jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Da-Chuan Lu | Taige Wang | Shubhayu Chatterjee | Yi‐Zhuang You
Source: Physical review. B./Physical review. B (article, 2022-10-06)
Field: Materials Science — Materials Chemistry

Correlated metals and unconventional superconductivity in rhombohedral trilayer graphene: A renormalization group analysis Motivated by recent experimental observations of correlated metallic phases and superconductivity in rhombohedral trilayer graphene (RTG), we perform an unbiased study of electronic ordering instabilities in hole-doped RTG. Specifically, we focus on electronic states energetically proximate to Van Hove singularities (VHSs), where a large density of states promotes different interaction-induced symmetry-breaking electronic orders. To resolve the Fermi surface near VHSs, we construct a fermionic hot-spot model and demonstrate that a perpendicular electric field can tune different nesting structures of the Fermi surface. Subsequently, we apply a renormalization group analysis to describe the low-energy phase diagrams of our model under both short-range repulsive interactions as well as realistic (long-range) Coulomb interactions. Our analysis shows instabilities towards either intervalley coherent metallic phases or superconducting phases. The dominant pairing channel depends crucially on the nature of Fermi surface nesting---repulsive Coulomb interaction favors spin-singlet $d$-wave pairing for relatively small displacement field and spin-singlet $i$-wave pairing for larger displacement field. We argue that the phase diagram of RTG can be well-understood by modeling the realistic Coulomb interaction as the sum of repulsive density-density interaction and ferromagnetic spin-triplet intervalley coherence (IVC) Hund's coupling, while phonon-mediated electronic interactions have a negligible effect on this system, in sharp contrast to twisted graphene multilayers. Condensed matter physics ; Pairing ; Physics ; Fermi surface ; Superconductivity ; Coulomb ; Van Hove singularity ; Renormalization group ; Density of states ; Graphene ; Fermi level ; Quantum mechanics Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Xinyan Liu | Cai Cheng | Wanghui Zhao | Hong‐Jie Peng | Tao Wang
Source: ACS Catalysis (article, 2022-03-25)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Screening of Stepped Alloy Surfaces for C<sub>1</sub> Catalysis Efficient reactivity assessment for stepped alloy surfaces presents a major challenge in designing effective catalysts for industrial catalytic conversion of single-carbon (C1) molecules. In this work, we propose a machine learning (ML)-assisted approach to screen active and stable binary alloys for various C1 catalytic processes. Leveraging only non-ab initio, simple bulk material properties as input features, the ML models exhibit impressive accuracy for predicting site-specific adsorption energies of atomic carbon and oxygen, which enable not only fast navigation through abundant material space but also extraction of explicable physical insights. The effectiveness of the ML models is further validated by applying their predictions to catalyst screening for common reactions in C1 catalysis, as well as a detailed kinetic study on one example candidate, Cu3Pd. This data-driven approach with fully interpretable physical features demonstrates the possibility of unearthing underlying catalyst design principles from apparent data and paves the road for the discovery of desirable alloy catalysts. Catalysis ; Ab initio ; Adsorption ; Reactivity (psychology) ; Alloy ; Materials science ; Chemical space ; Computer science ; Nanotechnology ; Chemistry ; Physical chemistry ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yaolong Zhang | Qidong Lin | Bin Jiang
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2022-11-16)
Field: Materials Science — Materials Chemistry

Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization Abstract Machine learning techniques have been widely applied in many fields of chemistry, physics, biology, and materials science. One of the most fruitful applications is machine learning of the complicated multidimensional function of potential energy or related electronic properties from discrete quantum chemical data. In particular, substantial efforts have been dedicated to developing various atomistic neural network (AtNN) representations, which refer to a family of methods expressing the targeted physical quantity as a sum of atomic components represented by atomic NNs. This class of approaches not only fully preserves the physical symmetry of the system but also scales linearly with respect to the size of a system, enabling accurate and efficient chemical dynamics and spectroscopic simulations in complicated systems and even a number of variably sized systems across the phases. In this review, we discuss different strategies in developing highly efficient and representable AtNN potentials, and in generalizing these scalar AtNN models to learn vectorial and tensorial quantities with the correct rotational equivariance. We also review active learning algorithms to generate practical AtNN models and present selected examples of AtNN applications in gas‐surface systems to demonstrate their capabilities of accurately representing both molecular systems and condensed phase systems. We conclude this review by pointing out remaining challenges for the further development of more reliable, transferable, and scalable AtNN representations in more application scenarios. This article is categorized under: Data Science &gt; Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics &gt; Molecular Interactions Computer science ; Artificial neural network ; Generalization ; Scalability ; Physical system ; Artificial intelligence ; Scalar (mathematics) ; Statistical mechanics ; Molecular dynamics ; Machine learning ; Statistical physics ; Theoretical computer science ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Vinayak Bhat | Parker Sornberger | Balaji Sesha Sarath Pokuri | Rebekah Duke | Baskar Ganapathysubramanian | Chad Risko
Source: Chemical Science (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed. Chemical space ; Computer science ; Time-dependent density functional theory ; Density functional theory ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Computational chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Elliot H. E. Farrar | Matthew N. Grayson
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol<sup>-1</sup> and substantially better than SQM methods without ML correction (5.71 kcal mol<sup>-1</sup>). Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved <i>via</i> the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria. Mechanism (biology) ; Computer science ; Empirical modelling ; Empirical research ; Quality (philosophy) ; Machine learning ; Artificial intelligence ; Simulation ; Mathematics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Marcel Ruth | Dennis Gerbig | Peter R. Schreiner
Source: Journal of Chemical Theory and Computation (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning Accurate thermochemistry is essential in many chemical disciplines, such as astro-, atmospheric, or combustion chemistry. These areas often involve fleetingly existent intermediates whose thermochemistry is difficult to assess. Whenever direct calorimetric experiments are infeasible, accurate computational estimates of relative molecular energies are required. However, high-level computations, often using coupled cluster theory, are generally resource-intensive. To expedite the process using machine learning techniques, we generated a database of energies for small organic molecules at the CCSD(T)/cc-pVDZ, CCSD(T)/aug-cc-pVDZ, and CCSD(T)/cc-pVTZ levels of theory. Leveraging the power of deep learning by employing graph neural networks, we are able to predict the effect of perturbatively included triples (T), that is, the difference between CCSD and CCSD(T) energies, with a mean absolute error of 0.25, 0.25, and 0.28 kcal mol<sup>-1</sup> (<i>R</i><sup>2</sup> of 0.998, 0.997, and 0.998) with the cc-pVDZ, aug-cc-pVDZ, and cc-pVTZ basis sets, respectively. Our models were further validated by application to three validation sets taken from the S22 Database as well as to a selection of known theoretically challenging cases. Thermochemistry ; Coupled cluster ; Computer science ; Computation ; Machine learning ; Statistical physics ; Chemistry ; Artificial intelligence ; Physics ; Molecule ; Thermodynamics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Dorián László Galata | Boldizsár Zsiros | Lilla Alexandra Mészáros | Brigitta Nagy | Edina Szabó | Attila Farkas | Zsombor Kristóf Nagy
Source: Journal of Pharmaceutical and Biomedical Analysis (article, 2022-02-12)
Field: Chemistry — Analytical Chemistry

Raman mapping-based non-destructive dissolution prediction of sustained-release tablets In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f<sub>2</sub> similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology. Principal component analysis ; Raman spectroscopy ; Dissolution ; Chemical imaging ; Chemistry ; Biological system ; Particle size ; Histogram ; Partial least squares regression ; Analytical Chemistry (journal) ; Artificial intelligence ; Pattern recognition (psychology) ; Chromatography ; Computer science ; Machine learning ; Optics ; Image (mathematics) ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Drug Solubulity and Delivery Systems
Authors: Sheng Gong | Shuo Wang | Tian Xie | Woo Hyun Chae | Runze Liu | Yang Shao‐Horn | Jeffrey C. Grossman
Source: JACS Au (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r<sup>2</sup>SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output. Stability (learning theory) ; Enthalpy ; Representation (politics) ; Artificial neural network ; Work (physics) ; Computer science ; Density functional theory ; Machine learning ; Experimental data ; Artificial intelligence ; Standard enthalpy change of formation ; Thermodynamics ; Algorithm ; Statistical physics ; Chemistry ; Mathematics ; Computational chemistry ; Physics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ryan Jacobs | Mingren Shen | Yuhan Liu | Hao Wei | Xiaoshan Li | Ruoyu He | Jacob R.C. Greaves | Donglin Wang | Zeming Xie | Zitong Huang | Chao Wang | Kevin G. Field | Dane Morgan
Source: Cell Reports Physical Science (article, 2022-05-01)
Field: Materials Science — Surfaces, Coatings and Films

Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs Transmission electron microscopy (TEM) is a popular method for characterizing and quantifying defects in materials. Analyzing digitized TEM images is typically done manually, which is a time-consuming and potentially error-prone task that is not scalable to large dataset sizes, motivating development of automated methods for quantifying and analyzing defects in TEM images. In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning mask regional convolutional neural network (Mask R-CNN) model. We evaluate the performance of the model based on distributions of defect shapes, sizes, and areal densities relevant to informing physical modeling and understanding irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests, which include a suite of random splits and dataset-size-dependent and domain-targeted cross-validation tests, exposing potential weak points in the model applicability domain. Our model predicts the expected irradiation-induced material hardening to within 10–20 MPa (about 10% of total hardening), on par with experimental error. Finally, we discuss the first phase of an effort to provide an easy-to-use, open-source object detection tool to the broader community for identifying defects in new images. Computer science ; Convolutional neural network ; Segmentation ; Deep learning ; Artificial intelligence ; Hardening (computing) ; Scalability ; Categorization ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Lena Pilz | Carsten Natzeck | Jonas Wohlgemuth | Nina Scheuermann | Peter G. Weidler | Ilona Wagner | Christof Wöll | Manuel Tsotsalas
Source: Advanced Materials Interfaces (article, 2022-12-04)
Field: Chemistry — Inorganic Chemistry

Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches Abstract Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer‐by‐layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi‐parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML‐based approach allowing to quickly identify optimized growth conditions for HKUST‐I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. In addition, an analysis of the results allows to gain further insights into the factors governing the growth of MOF thin films. Materials science ; Nanotechnology ; Crystallinity ; Substrate (aquarium) ; Thin film ; Layer (electronics) ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Shun Tian | Ke Zhou | Chuanqi Huang | Qian Chen | Zhibin Gao | Yilun Liu
Source: Extreme Mechanics Letters (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Investigation and understanding of the mechanical properties of MXene by high-throughput computations and interpretable machine learning MXenes ; Materials science ; Monolayer ; Stiffness ; Semiconductor ; Ultimate tensile strength ; Graphene ; Nanotechnology ; Throughput ; Nanoelectronics ; Composite material ; Optoelectronics ; Computer science MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Jonathan Lahnsteiner | Menno Bokdam
Source: Physical review. B./Physical review. B (article, 2022-01-04)
Field: Materials Science — Materials Chemistry

Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Cs</mml:mi><mml:mi>Pb</mml:mi><mml:msub><mml:mi>Br</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>, a phonon liquid with Cs rattlers The phonon dispersion relations of crystal lattices can often be well described with the harmonic approximation. However, when the potential energy landscape exhibits more anharmonicity, for instance, in the case of a weakly bonded crystal or when the temperature is raised, the approximation fails to capture all crystal lattice dynamics properly. Phonon-phonon scattering mechanisms become important and limit the phonon lifetimes. We take a novel approach and simulate the phonon dispersion of a complex dynamic solid at elevated temperatures with machine-learning force fields of near-first-principles accuracy. Through large-scale molecular dynamics simulations the projected velocity autocorrelation function (PVACF) is obtained. We apply this approach to the inorganic perovskite $\mathrm{Cs}\mathrm{Pb}{\mathrm{Br}}_{3}$. Imaginary modes in the harmonic picture of this perovskite are absent in the PVACF, indicating a dynamic stabilization of the crystal. The anharmonic nature of the potential makes a decoupling of the system into a weakly interacting phonon gas impossible. The phonon spectra of $\mathrm{Cs}\mathrm{Pb}{\mathrm{Br}}_{3}$ show the characteristics of a phonon liquid. Rattling motions of the ${\mathrm{Cs}}^{+}$ cations are studied by self-correlation functions and are shown to be nearly dispersionless motions of the cations with a frequency of $\ensuremath{\sim}0.8\phantom{\rule{0.16em}{0ex}}\mathrm{THz}$ within the lead-bromide framework. Phonon ; Anharmonicity ; Physics ; Lattice (music) ; Crystal (programming language) ; Condensed matter physics ; Machine learning ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices