jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simran Kaur | A.T. Krishnan | Sudip Chakraborty
Source: Journal of Energy Storage (article, 2023-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances and challenges of carbon nano onions (CNOs) for application in supercapacitor devices (SCDs) Supercapacitor ; Nanotechnology ; Materials science ; Capacitance ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Max Pinheiro | Shuang Zhang | Pavlo O. Dral | Mario Barbatti
Source: Scientific Data (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets Multidimensional surfaces of quantum chemical properties, such as potential energies and dipole moments, are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1.18 million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures. The diversity of our datasets is demonstrated by visualizing the geometries distribution with dimensionality reduction as well as via comparison of statistical features of the QM properties with those available in existing datasets. Our sampling targets broader quantum mechanical distribution of the configurational space than provided by commonly used sampling through classical molecular dynamics, upping the challenge for machine learning models. Interpolation (computer graphics) ; Sampling (signal processing) ; Computer science ; Geometry ; Database ; Computer graphics (images) ; Mathematics ; Computer vision Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xia Liang | Johan Klarbring | William J. Baldwin | Zhenzhu Li | Gábor Cśanyi | Aron Walsh
Source: The Journal of Physical Chemistry C (article, 2023-08-30)
Field: Materials Science — Materials Chemistry

Structural Dynamics Descriptors for Metal Halide Perovskites Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as "soft semiconductors" due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modeling and experiments. Here, we report the quantitative analysis of structural dynamics in time and space from molecular dynamics simulations of perovskite crystals. The compact descriptors provided cover a wide variety of structural properties, including octahedral tilting and distortion, local lattice parameters, molecular orientations, as well as their spatial correlation. To validate our methods, we have trained a machine learning force field (MLFF) for methylammonium lead bromide (CH<sub>3</sub>NH<sub>3</sub>PbBr<sub>3</sub>) using an on-the-fly training approach with Gaussian process regression. The known stable phases are reproduced, and we find an additional symmetry-breaking effect in the cubic and tetragonal phases close to the phase-transition temperature. To test the implementation for large trajectories, we also apply it to 69,120 atom simulations for CsPbI<sub>3</sub> based on an MLFF developed using the atomic cluster expansion formalism. The structural dynamics descriptors and Python toolkit are general to perovskites and readily transferable to more complex compositions. Tetragonal crystal system ; Molecular dynamics ; Chemical physics ; Halide ; Perovskite (structure) ; Materials science ; Octahedron ; Statistical physics ; Chemistry ; Crystal structure ; Physics ; Crystallography ; Computational chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Herman Rull | Markus Fischer | Stefan Kühn
Source: Journal of Cheminformatics (article, 2023-11-27)
Field: Chemistry — Spectroscopy

NMR shift prediction from small data quantities Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting [Formula: see text] and [Formula: see text] NMR chemical shifts of small molecules in specific solvents. Computer science ; Data mining ; Data science Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jon López-Zorrilla | Xabier M. Aretxabaleta | In Won Yeu | I. Etxebarria | Hegoi Manzano | Nongnuch Artrith
Source: The Journal of Chemical Physics (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extension of the atomic energy network (ænet), ænet-PyTorch provides access to all the tools included in ænet for the application and usage of the potentials. The package has been designed as an alternative to the internal training capabilities of ænet, leveraging the power of graphic processing units to facilitate direct training on forces in addition to energies. This leads to a substantial reduction of the training time by one to two orders of magnitude compared to the central processing unit implementation, enabling direct training on forces for systems beyond small molecules. Here, we demonstrate the main features of ænet-PyTorch and show its performance on open databases. Our results show that training on all the force information within a dataset is not necessary, and including between 10% and 20% of the force information is sufficient to achieve optimally accurate interatomic potentials with the least computational resources. Computer science ; Artificial neural network ; Net (polyhedron) ; Artificial intelligence ; Central processing unit ; Computational science ; Training (meteorology) ; Deep learning ; Machine learning ; Computer engineering ; Computer hardware Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wei Gu | Bin Yang | Dengfeng Li | Xunzhong Shang | Zhiyong Zhou | Jinming Guo
Source: Journal of Advanced Ceramics (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning The piezoelectric performance serves as the basis for the applications of piezoelectric ceramics. The ability to rapidly and accurately predict the piezoelectric coefficient (<i>d</i><sub>33</sub>) is of much practical importance for exploring high-performance piezoelectric ceramics. In this work, a data-driven approach combining feature engineering, statistical learning, machine learning (ML), experimental design, and synthesis is trialed to investigate its accuracy in predicting <i>d</i><sub>33</sub> of potassium–sodium–niobate ((K,Na)NbO<sub>3</sub>, KNN)-based ceramics. The atomic radius (AR), valence electron distance (DV) (Schubert), Martynov–Batsanov electronegativity (EN-MB), and absolute electronegativity (EN) are summarized as the four most representative features in describing <i>d</i><sub>33</sub> out of all 27 possible features for the piezoelectric ceramics. These four features contribute greatly to regression learning for predicting <i>d</i><sub>33</sub> and classification learning for distinguishing polymorphic phase boundary (PPB). The ML method developed in this work exhibits a high accuracy in predicting <i>d</i><sub>33</sub> of the piezoelectric ceramics. An example of KNN combined with 6 mol% LiNbO<sub>3</sub> demonstrates <i>d</i><sub>33</sub> of 184 pC/N, which is highly consistent with the predicted result. This work proposes a novel feature-oriented guideline for accelerating the design of piezoelectric ceramic systems with large <i>d</i><sub>33</sub>, which is expected to be widely used in other functional materials. Electronegativity ; Piezoelectricity ; Ceramic ; Materials science ; Phase boundary ; Piezoelectric coefficient ; Valence (chemistry) ; Artificial intelligence ; Natural bond orbital ; Computer science ; Machine learning ; Phase (matter) ; Physics ; Chemistry ; Computational chemistry ; Composite material ; Density functional theory Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Multiferroics and related materials
Authors: Zhiqiang Li | Xiaoyu Tan | Zhiwei Fu | Linhua Liu | Jia‐Yue Yang
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Thermal transport across copper–water interfaces according to deep potential molecular dynamics Nanoscale thermal transport at solid-liquid interfaces plays an essential role in many engineering fields. This work performs deep potential molecular dynamics (DPMD) simulations to investigate thermal transport across copper-water interfaces. Unlike traditional classical molecular dynamics (CMD) simulations, we independently train a deep learning potential (DLP) based on density functional theory (DFT) calculations and demonstrated its high computational efficiency and accuracy. The trained DLP predicts radial distribution functions (RDFs), vibrational densities of states (VDOS), density curves, and thermal conductivity of water confined in the nanochannel at a DFT accuracy. The thermal conductivity decreases slightly with an increase in the channel height, while the influence of the cross-sectional area is negligible. Moreover, the predicted interfacial thermal conductance (ITC) across the copper-water interface by DPMD is 2.505 × 10<sup>8</sup> W m<sup>-2</sup> K<sup>-1</sup>, the same order of magnitude as the CMD and experimental results but with a high computational accuracy. This work seeks to simulate the thermal transport properties of solid-liquid interfaces with DFT accuracy at large-system and long-time scales. Copper ; Molecular dynamics ; Thermal ; Chemical physics ; Dynamics (music) ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Computational chemistry ; Metallurgy Thermal properties of materials ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Yongjia Xu | Xinzheng Lu | Yifan Fei | Yuli Huang
Source: Advances in Structural Engineering (article, 2023-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hysteretic behavior simulation based on pyramid neural network: Principle, network architecture, case study and explanation An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection. Pyramid (geometry) ; Artificial neural network ; Computer science ; Network architecture ; Architecture ; Feature (linguistics) ; Artificial intelligence ; Computer network Magnetic Properties and Applications ; Force Microscopy Techniques and Applications ; Structural Health Monitoring Techniques
Authors: Santosh Adhikari | Jacob M. Clary | Ravishankar Sundararaman | Charles B. Musgrave | Derek Vigil‐Fowler | Christopher Sutton
Source: Chemistry of Materials (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning We used machine learning (ML) to accurately predict eigenvalues of the hybrid HSE06 functional using eigenvalues computed by the less computationally expensive PBE functional and associated electronic features based on the k-point resolved atomic band character. The ML model was trained by using eigenvalues from only one k-point for each of the 168 compounds in the training set. The HSE06 eigenvalues across all k-points were then predicted for a separate set of 169 compounds with a mean absolute error (MAE) of 0.13 eV, representing a significant improvement over the error of PBE-computed eigenvalues relative to that of HSE06 (MAE = 0.96 eV). These accurately predicted eigenvalues result in remarkably accurate predictions for the band structures, projected density of states, and band gaps, even though the model was not explicitly trained on these other properties. Finally, we demonstrate that our ML model has a similar accuracy for both ternary and quaternary compounds well outside the initial training set and on systems with 112 and 160 atoms, demonstrating its potential to rapidly predict HSE06-quality electronic structures of complex materials that are practically unfeasible for HSE06. Hybrid functional ; Eigenvalues and eigenvectors ; Ternary operation ; Set (abstract data type) ; Point (geometry) ; Dimension (graph theory) ; Training set ; Band gap ; Computer science ; Density functional theory ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics ; Materials science ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Renchao Che | Zhengchen Wu | Bin Quan | Ruixuan Zhang | Huiran Zhang | Jincang Zhang | Wencong Lu
Source: Advanced Functional Materials (article, 2023-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐Directed Fast and High‐Throughput Acquisition of High‐Efficiency Microwave Absorbents From Infinite Design Space Abstract Intrinsic composition properties and extrinsic micro‐/nano‐structural effects constitute the infinite design space of microwave absorption (MA) materials wherein the high‐efficiency performance is expected to advance stealth and anti‐interference technologies. However, restricted to the black box of physical mechanisms, discovering those materials too often relies on the traditional trial‐and‐error methods, falling into the time‐consuming loop between material modification and performance measurement. Herein, an unprecedented machine learning‐based forecasting system (MLFS) is constructed to directly predict the process conditions of carbonyl iron/ferrosoferric oxide hybrids with enhanced MA performance. The high‐throughput screening and inverse projection based on pattern recognition recommend a series of excellent MA materials with the highest performance correlation coefficient up to 0.9844. After manual selection from this set, the enhancement of maximum absorption efficiency and bandwidth of the optimal hybrid reach 207% and 360% in comparison with the original database. The standardized MLFS procedure immensely shortens the research cycle to a few weeks compared to several months of the manual orthogonal experiment. This is believed to be an expressway for accelerating the discovery of high‐performance MA materials and their industrialization. Microwave ; Materials science ; Throughput ; Computer science ; Bandwidth (computing) ; Process engineering ; Machine learning ; Telecommunications Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials
Authors: Ajit Kumar Dash | Hariharan Swaminathan | Ethan Berger | Mainak Mondal | Touko Lehenkari | Pushp Raj Prasad | Kenji Watanabe | Takashi Taniguchi | Hannu‐Pekka Komsa | Akshay Singh
Source: 2D Materials (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

Evidence of defect formation in monolayer MoS<sub>2</sub> at ultralow accelerating voltage electron irradiation Abstract Control on spatial location and density of defects in two-dimensional materials can be achieved using electron beam irradiation. Conversely, ultralow accelerating voltages ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>⩽</mml:mo> </mml:mrow> </mml:math> 5 kV) are used to measure surface morphology, with no expected defect creation. We find clear signatures of defect creation in monolayer MoS 2 at these voltages. Evolution of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mtext>E</mml:mtext> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">′</mml:mi> </mml:mrow> </mml:msup> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mrow> <mml:mtext>A</mml:mtext> </mml:mrow> <mml:mn>1</mml:mn> <mml:mrow> <mml:mi mathvariant="normal">′</mml:mi> </mml:mrow> </mml:msubsup> </mml:mrow> </mml:math> Raman modes with electron dose, and appearance of defect activated peaks indicate defect formation. To simulate Raman spectra of MoS 2 at realistic defect distributions, while retaining density-functional theory accuracy, we combine machine-learning force fields for phonons and eigenmode projection approach for Raman tensors. Simulated spectra agree with experiments, with sulphur vacancies as suggested defects. We decouple defects, doping and carbonaceous contamination using control (hBN covered and encapsulated MoS 2 ) samples. We observe cryogenic photoluminescence quenching and defect peaks, and find that carbonaceous contamination does not affect defect creation. These studies have applications in photonics and quantum emitters. Irradiation ; Monolayer ; Materials science ; Electron beam processing ; Electron ; Optoelectronics ; Voltage ; Nanotechnology ; Electrical engineering ; Physics ; Nuclear physics ; Engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Metal and Thin Film Mechanics
Authors: Paolo Restuccia | Gabriele Losi | Omar Chehaimi | Margherita Marsili | Maria Clelia Righi
Source: ACS Applied Materials & Interfaces (article, 2023-04-04)
Field: Materials Science — Materials Chemistry

High-Throughput First-Principles Prediction of Interfacial Adhesion Energies in Metal-on-Metal Contacts Adhesion energy, a measure of the strength by which two surfaces bind together, ultimately dictates the mechanical behavior and failure of interfaces. As natural and artificial solid interfaces are ubiquitous, adhesion energy represents a key quantity in a variety of fields ranging from geology to nanotechnology. Because of intrinsic difficulties in the simulation of systems where two different lattices are matched, and despite their importance, no systematic, accurate first-principles determination of heterostructure adhesion energy is available. We have developed robust, automatic high-throughput workflow able to fill this gap by systematically searching for the optimal interface geometry and accurately determining adhesion energies. We apply it here for the first time to perform the screening of around a hundred metallic heterostructures relevant for technological applications. This allows us to populate a database of accurate values, which can be used as input parameters for macroscopic models. Moreover, it allows us to benchmark commonly used, empirical relations that link adhesion energies to the surface energies of its constituent and to improve their predictivity employing only quantities that are easily measurable or computable. Adhesion ; Materials science ; Benchmark (surveying) ; Throughput ; Nanotechnology ; Heterojunction ; Workflow ; Interface (matter) ; Energy (signal processing) ; Surface energy ; Biological system ; Computer science ; Composite material ; Physics ; Optoelectronics ; Wireless Machine Learning in Materials Science ; Adhesion, Friction, and Surface Interactions ; Thermal properties of materials
Authors: Xianyang Zhang | Pengfei Chen | Siwuxie He | Bowen Jiang | Yong Wang | Yonghua Cheng | Jian Peng | Francis Verpoort | John Wang | Zongkui Kou
Source: InfoMat (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Single‐atom metal‐nitrogen‐carbon catalysts energize single molecule detection for biosensing Abstract Biosensors featuring single molecule detection present huge opportunities as well as challenges in food safety inspection, disease diagnosis, and environmental monitoring. Single‐molecule detection is largely lacking of high enough activity, precision molecule selectivity, and understanding in the exact operating mechanism. Single‐atom catalysts (SACs), especially those metals‐nitrogen‐carbon that mimic the natural metalloenzyme structure, and with well‐defined metal atom bond configurations, high level of molecular selectivity, and easy fabrication, endow single molecule detections with practical‐use feasibilities. The recent advances in single‐atom catalysts also present new pathways in the key mechanism understandings. In this short review, we will first visit the brief history and advantages of SACs that have been explored only recently for molecule‐scale biosensors, where they are analogous and also differentiated from those nanozymes and natural metalloenzymes. Their applications in electrochemical, photochemical, and photoelectrochemical sensors are then discussed comprehensively by focusing on the different molecule‐scale sensing modes in achieving local coordination‐modulated signal amplifications. Finally, we identify new opportunities and challenges faced by these SACs‐based single molecule detections in the further development of biosensors. image Biosensor ; Molecule ; Nanotechnology ; Catalysis ; Carbon fibers ; Selectivity ; Atom (system on chip) ; Fabrication ; Small molecule ; Carbon nanotube ; Electrochemistry ; Materials science ; Metal ; Chemistry ; Combinatorial chemistry ; Electrode ; Computer science ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications