techniques in the context of two dimensional lattice complex scalar field theory, which has a non-trivial phase diagram at nonzero temperature and chemical potential. Especially we introuced the field configuration production with generative adversarial network (GAN), where the GAN is showed to be able to automatically capture the implicit local constraint for the physical configurations and also the underlying physical distribution. We further explored generalize the configuration production at different parameter space using conditional GAN. Generative grammar ; Scalar field ; Computer science ; Scalar (mathematics) ; Generative adversarial network ; Convolutional neural network ; Lattice (music) ; Field (mathematics) ; Context (archaeology) ; Scalar field theory ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Deep learning ; Mathematics ; Physics ; Pure mathematics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Computational and Text Analysis Methods ; Scientific Computing and Data Management