jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siavash Salek Soltani | Chad W. Sinclair | Jörg Rottler
Source: arXiv (Cornell University) (preprint, 2021-11-19)
Field: Materials Science — Materials Chemistry

Exploring glassy dynamics with Markov state models from graph dynamical neural networks Amorphous materials exhibit structural heterogeneities that relax only on long timescales. Using machine learning techniques, we construct a Markov state model (MSM) for model glass formers that coarse-grains the dynamics into a low-dimensional space, in which transitions occur with rates corresponding to the slowest modes of the system. The transition timescale between states is more than an order of magnitude larger than the conventional alpha-relaxation time, and reveals a fragile to strong crossover at the glass transition. The learned map of states assigned to the particles exhibits correlations of a few molecular diameters both at liquid and glassy temperatures. We show that the MSM effectively constructs a map of scaled excess Voronoi volume, and the free energy difference between the two states is given exactly by the entropy of the these distributions. These results resonate with classic free volume theories of the glass transition, singling out local packing fluctuations as the slowest relaxing features. Statistical physics ; Amorphous solid ; Markov chain ; Glass transition ; Crossover ; Molecular dynamics ; Voronoi diagram ; Relaxation (psychology) ; Physics ; Mathematics ; Computer science ; Chemistry ; Artificial intelligence ; Quantum mechanics ; Crystallography ; Polymer ; Geometry ; Nuclear magnetic resonance Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Andreas Jeindl | Lukas Hörmann | Oliver T. Hofmann
Source: arXiv (Cornell University) (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Interplay Between Surface Polymorphism and Work Function Changes of Organic/Metal Interfaces Whenever molecules adsorb on metal surfaces, they might form a variety of different surface polymorphs, either in thermodynamic equilibrium, or kinetically trapped. The influence of this surface polymorphism on interface properties is a priori unknown. In this work we employ a combination of first-principles calculations and machine learning to investigate the influence of the polymorphism on the range of interface work functions that various material combinations can assume. For this, we perform a full theoretical structure search on three physically distinct model systems: We start with the hardly interacting naphthalene, which adsorbs flat on the surface, continue with a homologous series of acenequinones, which also adsorb flat but undergo charge transfer reactions, and finally investigate tetracyanoethylene, which undergoes a phase transition from lying to standing polymorphs on the Cu(111) surface. Employing ab initio thermodynamics, we also show the work function changes obtainable at thermodynamic equilibrium for different temperatures. Our thorough investigations indicate that flat lying molecules exhibit polymorphism-induced work function changes of a few hundred meV while re-orientation can lead to work function changes of several eV. Adsorption ; Work function ; Chemical physics ; Polymorphism (computer science) ; Ab initio ; Thermodynamics ; Chemistry ; Work (physics) ; Molecule ; Tetracyanoethylene ; Materials science ; Metal ; Physical chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; nanoparticles nucleation surface interactions
Authors: Yanzhou Wang | Zheyong Fan | Ping Qian | Tapio Ala-Nissilä | A. Miguel
Source: arXiv (Cornell University) (preprint, 2021-09-21)
Field: Materials Science — Materials Chemistry

Atomic Structure and Pore Size Distribution in Nanoporous Carbon We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csanyi [Phys. Rev. B 2017, 95, 094203] adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7~g/cm$^3$) with structures containing 131,072 atoms. Our results are in good agreement with experimental data for the available observables, and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials. Nanoporous ; Molecular dynamics ; Materials science ; van der Waals force ; Carbon fibers ; Diffraction ; Characterization (materials science) ; Work (physics) ; Gaussian ; Chemical physics ; Molecular physics ; Nanotechnology ; Composite material ; Computational chemistry ; Physics ; Thermodynamics ; Chemistry ; Molecule ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Navid Ansari | Hans‐Peter Seidel | Vahid Babaei
Source: arXiv (Cornell University) (preprint, 2021-09-27)
Field: Materials Science — Materials Chemistry

Mixed Integer Neural Inverse Design In computational design and fabrication, neural networks are becoming important surrogates for bulky forward simulations. A long-standing, intertwined question is that of inverse design: how to compute a design that satisfies a desired target performance? Here, we show that the piecewise linear property, very common in everyday neural networks, allows for an inverse design formulation based on mixed-integer linear programming. Our mixed-integer inverse design uncovers globally optimal or near optimal solutions in a principled manner. Furthermore, our method significantly facilitates emerging, but challenging, combinatorial inverse design tasks, such as material selection. For problems where finding the optimal solution is not desirable or tractable, we develop an efficient yet near-optimal hybrid optimization. Eventually, our method is able to find solutions provably robust to possible fabrication perturbations among multiple designs with similar performances. Inverse ; Integer programming ; Mathematical optimization ; Integer (computer science) ; Computer science ; Optimal design ; Selection (genetic algorithm) ; Artificial neural network ; Piecewise ; Piecewise linear function ; Linear programming ; Mathematics ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced Multi-Objective Optimization Algorithms
Authors: Sterling G. Baird | Taylor D. Sparks
Source: ChemRxiv (preprint, 2021-10-31)
Field: Materials Science — Materials Chemistry

High-throughput Calculation of Atomic Planar Density for Compounds A large collection of element-wise planar densities for compounds obtained from the Materials Project is calculated using brute force computational geometry methods. We demonstrate that the element-wise max lattice plane densities can be useful as machine learning features. The methods described here are implemented in an open-source Mathematica package hosted at https://github.com/sgbaird/LatticePlane. Planar ; Computational science ; Computer science ; Lattice (music) ; Element (criminal law) ; Brute force ; Plane (geometry) ; Throughput ; Open source ; Algorithm ; Computational physics ; Geometry ; Physics ; Computer graphics (images) ; Software ; Mathematics ; Programming language ; Acoustics ; Telecommunications Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Andrey A. Buglak | Asterios Charisiadis | Aimee Sheehan | Christopher J. Kingsbury | Mathias O. Senge | Mikhail A. Filatov
Source: ChemRxiv (preprint, 2021-03-11)
Field: Materials Science — Materials Chemistry

Quantitative Structure–property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. Herein, we analyzed the available data on singlet oxygen generation quantum yields (F?) for a dataset containing > 70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran). In order to build reliable QSPR model, we synthesized a series of new BODIPYs containing different electron donating aryl groups in the meso position, studied their optical and structural properties along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using Density Functional Theory (DFT), namely M06-2X functional. QSPR models predicting F? values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R = 0.88 ? 0.91 and q2 = 0.62 ? 0.69) for all three solvents. A small root mean squared error of 8.2% was obtained for F? values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting F? values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability. Singlet oxygen ; Intersystem crossing ; BODIPY ; Chemistry ; Quantitative structure–activity relationship ; Quantum yield ; Density functional theory ; Singlet state ; Photochemistry ; Conical intersection ; Computational chemistry ; Excited state ; Molecule ; Oxygen ; Organic chemistry ; Fluorescence ; Stereochemistry ; Physics Luminescence and Fluorescent Materials ; Radical Photochemical Reactions ; Porphyrin and Phthalocyanine Chemistry
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: ChemRxiv (preprint, 2021-07-02)
Field: Chemistry — Inorganic Chemistry

Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br /> Lanthanide ; Materials science ; Adsorption ; Metal-organic framework ; Reproducibility ; Relative humidity ; Chromatography ; Thermodynamics ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Organic and Molecular Conductors Research
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: ChemRxiv (preprint, 2021-05-20)
Field: Chemistry — Inorganic Chemistry

Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%). Lanthanide ; Materials science ; Adsorption ; Metal-organic framework ; Relative humidity ; Reproducibility ; Proton ; Chemical engineering ; Chromatography ; Thermodynamics ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Organic and Molecular Conductors Research
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 with Deep Neural network Molecular dynamics (MD) simulation, which is considered an important tool for studying physical and chemical processes at the atomic scale, requires accurate calculations of energies and forces. Although reliable energies and forces can be obtained by electronic structure calculations such as those based on density functional theory (DFT), this approach is computationally expensive. In this work, we propose a full-stack model using deep neural network (NN) to enhance the calculation of force and energy, in which the NN is designed to extract the embedding feature of pairwise interactions of an atom and its neighbors, which are aggregated to obtain its feature vector for predicting atomic force and potential energy. By designing the features of the pairwise interactions, we can control the performance of models and take into account the many-body effects and other physics of the atomic interactions. Moreover, we demonstrated that using the Coulomb matrix of the local structures in complement to the pairwise information, we can improve the prediction of force and energy for silicon systems and the transferability of our models is confirmed to larger systems, with high accuracy. Pairwise comparison ; Artificial neural network ; Computer science ; Statistical physics ; Work (physics) ; Feature (linguistics) ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michael Ashby | Jenna Pope | PNNL Developer Central
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

pnnl/mol_dgnn Molecular dynamics simulations often rely on parameterized atomistic potentials, which can be inaccurate compared to ab initio simulation methods. However, dynamic ab initio simulations come with high computational cost. Deep learning techniques offer a method to obtain both high accuracy and low cost predictions of dynamics. Our method gives a prediction of the structural dynamics of a molecule accounting for the prior dynamics, giving high-accuracy predictions at low computational cost Environmental science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Alexander S. Lyons | Steven W. Rick
Source: arXiv (Cornell University) (preprint, 2021-01-07)
Field: Materials Science — Materials Chemistry

Simulations of water and hydrophobic hydration using a neural network potential Using a neural network potential (ANI-1ccx) generated from quantum data on a large data set of molecules and pairs of molecules, isothermal, constant volume simulations demonstrate that the model can be as accurate as ab initio molecular dynamics for simulations of pure liquid water and the aqueous solvation of a methane molecule. No theoretical or experimental data for the liquid phase is used to train the model, suggesting that the ANI-1ccx approach is an effective method to link high level ab initio methods to potentials for large scale simulations. Solvation ; Ab initio ; Molecule ; Molecular dynamics ; Liquid water ; Chemical physics ; Isothermal process ; Artificial neural network ; Aqueous solution ; Chemistry ; Ab initio quantum chemistry methods ; Water model ; Computational chemistry ; Statistical physics ; Materials science ; Thermodynamics ; Physics ; Physical chemistry ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Ayana Ghosh | Christopher T. Nelson | Mark P. Oxley | Xiaohang Zhang | Maxim Ziatdinov | Ichiro Takeuchi | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2021-02-25)
Field: Materials Science — Materials Chemistry

Deep learning polarization distributions in ferroelectrics from STEM data: with and without atom finding Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic-scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations. Maxima and minima ; Scanning transmission electron microscopy ; Polarization (electrochemistry) ; Convolutional neural network ; Atomic units ; Physics ; Classifier (UML) ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Computer science ; Optics ; Biological system ; Materials science ; Chemistry ; Transmission electron microscopy ; Mathematics ; Biology ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Giulio Imbalzano | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2021-03-16)
Field: Materials Science — Materials Chemistry

Properties of a III-V semiconductor across temperatures and compositions from first principles Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the Ga$_x$As$_{1-x}$ system. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committe model can be used to reliably determine the uncertainty induced by the limitations of the ML model on its predictions, to identify regions of phase space that are predicted with insufficient accuracy, and to iteratively refine the training set to achieve consistent, reliable modeling. Phase diagram ; Phase (matter) ; Polar ; Parameter space ; Set (abstract data type) ; Range (aeronautics) ; Series (stratigraphy) ; Table (database) ; Ab initio ; Space (punctuation) ; Computer science ; Binary number ; Statistical physics ; Diagram ; Semiconductor ; Materials science ; Algorithm ; Data mining ; Mathematics ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions