jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiurong Zhu | Jian‐Min Zuo
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

<i>PyEMAPS</i>: an open source Python package for transmission electron diffraction simulations and crystallographic computing Transmission electron diffraction is a powerful technique for the structure analysis of a broad range of materials at nanometer scale.Computer simulations have played a critical role in the interpretation and quantification of electron diffraction patterns.Diffraction simulation also bridges the gaps between crystallography, electron diffraction theory and electron microscopy practices.The development of data-driven electron microscopy, based four-dimensional scanning transmission electron microscopy (4D-STEM), especially can benefit from the simulation of large diffraction datasets for machine learning, for example [1-3].Several solutions have been developed to meet the needs of electron diffraction simulations [4-7].Here, we introduce an integrated Python package, called PyEMAPS for electron diffraction and crystallography computation and demonstrate its applications for electron crystallography analysis.The package is open source and freely available for education and research.By making electron diffraction simulation and crystallographic computation with ease of use in a popular scripting language, we aim to accelerate research and education in electron diffraction and microscopy.The implementation in Python also opens up to a large repository of packages in its open source communities for advanced computing, such as parallel computing and machine learning. Python (programming language) ; Open source ; Crystallography ; Computer science ; Computational science ; Electron diffraction ; Diffraction ; Materials science ; Physics ; Operating system ; Chemistry ; Optics ; Software Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yimeng Min | Ming-Chiang Chang | Shufeng Kong | John M. Gregoire | R. B. van Dover | Michael O. Thompson | Carla P. Gomes
Source:  (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Physically Informed Graph-Based Deep Reasoning Net for Efficient Combinatorial Phase Mapping Phase mapping is a crucial challenge in materials discovery, which entails determining crystalline phase distribution in condition space based on a collection of X-ray diffraction (XRD) data. This task involves exploring the space of potential phases, identifying existing phases, and determining their respective weight distribution in the condition space while adhering to strict physics constraints. In recent years, there has been a growing interest in leveraging machine learning (ML) techniques to tackle the phase mapping problem. ML methods offer the potential to handle larger and more complex phase mapping instances and provide enhanced accuracy compared to traditional approaches. Among promising ML approaches, DRNets, which formulates the phase mapping problem as an unsupervised pattern demixing problem, represents the current state of the art. Despite its practical effectiveness, DRNets does have certain limitations. For instance, it employs a single multiplicative factor to calculate the stick locations in XRD patterns, which may not accurately reflect the underlying physics of X-ray diffraction. Additionally, DRNets relies on an expensive path-based schema to enforce phase weight smoothness. To overcome these limitations, we propose a novel approach called Physically-informed Graph-based DRNet (PG-DRNet). PG-DRNet incorporates a physical decoder that estimates the crystals' lattice parameters and reconstructs XRD patterns based on Bragg's law. Additionally, we introduce a graph-based schema to enforce phase weight smoothness as well as lattice and peak intensity shift. This graph-based schema provides several advan-tages, including improved computational efficiency compared to the path-based schema utilized in DRNets. To thoroughly evaluate the effectiveness of our approach, we conducted experiments on various chemical systems. Notably, our evaluation went beyond the scope of previous studies that solely focused on varying compositions and extends to explore the additional dimensions of varying annealing time and temperature conditions. Our results demonstrate that PG-DRNet achieves higher accuracy, lower reconstruction loss and significantly faster performance when compared to DRNet results. Computer science ; Graph ; Phase (matter) ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Vinu Unnikrishnan | Vikas Varshney | Landon Gaber | J. Castle | Ajit K. Roy
Source:  (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING BASED STRUCTURAL AND FAILURE CHARACTERISTICS OF CARBON NANOTUBE JUNCTION CONFIGURATIONS Carbon nanotube based multi-terminal junction configurations are of great interest because of their potential aerospace and electronics applications. The performance of nanodevices based on these carbon nanotube junctions can be affected by the structural properties of the topologically accurate junction morphology like mechanical strength, thermal and electrical conductivities. It is therefore necessary to be able to investigate and characterize these properties accurately and quickly such that optimum structural configurations can be intelligently configured for the desired function. It is in this scenario that traditional atomistic simulations encounter severe limitations in terms of computational time required and hence the use of artificial intelligence techniques becomes necessary. In this work, we present a Machine Learning based characterization of structural and failure properties of carbon nanotube junction configurations. Structural and failure properties of a large set of 3D nanotube (CNT) junction configurations are first studied using molecular dynamics (MD) simulations. The stress-strain data until failure for each of the carbon nanotube junction configuration was estimated and various structural properties were used in the generation of the dataset. This dataset was used in a gradient boosting-based machine learning algorithm for the characterization of mechanical and failure properties of the nanotube junctions. Gradient boosting algorithms are prediction-based models that consists of sequential ensemble learning techniques in which the overall performance of the model is enhanced over multiple iterations. The coefficient of determination (R2) value is used to evaluate the robustness of the developed models for structural and failure parameters identified earlier. The machine learning techniques can be effective in predicting the structural properties of nanotube junction configurations with greater confidence. Robustness (evolution) ; Carbon nanotube ; Materials science ; Computer science ; Boosting (machine learning) ; Nanotube ; Artificial intelligence ; Characterization (materials science) ; Nanotechnology ; Algorithm Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Zhao, Ziyuan
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Physically Interpretable Biomolecular Conformation Generation with A Deep Probabilistic Framework The structure of proteins is highly dynamic, and their ability to undergo conformational changes is crucial for their biological function. While deep learning has shown success in predicting static 3D structures of proteins from their amino acid sequence, efficiently sampling various conformations and transition states between them remains a challenge. To address this issue, we propose a novel framework that combines theories and methods from coarse-graining and deep learning communities to generate biomolecular conformations in an efficient and physically interpretable manner. Our model learns a coarse-grained mechanical representation of alanine dipeptide as an elastic network of interconnected harmonic springs and generates novel but realistic conformations from this representation. We demonstrate that our model also preserves the essential physics of the molecular system by learning the correspondence of dihedral angles between coarse-grained and fully-atomistic conformations. Finally, we use our model to explore interpolating between two distant metastable conformations of alanine dipeptide by mechanically manipulating the coarse-grained conformations, generating smooth and physically plausible transition pathways implied by the forcefield but not present in our training data. Our work contributes to the field of molecular modeling by providing an efficient and parsimonious framework to sample from the conformational landscape of complex biomolecules, which can help in understanding the mechanisms of biological processes, accelerating drug discovery, and advancing the field of protein design. Conformational ensembles ; Dipeptide ; Representation (politics) ; Dihedral angle ; Deep learning ; Probabilistic logic ; Field (mathematics) ; Force field (fiction) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Crystallography and molecular interactions
Authors: Ri He | Hua Wang | Fucai Liu | Shi Liu | Houfang Liu | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2023-05-15)
Field: Materials Science — Materials Chemistry

Unconventional ferroelectric domain switching dynamics in CuInP2S6 from first principles The switching dynamics of ferroelectric materials is a crucial intrinsic property which directly affects the operation and performance of ferroelectric devices. In conventional ferroelectric materials, the typical ferroelectric switching mechanism is governed by a universal process of domain wall motion. However, recent experiments indicate that Van der Waals ferroelectric CuInP2S6 possesses anomalous polarization switching dynamics under an electric field. It is important to understand the switching dynamics, but it remains theoretically unexplored in CuInP2S6 due to the lack of description of its order-disorder phase transition characteristics by density functional theory. Here, we employ a machine-learning potential trained from the first principles density functional theory dataset to conduct the large-scale atomistic simulations of temperature-driven order-disorder ferroelectric phase transition in CuInP2S6. Most importantly, it is found that the electric field-driven polarization switching in CuInP2S6 is mediated by single Cu dipole flip, rather than conventional domain wall motion mechanism. This intrinsic unconventional switching behavior can be attributed to the competition between the energy barrier of domain wall motion and single dipole flip. Ferroelectricity ; Polarization (electrochemistry) ; Electric field ; Condensed matter physics ; Materials science ; Dipole ; Phase transition ; Domain wall (magnetism) ; van der Waals force ; Physics ; Optoelectronics ; Chemistry ; Dielectric ; Quantum mechanics ; Magnetic field Solid-state spectroscopy and crystallography ; Multiferroics and related materials ; Machine Learning in Materials Science
Authors: Jiapeng Sun | Hanyang Li | Yu Zhang | Jin‐Hua Mou | Jiaxing Gao | Zhihai Liu
Source:  (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

A non-volatile bipolar optical fiber memory based on phase change materials All-photon memory, holding significant potential for applications in optical communication systems and neural network computing, and developing an all-optical dual-channel fiber storage platform that achieves integrated storage and computation is challenging. In this paper, a non-volatile, high-contrast, and highly repeatable bipolar memory is demonstrated, achieved by integrating two tapered fibers with a fiber microsphere containing phase change materials(PCMs). Employing an external laser modulation technique, repeatable or randomly accessible 6-level data storage is enabled by altering the state of Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST). Multi-stage writing is accomplished using a 532 nm pump laser witha10 ns pulse width and laser energy ranging from 0.423 mJ to 1.206 mJ, while a 793 nm continuous wave (CW) laser with an average power of 4 mW to 11 mW is utilized for the multi-stage reset process. Exhibiting a write response time of 75ns, a reset response time of 180 ns, and a contrast of 18 dB, the bipolar memory preliminarily realizes the synaptic weight update mechanism in the synapse of neural network systems Reset (finance) ; Materials science ; Optical fiber ; Optoelectronics ; Phase-change memory ; Fiber ; Laser ; Computer science ; Optics ; Nanotechnology ; Telecommunications ; Physics Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: Ankush Checkervarty | Jens‐Uwe Sommer | Marco Werner
Source: The Journal of Chemical Physics (article, 2023-01-24)
Field: Materials Science — Materials Chemistry

Machine learning of an implicit solvent for dynamic Monte Carlo simulations The Bond Fluctuation Model (BFM) is a highly efficient and versatile method for simulating polymers, membranes, and soft matter. Due to its coarse-grained nature, the BFM is employed to understand the universal properties of polymers. Solvent effects are often mediated by explicit solvent particles, while implicit solvent models have had limited use as they may lead to frozen states and, thus, ergodicity-related problems. In simulation setups, such as coagulated multiple homopolymers chains, explicit solvent models are computationally expensive because the region of interest can be localized in a small space compared to the dimension of the periodic box. We introduce an implicit solvent model based on an artificial neural network (NN) that was trained with BFM simulation data for single homopolymers in an explicit solvent. We demonstrate that NN-based simulations that take into account only the information of the local environment of monomers reproduce the expected universal macroscopic properties of the polymer under varying solvent conditions. The homopolymer chains simulated using the NN reproduce the coil-globule transition, the static and dynamic bond autocorrelation, and the mean square displacement of chain monomers. We show that the learned parameters from a single chain system can be transferred to a system containing multiple homopolymers, indicating that the learned parameters are transferable to considerably different systems. Statistical physics ; Solvent ; Monte Carlo method ; Molecular dynamics ; Polymer ; Artificial neural network ; Materials science ; Solvent models ; Monomer ; Chain (unit) ; Autocorrelation ; Computer science ; Chemical physics ; Thermodynamics ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Artificial intelligence ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Danish Khan | Stefan Heinen | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-03-28)
Field: Materials Science — Materials Chemistry

Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations The feature vector mapping used to represent chemical systems is a key factor governing the superior data-efficiency of kernel based quantum machine learning (QML) models applicable throughout chemical compound space. Unfortunately, the most accurate representations require a high dimensional feature mapping, thereby imposing a considerable computational burden on model training and use. We introduce compact yet accurate, linear scaling QML representations based on atomic Gaussian many-body distribution functionals (MBDF), and their derivatives. Weighted density functions (DF) of MBDF values are used as global representations which are constant in size, i.e.~invariant with respect to the number of atoms. We report predictive performance and training data efficiency that is competitive with state of the art for two diverse datasets of organic molecules, QM9 and QMugs. Generalization capability has been investigated for atomization energies, HOMO-LUMO eigenvalues and gap, internal energies at 0 K, zero point vibrational energies, dipole moment norm, static isotropic polarizability, and heat capacity as encoded in QM9. MBDF based QM9 performance lowers the optimal Pareto front spanned between sampling and training cost to compute node minutes,~effectively sampling chemical compound space with chemical accuracy at a sampling rate of $\sim 48$ molecules per core second. Kernel (algebra) ; Computer science ; Mathematics ; Topology (electrical circuits) ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Burcu Üstün | Hamide Aydın | Serkan Naci Koç | Ahmet Uluslu | Ü. Kurtan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrospun Nitrogen Enriched Pei-Derived Carbon Nanofiber for Supercapacitors with Artificial Neural Network Modeling Supercapacitor ; Nanofiber ; Artificial neural network ; Materials science ; Nitrogen ; Carbon nanofiber ; Nanotechnology ; Artificial intelligence ; Chemical engineering ; Polymer science ; Computer science ; Chemistry ; Organic chemistry ; Carbon nanotube ; Engineering ; Capacitance Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications
Authors: Olaiyan Alolaiyan | Shahad Albwardi | Sarah Alsaggaf | Thamer Tabbakh | Frank W. DelRio‬ | Moh. R. Amer
Source: arXiv (Cornell University) (preprint, 2023-12-14)
Field: Materials Science — Materials Chemistry

Unlocking High Performance, Ultra-Low Power Van der Waals Transistors: Towards Back-End-of-Line In-Sensor Machine Vision Applications Recent reports on machine learning (ML) and machine vision (MV) devices have demonstrated the potentials of 2D materials and devices. Yet, scalable 2D devices are being challenged by contact resistance and Fermi Level Pinning (FLP), power consumption, and low-cost CMOS compatible lithography processes. To enable CMOS+2D, it is essential to find a proper lithography strategy that can fulfill these requirements. Here, we explore modified van der Waals (vdW) deposition lithography and demonstrate a relatively new class of van-der-Waals-Field-Effect-Transistors (vdW-FETs) based on 2D materials. This lithography strategy enables us to unlock high performance devices evident by high current on-off ratio (Ion/Ioff), high turn-on current density (Ion), and weak Fermi Level Pinning (FLP). We utilize this approach to demonstrate a gate-tunable near-ideal diode using MoS2/WSe2 heterojunction with an ideality factor of ~1.65 and current rectification of 102. We finally demonstrate a highly sensitive, scalable, and ultra-low power phototransistor using MoS2/ WSe2 vdW-FET for Back-End-of-Line (BEOL) integration. Our phototransistor exhibits the highest gate-tunable photoresponsivity achieved to date for white light detection with ultra-low power dissipation, enabling ultra-sensitive, ultra-fast, and efficient optoelectronic applications such as in-sensor neuromorphic machine vision. Our approach shows the great potential of modified vdW deposition lithography for back-end-of-line CMOS+2D applications. Optoelectronics ; CMOS ; Transistor ; Rectification ; Materials science ; Lithography ; van der Waals force ; Photolithography ; Photodiode ; Back end of line ; Nanotechnology ; Electrical engineering ; Physics ; Voltage ; Engineering 2D Materials and Applications ; Graphene research and applications ; Nanowire Synthesis and Applications
Authors: Emanuel P. Santos | Roberta S. Pugina | Eloísa G. Hilário | Alyson J. A. Carvalho | Carlos Jacinto | Francisco A. M. G. Rego-Filho | Askery Canabarro | Anderson S. L. Gomes | José Maurício A. Caiut | André L. Moura
Source: arXiv (Cornell University) (preprint, 2023-06-22)
Field: Materials Science — Materials Chemistry

Towards accurate real-time luminescence thermometry: an automated machine learning approach Luminescence thermometry has been extensively exploited in the last decades both from the fundamental and applied point of views. The application of photoluminescent nanoparticles on the microscopic level based on rare-earth doped (RED) nanostructures is yet a challenge. Distinct underlying physical mechanisms in the RED nanomaterials have been exploited, such as intensity ratio between radiative transitions associated with thermally coupled energy levels, energy peak and lifetime of an excited state variations with the temperature. The drawbacks of such systems are the relatively low thermal sensitivity (Sr), and the large temperature uncertainty. To overcome that, several research groups have been seeking new functionalized materials. The majority of the efforts have been directed towards increasing Sr with record around 10 %°C-1, which is, however, considered unsatisfactory. We propose the use of an automated machine learning tool to retrieve an ideal pipeline improving the response of photoluminescence thermometers. As a proof-of-concept, we used Nd3+-doped YAG nanoparticles, excited at 760 nm, and the photoluminescence spectra in the range from 860 nm to 960 nm as input parameters. In addition to the improvement in the accuracy (&gt; 5.5x over traditional methods), the implementation is very simple, without the requirement of any deconvolution procedure or knowledge of any underlying physical mechanism. Our findings demonstrate that this approach is resilient to natural variances across various spectral acquisitions, which may otherwise lead to an inaccurate estimation of temperature, opening the door for real-time applications. Our open-source code is designed to be accessible to individuals without programming experience. Photoluminescence ; Luminescence ; Excited state ; Materials science ; Computer science ; Nanomaterials ; Nanotechnology ; Optoelectronics ; Energy (signal processing) ; Deconvolution ; Artificial intelligence ; Algorithm ; Physics ; Atomic physics Luminescence Properties of Advanced Materials ; Optical properties and cooling technologies in crystalline materials ; Machine Learning in Materials Science
Authors: Man Costinela Isabela | Dragoş Lucian Isac | Raluca Jalba | Ștefan Gabriel Soriga | Yuheng Zhao | Ionut Trancă | Frederik Tielens
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Computational Investigation of the Oxygen Reduction Reaction on the Edges of Differently-Sized, Shaped and Terminated Graphene Nanoclusters Nanoclusters ; Graphene ; Oxygen reduction reaction ; Reduction (mathematics) ; Materials science ; Oxygen reduction ; Oxygen ; Nanotechnology ; Chemical engineering ; Chemistry ; Physical chemistry ; Mathematics ; Engineering ; Geometry ; Organic chemistry ; Electrochemistry ; Electrode Graphene research and applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science