jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Catalin D. Spataru | Matthew Witman | Reese E. Jones
Source: arXiv (Cornell University) (preprint, 2021-07-27)
Field: Materials Science — Materials Chemistry

Tuning the critical Li intercalation concentrations for MoX$_2$ bilayer phase transitions Transition metal dichalcogenides (TMDs), such as MoS$_2$, are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge-tunable ion-insertion materials that could be used in electro-chemical devices. In this work we study the phase stability and electronic structure of H and T$^\prime$ Li-intercalated MoX$_2$ bilayers with X=S, Se, or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches, we find that the H phase is more stable at low Li concentration for all X, and the critical Li concentration at which the T$^\prime\to$H transition occurs decreases with increasing mass of X. Furthermore the relative free energy of the two phases becomes less sensitive to Li insertion with increasing atomic mass of the chalcogen atom X. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a (positive) conductivity jump at the phase transition from H to T$^\prime$. Intercalation (chemistry) ; Materials science ; Conductivity ; Phase transition ; Phase (matter) ; Ion ; Bilayer ; Electronic structure ; Analytical Chemistry (journal) ; Chemistry ; Physical chemistry ; Thermodynamics ; Computational chemistry ; Inorganic chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Andreas Erlebach | Petr Nachtigall | Lukáš Grajciar
Source: arXiv (Cornell University) (preprint, 2021-02-24)
Field: Materials Science — Materials Chemistry

Accurate large-scale simulations of siliceous zeolites by neural network potentials The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computationally feasible protocol for identification of hypothetical zeolites that could be targeted experimentally is a great challenge. To tackle the challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain the accuracy of the DFT reference for thermodynamic stabilities, vibrational properties, and reactive and non-reactive phase transformations. The novel NNPs outperforms specialized, analytical force fields for silica, such as ReaxFF, by order(s) of magnitude in accuracy, while speeding up the calculations in comparison to DFT by at least three orders of magnitude. As a showcase, we screened an existing zeolite database containing 330 thousand structures and revealed more than 20 thousand additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of hypothetical and existing zeolites. ReaxFF ; Density functional theory ; Zeolite ; Artificial neural network ; Computer science ; Chemistry ; Computational chemistry ; Catalysis ; Machine learning ; Molecular dynamics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Benjamin W. Frazier | Steven M. Anlage
Source: arXiv (Cornell University) (preprint, 2021-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Future of Intelligent Wavefront Shaping for Smart Radio Environments As the electromagnetic spectrum becomes more congested and the environments in which we need to operate become more complicated, control over the environment itself becomes necessary to ensure the integrity of wireless communication channels. Wavefront shaping with programmable metasurfaces allows wave fields to be manipulated in both time and space, providing a method to interact with the environment. When coupled with deep learning, intelligent wavefront shaping serves as a catalyst, enabling smart radio environments and unlocking applications beyond traditional wireless communication networks. In this paper, we discuss the outlook of intelligent wavefront shaping for wave propagation in complex environments and highlight its transformative potential. Wavefront ; Wireless ; Computer science ; Transformative learning ; Telecommunications ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Indoor and Outdoor Localization Technologies ; Advanced Wireless Communication Technologies
Authors: Logan Ward | Ganesh Sivaraman | J. Gregory Pauloski | Yadu Babuji | Ryan Chard | Naveen K. Dandu | Paul C. Redfern | Rajeev S. Assary | Kyle Chard | Larry A. Curtiss | Rajeev Thakur | Ian Foster
Source: arXiv (Cornell University) (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use machine learning (ML) to create proxy models of simulations show particular promise for guiding ensembles but are challenging to deploy because of the need to coordinate dynamic mixes of simulation and learning tasks. We present Colmena, an open-source Python framework that allows users to steer campaigns by providing just the implementations of individual tasks plus the logic used to choose which tasks to execute when. Colmena handles task dispatch, results collation, ML model invocation, and ML model (re)training, using Parsl to execute tasks on HPC systems. We describe the design of Colmena and illustrate its capabilities by applying it to electrolyte design, where it both scales to 65536 CPUs and accelerates the discovery rate for high-performance molecules by a factor of 100 over unguided searches. Computer science ; Scalability ; Python (programming language) ; Implementation ; Machine learning ; Performance tuning ; Factor (programming language) ; Artificial intelligence ; Distributed computing ; Software engineering ; Programming language ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Parallel Computing and Optimization Techniques
Authors: Sheena Agarwal | Kavita Joshi | Samagra Agrawal
Source: ChemRxiv (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning Identifying factors that influence interactions at the surface is still an active area of research. In this study, we present the importance of analyzing bondlength activation, while interpreting Density Functional Theory (DFT) results, as yet another crucial indicator for catalytic activity. We studied theadsorption of small molecules, such as O 2 , N 2 , CO, and CO 2 , on seven face-centered cubic (fcc) transition metal surfaces (M = Ag, Au, Cu, Ir, Rh, Pt, and Pd) and their commonly studied facets (100, 110, and 111). Through our DFT investigations, we highlight the absence of linear correlation between adsorption energies (E ads ) and bondlength activation (BL act ). Our study indicates the importance of evaluating both to develop a better understanding of adsorption at surfaces. We also developed a Machine Learning (ML) model trained on simple periodic table properties to predict both, E ads and BL act . Our ML model gives an accuracy of Mean Absolute Error (MAE) ∼ 0.2 eV for E ads predictions and 0.02 Å for BL act predictions. The systematic study of the ML featuresthat affect E ads and BL act further reinforces the importance of looking beyond adsorption energies to get a full picture of surface interactions with DFT. Adsorption ; Density functional theory ; Chemistry ; Molecule ; Computational chemistry ; Physical chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Alexander Ryabov | Petr Zhilyaev
Source: arXiv (Cornell University) (preprint, 2021-12-09)
Field: Chemistry — Spectroscopy

Application of neural network for exchange-correlation functional interpolation Density functional theory (DFT) is one of the primary approaches to get a solution to the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in analytical form. Accordingly, the accuracy improvement of the DFT is mainly based on the development of XC functional approximations. Commonly, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there is no universal functional form to incorporate these data into XC functional. Various parameterizations use heuristic rules to build a specific XC functional. The neural network (NN) approach to interpolate the data from higher precision theories can give a unified path to parametrize an XC functional. Moreover, data from many existing quantum chemical databases could provide the XC functional with improved accuracy. In this work, we develop NN XC functional, which gives both exchange potential and exchange energy density. Proposed NN architecture consists of two parts NN-E and NN-V, which could be trained in separate ways, which adds additional flexibility. We also show the suitability of the developed NN XC functional in the self-consistent cycle when applied to atoms, molecules, and crystals. Density functional theory ; Hybrid functional ; Interpolation (computer graphics) ; Orbital-free density functional theory ; Energy functional ; Functional theory ; Flexibility (engineering) ; Artificial neural network ; Statistical physics ; Heuristic ; Algorithm ; Physics ; Computer science ; Mathematics ; Artificial intelligence ; Quantum mechanics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Guilherme Duarte de Barros | Juliana De Oliveira | Vitor Matheus Ferraz Pereira | Wesley da Silva Pereira | André Luiz Maciel Santana
Source: Anais do XII Computer on the Beach - COTB '21 (article, 2021-04-29)
Field: Materials Science — Biomaterials

Análise morfológica de nanofibras Uma abordagem por visão computacional e aprendizagem de máquina The studies and applications of nanofibers have grown over the years. It was observed that the properties of the nanometer-scale yarns present advantages in applications in several areas, such as biomedical, energy storage and production, and applications involving water filtration. These materials are synthesized through a technical process and, for that reason, they are subject to the presentation of failures. The most common flaws are the formation of granules and pores. With the evolution of computing, applications that use machine learning resources can assist in detecting these failures. This work aims to evaluate and compare two different approaches to morphological analysis to see losses in nanofibers. Firstly, a data set was created using a Scanning Electron Microscope. After that, each image was analyzed by ImageJ software and by RNA solution. As a hypothesis, the article will assess whether the beads identification and the number of beads by the analog method are statistically similar (H0) or statistically different (H1) from the machine learning method. The preliminary results indicate that for the group that used 100 images and computer visualization, the analog method's accuracy was 7.23%. In order to accuracy increase, another test with 150 more distinct images was done, bringing a new result of 55.09%. The analysis time was considerably less when performed by the computational method. It was possible to conclude that the computational approach does not have the beads identification statistically similar to the analog way concerning the methodology used. Therefore, rejected H0. However, the directly proportional relationship of accuracy with the number of samples suggests that training with more various images can calibrate the algorithm. Computer science ; Nanofiber ; Visualization ; Artificial intelligence ; Software ; Data presentation ; Process (computing) ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Electrospun Nanofibers in Biomedical Applications
Authors: Jaber Rezaei Mianroodi | Shahed Rezaei | Nima H. Siboni | Bai‐Xiang Xu | Dierk Raabe
Source: arXiv (Cornell University) (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Lossless Multi-Scale Constitutive Elastic Relations with Artificial\n Intelligence The elastic properties of materials derive from their electronic and atomic\nnature. However, simulating bulk materials fully at these scales is not\nfeasible, so that typically homogenized continuum descriptions are used\ninstead. A seamless and lossless transition of the constitutive description of\nthe elastic response of materials between these two scales has been so far\nelusive. Here we show how this problem can be overcome by using Artificial\nIntelligence (AI). A Convolutional Neural Network (CNN) model is trained, by\ntaking the structure image of a nanoporous material as input and the\ncorresponding elasticity tensor, calculated from Molecular Statics (MS), as\noutput. Trained with the atomistic data, the CNN model captures the size- and\npore-dependency of the material's elastic properties which, on the physics\nside, can stem from surfaces and non-local effects. Such effects are often\nignored in upscaling from atomistic to classical continuum theory. To\ndemonstrate the accuracy and the efficiency of the trained CNN model, a Finite\nElement Method (FEM) based result of an elastically deformed nanoporous beam\nequipped with the CNN as constitutive law is compared with that by a full\natomistic simulation. The good agreement between the atomistic simulations and\nthe FEM-AI combination for a system with size and surface effects establishes a\nnew lossless scale bridging approach to such problems. The trained CNN model\ndeviates from the atomistic result by 9.6\\% for porosity scenarios of up to\n90\\% but it is about 230 times faster than the MS calculation and does not\nrequire to change simulation methods between different scales. The efficiency\nof the CNN evaluation together with the preservation of important atomistic\neffects makes the trained model an effective atomistically-informed\nconstitutive model for macroscopic simulations of nanoporous materials and\nsolving of inverse problems.\n Constitutive equation ; Finite element method ; Convolutional neural network ; Continuum mechanics ; Lossless compression ; Computer science ; Nanoporous ; Statistical physics ; Artificial neural network ; Elasticity (physics) ; Continuum hypothesis ; Algorithm ; Artificial intelligence ; Mechanics ; Physics ; Materials science ; Classical mechanics ; Data compression ; Nanotechnology ; Thermodynamics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Composite Material Mechanics
Authors: Van-Quyen Nguyen | Viet-Cuong Nguyen | Nguyễn Tiến Cường | Tien-Lam Pham
Source: arXiv (Cornell University) (preprint, 2021-11-10)
Field: Materials Science — Materials Chemistry

Pairwise interactions for Potential energy surfaces and Atomic forces\n with Deep Neural network Molecular dynamics (MD) simulation, which is considered an important tool for\nstudying physical and chemical processes at the atomic scale, requires accurate\ncalculations of energies and forces. Although reliable energies and forces can\nbe obtained by electronic structure calculations such as those based on density\nfunctional theory (DFT), this approach is computationally expensive. In this\nwork, we propose a full-stack model using deep neural network (NN) to enhance\nthe calculation of force and energy, in which the NN is designed to extract the\nembedding feature of pairwise interactions of an atom and its neighbors, which\nare aggregated to obtain its feature vector for predicting atomic force and\npotential energy. By designing the features of the pairwise interactions, we\ncan control the performance of models and take into account the many-body\neffects and other physics of the atomic interactions. Moreover, we demonstrated\nthat using the Coulomb matrix of the local structures in complement to the\npairwise information, we can improve the prediction of force and energy for\nsilicon systems and the transferability of our models is confirmed to larger\nsystems, with high accuracy.\n Pairwise comparison ; Artificial neural network ; Computer science ; Feature (linguistics) ; Work (physics) ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Vinicius Hernandes | Murilo Sodré Marques | José Rafael Bordin
Source: Journal of Physics Condensed Matter (preprint, 2021-10-12)
Field: Materials Science — Materials Chemistry

Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures Characterization of phases of soft matter systems is a challenge faced in many physical chemical problems. For polymorphic fluids it is an even greater challenge. Specifically, glass forming fluids, as water, can have, besides solid polymorphism, more than one liquid and glassy phases, and even a liquid-liquid critical point. In this sense, we apply a neural network algorithm to analyze the phase behavior of a mixture of core-softened fluids that interact through the continuous-shouldered well (CSW) potential, which have liquid polymorphism and liquid-liquid critical points, similar to water. We also apply the neural network to mixtures of CSW fluids and core-softened alcohols models. We combine and expand methods based on bond-orientational order parameters to study mixtures, applied to mixtures of hardcore fluids and to supercooled water, to include longer range coordination shells. With this, the trained neural network was able to properly predict the crystalline solid phases, the fluid phases and the amorphous phase for the pure CSW and CSW-alcohols mixtures with high efficiency. More than this, information about the phase populations, obtained from the network approach, can help verify if the phase transition is continuous or discontinuous, and also to interpret how the metastable amorphous region spreads along the stable high density fluid phase. These findings help to understand the behavior of supercooled polymorphic fluids and extend the comprehension of how amphiphilic solutes affect the phases behavior. Supercooling ; Metastability ; Amorphous solid ; Polyamorphism ; Polymorphism (computer science) ; Phase (matter) ; Phase transition ; Artificial neural network ; Materials science ; Glass transition ; Thermodynamics ; Chemical physics ; Chemistry ; Physics ; Computer science ; Crystallography ; Artificial intelligence ; Organic chemistry ; Composite material ; Polymer Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Theoretical and Computational Physics
Authors: Vadim Korolev | Yurii M. Nevolin | Thomas A. Manz | Pavel Protsenko
Source: Journal of Chemical Information and Modeling (preprint, 2021-11-17)
Field: Materials Science — Materials Chemistry

Parametrization of Nonbonded Force Field Terms for Metal–Organic Frameworks Using Machine Learning Approach The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques as often as experiments. MOFs are widely known for their outstanding adsorption properties, so a precise description of the host-guest interactions is essential for high-throughput screening aimed at ranking the most promising candidates. However, highly accurate ab initio calculations cannot be routinely applied to model thousands of structures due to the demanding computational costs. Furthermore, methods based on force field (FF) parametrization suffer from low transferability. To resolve this accuracy-efficiency dilemma, we applied a machine learning (ML) approach: extreme gradient boosting. The trained models reproduced the atom-in-material quantities, including partial charges, polarizabilities, dispersion coefficients, quantum Drude oscillator, and electron cloud parameters, with accuracy similar to the reference data set. The aforementioned FF precursors make it possible to thoroughly describe noncovalent interactions typical for MOF-adsorbate systems: electrostatic, dispersion, polarization, and short-range repulsion. The presented approach can also readily facilitate hybrid atomistic simulation/ML workflows. Density functional theory ; Parametrization (atmospheric modeling) ; Force field (fiction) ; Ab initio ; Computer science ; Statistical physics ; Materials science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Aditya Nandy | Chenru Duan | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2021-06-24)
Field: Chemistry — Inorganic Chemistry

Using Machine Learning and Data Mining to Leverage Community Knowledge\n for the Engineering of Stable Metal-Organic Frameworks Although the tailored\nmetal active sites and porous architectures\nof MOFs hold great promise for engineering challenges ranging from\ngas separations to catalysis, a lack of understanding of how to improve\ntheir stability limits their use in practice. To overcome this limitation,\nwe extract thousands of published reports of the key aspects of MOF\nstability necessary for their practical application: the ability to\nwithstand high temperatures without degrading and the capacity to\nbe activated by removal of solvent molecules. From nearly 4000 manuscripts,\nwe use natural language processing and image analysis to obtain over\n2000 solvent-removal stability measures and 3000 thermal degradation\ntemperatures. We analyze the relationships between stability properties\nand the chemical and geometric structures in this set to identify\nlimits of prior heuristics derived from smaller sets of MOFs. By training\npredictive machine learning (ML, i.e., Gaussian process and artificial\nneural network) models to encode the structure–property relationships\nwith graph- and pore-structure-based representations, we are able\nto make predictions of stability orders of magnitude faster than conventional\nphysics-based modeling or experiment. Interpretation of important\nfeatures in ML models provides insights that we use to identify strategies\nto engineer increased stability into typically unstable 3d-transition-metal-containing\nMOFs that are frequently targeted for catalytic applications. We expect\nour approach to accelerate the time to discovery of stable, practical\nMOF materials for a wide range of applications. Heuristics ; Leverage (statistics) ; Stability (learning theory) ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Metal-organic framework ; Biochemical engineering ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science