jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Edgar A. Engel | Venkat Kapil | Michele Ceriotti
Source: The Journal of Physical Chemistry Letters (preprint, 2021-08-06)
Field: Chemistry — Spectroscopy

Importance of Nuclear Quantum Effects for NMR Crystallography The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy. To test the limits of this approximation, we systematically study the importance of finite-temperature and quantum nuclear fluctuations for <sup>1</sup>H, <sup>13</sup>C, and <sup>15</sup>N shieldings in polymorphs of three paradigmatic molecular crystals: benzene, glycine, and succinic acid. The effect of quantum fluctuations is comparable to the typical errors of shielding predictions for static nuclei with respect to experiments, and their inclusion improves the agreement with measurements, translating to more reliable assignment of the NMR spectra to the correct candidate structure. The use of integrated machine-learning models, trained on first-principles energies and shieldings, renders rigorous sampling of nuclear fluctuations affordable, setting a new standard for the calculations underlying NMR structure determinations. Crystal structure prediction ; Maxima and minima ; Chemical shift ; Solid-state nuclear magnetic resonance ; Nuclear magnetic resonance crystallography ; Chemistry ; Statistical physics ; Nuclear magnetic resonance spectroscopy ; Physics ; Nuclear magnetic resonance ; Materials science ; Molecule ; Quantum mechanics ; Mathematics ; Fluorine-19 NMR Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Alexis M. Fenton | Fikile R. Brushett
Source: ChemRxiv (preprint, 2021-04-26)
Field: Chemistry — Electrochemistry

Using Voltammetry Augmented with Physics-Based Modeling and Bayesian Hypothesis Testing to Estimate Electrolyte Composition Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in electrolytes and as such has been used in numerous fields of study. Recently, automation has been introduced into voltammetric analyses to extend their capabilities (e.g., Bayesian parameter estimation, compound identification &lt;i&gt;via&lt;/i&gt; machine learning); however, opportunities exist to enable more versatile methods across a wider range of electrolyte and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of electroactive species in multicomponent electrolytes across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five &lt;i&gt;N&lt;/i&gt;-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled an electrolyte containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating its ability to identify electroactive constituents of a multicomponent solution. Finally, we identify areas of further improvement (e.g., achieving greater detection accuracy) and future applications to potentially enhance &lt;i&gt;in situ&lt;/i&gt; or &lt;i&gt;operando&lt;/i&gt; diagnostic workflows. Electrolyte ; Voltammetry ; Phenothiazine ; Cyclic voltammetry ; Bayesian probability ; Protocol (science) ; Biological system ; Electrochemistry ; Bayesian inference ; Computer science ; Titration ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Electrode ; Artificial intelligence ; Chromatography ; Physical chemistry Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Pol Torres | Stephen Wu | Shenghong Ju | Liu, Chang | Terumasa Tadano | Ryo Yoshida | Junichiro Shiomi
Source: arXiv (Cornell University) (preprint, 2021-10-19)
Field: Materials Science — Materials Chemistry

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices. Anharmonicity ; Thermal conductivity ; Phonon ; Thermal ; Materials science ; Relaxation (psychology) ; Dissipation ; Statistical physics ; Computer science ; Condensed matter physics ; Mechanics ; Thermodynamics ; Physics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Mingjian Wen | Yaser Afshar | Ryan S. Elliott | Ellad B. Tadmor
Source: Computer Physics Communications (preprint, 2021-11-16)
Field: Materials Science — Materials Chemistry

KLIFF: A framework to develop physics-based and machine learning interatomic potentials Python (programming language) ; Computer science ; Interatomic potential ; Modular design ; Artificial neural network ; Computational science ; Artificial intelligence ; Process (computing) ; Machine learning ; Molecular dynamics ; Programming language ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Protein Structure and Dynamics
Authors: Ritaban Dutta | Cherry Chen | D. Renshaw | Daniel Liang
Source: Research Square (preprint, 2021-06-22)
Field: Materials Science — Materials Chemistry

Vision Based Supervised Restricted Boltzmann Machine Helps to Actuate Novel Shape Memory Alloy Accurately Abstract Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation. SMA* ; Shape-memory alloy ; Computer science ; Artificial intelligence ; Computer vision ; Machine learning ; Algorithm Shape Memory Alloy Transformations
Authors: Andrew E. Sifain | Levi Lystrom | Richard A. Messerly | Justin S. Smith | Benjamin Nebgen | Kipton Barros | Sergei Tretiak | Nicholas Lubbers | Brendan J. Gifford
Source: ChemRxiv (preprint, 2021-02-24)
Field: Materials Science — Materials Chemistry

Predicting Phosphorescence Energies and Inferring Wavefunction Localization with Machine Learning &lt;p&gt;Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on &lt;i&gt;ab-initio&lt;/i&gt; datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method to an out-of-sample test set of large phosphorescent compounds and demonstrate the substantial improvement that localization layers have on predicting their phosphorescence energies. Remarkably, the inferred localization weights have a strong relationship with the &lt;i&gt;ab-initio&lt;/i&gt; spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model.&lt;/p&gt; Phosphorescence ; Singlet state ; Ab initio ; Artificial neural network ; Energy (signal processing) ; Chemistry ; Physics ; Computer science ; Atomic physics ; Machine learning ; Excited state ; Quantum mechanics ; Fluorescence Machine Learning in Materials Science ; bioluminescence and chemiluminescence research ; Analytical Chemistry and Sensors
Authors: Naoki Iinuma | Fusataka Kuniyoshi | Jun Ozawa | Makoto Miwa
Source: Research Square (preprint, 2021-06-30)
Field: Materials Science — Materials Chemistry

Annotation and Classification of Graphs of Property Values Reported in Material Science Literature Annotation ; Property (philosophy) ; Computer science ; Natural language processing ; Information retrieval ; Artificial intelligence ; Epistemology ; Philosophy Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Material Selection and Properties
Authors: Kevin M. Roccapriore | Qiang Zou | Rui Xue | Maxim Ziatdinov | Mingming Fu | David Mandrus | Mina Yoon | Bobby G. Sumpter | Zheng Gai | Sergei Kalinin
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Understanding chemical bonding in adatom configurations on the surface of the magnetic Weyl semi-metal Co 3 Sn 2 S 2 with machine learning, 3D scanning tunneling spectroscopy, and first-principles calculations Scanning tunneling microscope ; Scanning tunneling spectroscopy ; Surface (topology) ; Metal ; Materials science ; Spectroscopy ; Condensed matter physics ; Quantum tunnelling ; Chemical bond ; Chemical physics ; Nanotechnology ; Physical chemistry ; Chemistry ; Physics ; Metallurgy ; Quantum mechanics ; Geometry ; Mathematics Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; 2D Materials and Applications
Authors: Ivan A. Kruglov | A. V. Yanilkin | Yana Propad | Artem R. Oganov
Source: arXiv (Cornell University) (preprint, 2021-01-25)
Field: Materials Science — Materials Chemistry

Crystal structure prediction at finite temperatures Crystal structure prediction is a central problem of theoretical crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on either energy landscape exploration$^{1,2}$ or, more commonly, global optimization$^{3-8}$, largely solved this problem and enabled fully non-empirical computational materials discovery$^{9,10}$. A major shortcoming is that, to avoid expensive calculations of the entropy, crystal structure prediction was done at zero Kelvin and searched for the global minimum of the enthalpy, rather than free energy. As a consequence, high-temperature phases (especially those which are not quenchable to zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction at finite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with a force field (which can be anything from a parametric force field for simpler cases to a trained on-the-fly machine learning interatomic potential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate ab initio results. We test the accuracy of this method on metals (probing the P-T phase diagram of Al and Fe), a refractory intermetallide (WB), and a significantly ionic ceramic compound (Earth-forming silicate MgSiO3 at pressures and temperatures of the Earth's lower mantle). We find that the hcp-phase of aluminum has a wider stability field than previously thought, and the temperature-induced transition $α$-$β$ in WB occurs at 2789 K. It is also found that iron has hcp structure at conditions of the Earth's inner core, and the much debated (and important for constraining Earth's thermal structure) Clapeyron slope of the post-perovskite phase transition in MgSiO3 is 5.88 MPa/K. Crystal structure prediction ; Anharmonicity ; Ab initio ; Interatomic potential ; Phase diagram ; Crystal structure ; Thermodynamics ; Materials science ; Phase transition ; Molecular dynamics ; Statistical physics ; Physics ; Chemistry ; Phase (matter) ; Condensed matter physics ; Computational chemistry ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Omri Abaarbanel | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2021-03-09)
Field: Materials Science — Materials Chemistry

Machine Learning to Accelerate Screening for Marcus Reorganization Energies Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic applications, including solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy, which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use towards screening new compounds with low internal reorganization energies. Our models have an overall root mean square error of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters. Intramolecular force ; Marcus theory ; Charge (physics) ; Density functional theory ; Work (physics) ; Computer science ; Transistor ; Materials science ; Range (aeronautics) ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Voltage ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Conducting polymers and applications
Authors: Jian‐Min Zuo | Renliang Yuan | Yu‐Tsun Shao | Haw-Wen Hsiao | Saran Pidaparthy | Yang Hu | Qun Yang | Jiong Zhang
Source: arXiv (Cornell University) (preprint, 2021-10-05)
Field: Materials Science — Materials Chemistry

Data-Driven Electron Microscopy: Electron Diffraction Imaging of Materials Structural Properties Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs) and process DPs to extract crystallographic information to form images or tomograms based on crystal structural properties, giving rise to data-driven electron microscopy. Critical to this kind of imaging is the type of crystallographic information being collected, which can be achieved with a judicious choice of electron diffraction techniques, and the efficiency and accuracy of DP processing, which requires the development of new algorithms. Here, we review recent progress made in data collection, new algorithms, and automated electron DP analysis. These progresses will be highlighted using application examples in materials research. Future opportunities based on smart sampling and machine learning are also discussed. Electron crystallography ; Nanocrystalline material ; Electron diffraction ; Electron tomography ; Diffraction ; Characterization (materials science) ; Transmission electron microscopy ; Materials science ; Detector ; Electron ; Energy filtered transmission electron microscopy ; Reflection high-energy electron diffraction ; Computer science ; Electron backscatter diffraction ; Scanning transmission electron microscopy ; Crystallography ; Nanotechnology ; Optics ; Chemistry ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: M. D ́Amore | Toshiaki Taniike | Minoru Terano | Anna Maria Ferrari
Source: Preprints.org (preprint, 2021-12-28)
Field: Materials Science — Materials Chemistry

Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT Understanding the structure and properties of MgCl2/TiCl4/ID nanoclusters is a key to uncover the origin of Ziegler-Natta catalysis. In this work MgCl2/TiCl4 nanoplatelets derived by machine learning and DFT calculations have been used to model the interaction with ethyl-benzoate EB (as internal donor) with available exposed sites of binary TixCly/MgCl2 systems. The influence of vicinal Ti2Cl8 and coadsorbed TiCl4 on energetic, structural and spectroscopic behaviour of EB has been considered. The adsorption of homogeneous-like TiCl4EB and TiCl4(EB)2 at the various surface sites have been also simulated. Calculations have been carried out by employing B3LYP-D2 and M06 functionals. The adducts have been characterized by computing IR and Raman spectra that have been found to provide specific fingerprints useful to identify surface species; IR spectra have been successfully compared to available experimental data. Nanoclusters ; Raman spectroscopy ; Adsorption ; Vicinal ; Adduct ; Catalysis ; Chemistry ; Homogeneous ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Ionic liquids properties and applications
Authors: Wei Li | Craig Burkhart | Patrycja Polińska | Vagelis Harmandaris | Manolis Doxastakis
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Backmapping coarse-grained macromolecules: an efficient and versatile machine-learning approach Multiscale modeling of polymers exchanges information between coarse and fine representations of molecules to capture material properties over a wide range of spatial and temporal scales. Restoring details at a finer scale requires us to generate information following embedded physics and statistics of the models at two different levels of description. Techniques designed to address this persistent challenge balance among accuracy, efficiency, and general applicability. In this work, we present an image-based approach for structural backmapping from coarse-grained to atomistic models with cis-1,4 polyisoprene melts as an illustrative example. Through machine learning, we train conditional generative adversarial networks on the correspondence between configurations at the levels considered. The trained model is subsequently applied to provide predictions of atomistic structures from the input coarse-grained configurations. The effect of different data representation schemes on training and prediction quality is examined. Our proposed backmapping approach shows remarkable efficiency and transferability over different molecular weights in the melt based on training sets constructed from oligomeric compounds. We anticipate that this versatile backmapping approach can be readily extended to other complex systems to provide high-fidelity initial configurations with minimal human intervention. Computer science ; Representation (politics) ; Fidelity ; Transferability ; Benchmark (surveying) ; Range (aeronautics) ; Generative grammar ; High fidelity ; Scale (ratio) ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Wenhao Sun
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

Unsupervised knowledge discovery in 'big' materials data A major objective in recent computational materials research has been the search and discovery of novel materials with superior properties.However, prior to the availability of immense computational power, materials design was guided by conceptual frameworks for synthesis-structure-property relationships, such as Pauling's Rules, the Hume-Rothery Rules, Pettifor Tables, Structure Maps, Ashby Tables, etc.Not only can these heuristic frameworks point us towards new and valuable materials, they also provide a satisfying conceptual foundation upon which to base our scientific intuition.In this talk, I will discuss how we can leverage unsupervised machine-learning algorithms to extract new heuristic relationships from modern large-scale materials databases.In order to extract meaningful synthesis-structure-property relationships, we will first need physically-relevant materials features.Many relevant materials features are not immediately available in current materials property databases.Determination of which features to construct will likely rely on domain knowledge and physical intuition, at least in the near-term future.We will demonstrate how these computational materials discovery and informatics tools can be used to survey, visualize, and explain stability relationships across the inorganic ternary metal nitrides [1]. 1.W. Sun et al., "A map of the inorganic ternary metal nitrides", Nature Materials (2019) Computer science ; Materials informatics ; Data science ; Intuition ; Property (philosophy) ; Knowledge extraction ; Leverage (statistics) ; Artificial intelligence ; Health informatics ; Cognitive science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides