jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ping Guo | Hailin Miao | Yanbo Zou | Shifeng Mao | Zejun Ding
Source: arXiv (Cornell University) (preprint, 2023-11-25)
Field: Materials Science — Surfaces, Coatings and Films

Application of Machine Learning Method to Model-Based Library Approach to Critical Dimension Measurement by CD-SEM The model-based library (MBL) method has already been established for the accurate measurement of critical dimension (CD) of semiconductor linewidth from a critical dimension scanning electron microscope (CD-SEM) image. In this work the MBL method has been further investigated by combing the CD-SEM image simulation with a neural network algorithm. The secondary electron linescan profiles were calculated at first by a Monte Carlo simulation method, enabling to obtain the dependence of linescan profiles on the selected values of various geometrical parameters (e.g., top CD, sidewall angle and height) for Si and Au trapezoidal line structures. The machine learning methods have then been applied to predicate the linescan profiles from a randomly selected training set of the calculated profiles. The predicted results agree very well with the calculated profiles with the standard deviation of 0.1% and 6% for the relative error distributions of Si and Au line structures, respectively. This result shows that the machine learning methods can be practically applied to the MBL method for the purpose of reducing the library size, accelerating the construction of the MBL database and enriching the content of an available MBL database. Critical dimension ; Laser linewidth ; Scanning electron microscope ; Monte Carlo method ; Dimension (graph theory) ; Artificial neural network ; Algorithm ; Computer science ; Materials science ; Artificial intelligence ; Statistical physics ; Optics ; Physics ; Mathematics ; Statistics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advancements in Photolithography Techniques
Authors: M. Lal | Akashdeep Singh | Ryan Mzik | Amirmasoud Lanjan | Seshasai Srinivasan
Source: Preprints.org (preprint, 2023-11-28)
Field: Materials Science — Materials Chemistry

AI-based Nano-scale Material Property Prediction For Li-Ion Batteries Molecular dynamics (MD) simulations present a sophisticated nano-scale computational approach that can play a critical role in material design for next generation batteries. One critical piece of information needed for MD simulations is the non-bonded potential parameters, which can be obtained through quantum mechanics (QM) calculations or experimental methods. However, experimental data is not available for exploratory and novel materials to derive the potential parameters. Also, the QM approach needs significant computational power and is too time consuming for large systems. On the other hand, current Artificial Intelligence techniques are faster, but they cannot be generalized across molecules and properties. As a result, there is a significant barrier to discovering and exploring new materials. Overcoming this barrier, in this work, we propose a Machine Learning (ML) based technique that can learn inter-atomic potential parameters for various particle-particle interactions employing QM calculations. This ML model can be used as an alternative for QM calculations for predicting non-bonded interactions in a computationally efficient manner. Using these parameters as input to MD simulations, we can predict a diverse range of properties, enabling researchers to design new and novel materials suitable for various applications in the absence of experimental data. We employ our ML-based technique to learn the Buckingham potential, a non-bonded interatomic potential. Subsequently, we utilize these predicted values to compute the densities of four distinct molecules, achieving an accuracy exceeding 93%. This serves as a strong demonstration of the efficacy of our proposed approach. Range (aeronautics) ; Computer science ; Molecular dynamics ; Scale (ratio) ; Interatomic potential ; Work (physics) ; Particle (ecology) ; Statistical physics ; Materials science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Dan Hu | Yingli Liu | Tao Shen
Source:  (article, 2023-07-30)
Field: Materials Science — Materials Chemistry

Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction. Alloy ; Computer science ; Materials science ; Silicon ; Metallurgy Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Aluminum Alloy Microstructure Properties
Authors: Haoyu Li | Shichang Zhang | Longwen Tang | Mathieu Bauchy | Yizhou Sun
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks Metallic Glasses (MGs) are widely used materials that are stronger than steel while being shapeable as plastic. While understanding the structure-property relationship of MGs remains a challenge in materials science, studying their energy barriers (EBs) as an intermediary step shows promise. In this work, we utilize Graph Neural Networks (GNNs) to model MGs and study EBs. We contribute a new dataset for EB prediction and a novel Symmetrized GNN (SymGNN) model that is E(3)-invariant in expectation. SymGNN handles invariance by aggregating over orthogonal transformations of the graph structure. When applied to EB prediction, SymGNN are more accurate than molecular dynamics (MD) local-sampling methods and other machine-learning models. Compared to precise MD simulations, SymGNN reduces the inference time on new MGs from roughly 41 days to less than one second. We apply explanation algorithms to reveal the relationship between structures and EBs. The structures that we identify through explanations match the medium-range order (MRO) hypothesis and possess unique topological properties. Our work enables effective prediction and interpretation of MG EBs, bolstering material science research. Invariant (physics) ; Graph ; Computer science ; Convolutional neural network ; Artificial neural network ; Representation (politics) ; Theoretical computer science ; Graph property ; Artificial intelligence ; Algorithm ; Physics ; Voltage graph ; Quantum mechanics ; Line graph Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Graph Neural Networks
Authors: Ali Davariashtiyani | Sara Kadkhodaei
Source: Research Square (preprint, 2023-06-29)
Field: Materials Science — Materials Chemistry

Voxel Image of Crystals for High-Throughput Materials Screening: Formation Energy Prediction by a Deep Convolutional Network Throughput ; Voxel ; Computer science ; Artificial intelligence ; Convolutional neural network ; Energy (signal processing) ; Deep learning ; Image (mathematics) ; Materials science ; Telecommunications ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Authors: Kehan Wang | Longkun Xu | Wei Shao | Haishun Jin | Qiang Wang | Ming Ma
Source: ChemRxiv (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

A multiple-fidelity Method for Accurate Simulation of MoS 2 Properties Using JAX-ReaxFF and Neural Network Potentials Reactive force field (ReaxFF) is one of the most commonly used force field to model the chemical reactions on atomic level. Recently, JAX-ReaxFF, combined with auto- matic differentiation, has been used to efficiently parameterize ReaxFF. However, pre- dicted properties using parameterized ReaxFF may be inaccurate due to the inductive bias of its analytical formula. While neural network-based potentials (NNPs) trained on density functional theory (DFT)-labeled data offer a more accurate method, it re- quires a large amount of training data to be trained from scratch. To overcome these issues, we present a multiple-fidelity method that combines JAX-ReaxFF and NNP, and apply the method on MoS 2 , a promising two-dimensional (2D) semiconductor for flexible electronics due to its excellent mechanical, optical, and electronic properties. By optimizing ReaxFF for MoS 2 and incorporating implicit prior physical information in the functional forms, we show that ReaxFF can serve as a cost-effective way to generate pretraining data, facilitating more accurate simulations of MoS 2 properties, such as the convex hull diagram, sulfur vacancy formation, and interaction with S 8 using SchNet. Moreover, in the Mo-S-H multi-element system, the pretraining strat- egy can reduce root-mean-square errors(RMSE) of energy by 20%. This approach can be extended to a wide variety of material systems, accelerating their computational research. ReaxFF ; Force field (fiction) ; Parameterized complexity ; Computer science ; Fidelity ; Anatase ; Molecular dynamics ; Chemistry ; Computational chemistry ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; 2D Materials and Applications
Authors: Feng Guo | Zhong-Hao Ye | Chuanguo Chai | Yushi Wen
Source: Research Square (preprint, 2023-07-11)
Field: Materials Science — Materials Chemistry

ReaxFF-nn: A Reactive Machine Learning Potential in GULP and the Applications in the Thermal Conductivity Calculation of Carbon Nanostructures Abstract ReaxFF -nn stands for Reactive Force Field ( ReaxFF ) with neural networks and is currently added to the General Utility Lattice Program (GULP) by modern FORTRAN programming. With GULP and ReaxFF -nn with parameters that are trained by our I-ReaxFF package, the thermal properties, crystal properties, energy minimization, etc., can be done with precision at the quasi-density functional theory (DFT) level. Compared to other Machine Learning Potentials (MLPs), we do not construct a thoroughly new machine learning potential, but just simply used a small neural network for the bond order and bond energy calculations, and the uncorrected bond orders are chosen as the input atomic feature vector. The advantage of To validate the model which we have coded in the GULP, we compared the forces of a random structure between the auto-differentiate package and our FORTRAN code, and the difference between them is about 10 -6 . Furthermore, we provided a systematical study of the thermal conductivity ( κ ) of graphene and carbon nanotubes (CNTs) through the phonon Boltzmann transport equation. The value of κ thermal conductivity of graphene is very close to the DFT calculations. Therefore, we declare that the potential we have trained for sp2 carbon can reach the quasi-DFT level. In this work, we report the thermal conductivity of CNTs calculated by ReaxFF-nn at quasi-DFT level are range from 107.032 to 310.019 W.m -1 .K -1 . Thermal conductivity ; Nanostructure ; ReaxFF ; Conductivity ; Carbon fibers ; Materials science ; Nanotechnology ; Chemistry ; Chemical engineering ; Engineering ; Physical chemistry ; Computational chemistry ; Composite material ; Molecular dynamics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Prof. Linda C. Thompson
Source: American Journal of Materials Engineering (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Scholarly Article: Materials For Energy Conversion: Challenges And Prospects Materials science is central to the advancement of energy conversion technologies critical for achieving global sustainability goals. This article provides an overview of the current challenges and future prospects in the development of functional materials for energy conversion applications, such as solar photovoltaics, fuel cells, thermoelectrics, and batteries. The complex interplay between electronic structure, thermodynamic stability, and interfacial phenomena necessitates multidisciplinary strategies for material design. Key issues include material degradation, cost scalability, and eco-sustainability. Emerging paradigms such as machine learning-driven material discovery and hybrid organic-inorganic systems offer promising directions. This review highlights recent advances while emphasizing the need for integrated approaches that bridge experimental, theoretical, and computational insights. Multidisciplinary approach ; Sustainability ; Energy transformation ; Energy (signal processing) ; Bridge (graph theory) ; Key (lock) ; Grand Challenges ; Fuel cells Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Chemical and Physical Properties of Materials
Authors: Chuyu Liu | Zhensen Chen | Yibing Chen | Xubin Chai | Zhongsen Li | Xiaolei Song
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

FAR-CEST: Fast Acquisition and Reconstruction for Chemical Exchange Saturation Transfer (CEST) imaging using a Deep-Learning Approach We developed a Fast Acquisition and Reconstruction CEST (FAR-CEST) method at 3T human scanners, based on a deep learning approach. A 10X accelerated acquisition was achieved, which under-sampled K-space using a randomized Cartesian pattern of variable density. To fully utilize the correlation among saturation offset dimension, especially to compensate for sparsely-sampled K-space edge, a 3D-Res-Unet model was trained for reconstruction. Results on healthy adult brain suggested that FAR-CEST can produce high quality saturation-weighted images and Z-spectra，but the CEST contrast slightly altered. The highly-acceleration feature of FAR-CEST has been initially validated, yet still require improvement on reconstruction accuracy. Artificial intelligence ; Offset (computer science) ; Cartesian coordinate system ; Iterative reconstruction ; Computer science ; Pattern recognition (psychology) ; Magnetic resonance imaging ; Nuclear magnetic resonance ; Physics ; Mathematics ; Radiology ; Medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Yeskendir Koishekenov | Erik J. Bekkers
Source: arXiv (Cornell University) (preprint, 2023-05-03)
Field: Materials Science — Materials Chemistry

An Exploration of Conditioning Methods in Graph Neural Networks The flexibility and effectiveness of message passing based graph neural networks (GNNs) induced considerable advances in deep learning on graph-structured data. In such approaches, GNNs recursively update node representations based on their neighbors and they gain expressivity through the use of node and edge attribute vectors. E.g., in computational tasks such as physics and chemistry usage of edge attributes such as relative position or distance proved to be essential. In this work, we address not what kind of attributes to use, but how to condition on this information to improve model performance. We consider three types of conditioning; weak, strong, and pure, which respectively relate to concatenation-based conditioning, gating, and transformations that are causally dependent on the attributes. This categorization provides a unifying viewpoint on different classes of GNNs, from separable convolutions to various forms of message passing networks. We provide an empirical study on the effect of conditioning methods in several tasks in computational chemistry. Computer science ; Concatenation (mathematics) ; Theoretical computer science ; Artificial intelligence ; Graph ; Categorization ; Machine learning ; Artificial neural network ; Flexibility (engineering) ; Node (physics) ; Mathematics ; Arithmetic Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Naoki Matsuda | Xiaobo Han | Ikuro Suzuki
Source: Proceedings for Annual Meeting of The Japanese Pharmacological Society (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Prediction method for chemically-induced pain based on deep learning of typical pain-related channels response in peripheral neurons Drug-induced peripheral neuropathy occurs as an adverse reaction of chemotherapy. However, a highly accurate method for assessing peripheral neuropathy and pain caused by compounds has not been established. The microelectrode array (MEA) assay using human induced pluripotent stem cell (iPSC)-derived neurons is expected to be one of the in vitro assays for predicting the mechanism of action (MoA) of pharmaceuticals. However, it is difficult to detect the reactions of drugs with different modes of action with a single parameter, and the analysis method is a challenge. To address this issue, acquiring more detailed information on neuronal network activity focused on individual cells is considered effective. In this study, human iPSC-derived sensory neurons and rat DRG neurons were cultured on 236,880 electrode CMOS-MEA, and the precise electrical activity of individual neurons was acquired. The relationship between the spontaneous activity pattern of peripheral neurons and the induced response pattern to agonists of TRPV1, TRPA1, and TRPM8, respectively, was analyzed. As a result, differences in agonist types were visualized by UMAP analysis based on spike patterns. Next, we developed a pain prediction AI that learned UMAP coordinate information and agonist type. The developed model predicted agonist type and concentrations with 81% accuracy. The CMOS-MEA, which can acquire precise electrical activity of single neurons, can increase the parameters that can detect the effects of drugs, making it effective as a method for predicting the MoA of compounds. Multielectrode array ; Agonist ; Induced pluripotent stem cell ; Sensory system ; Neuroscience ; Electrophysiology ; TRPV1 ; Microelectrode ; Chemistry ; Computer science ; Biology ; Receptor ; Electrode ; Transient receptor potential channel Conducting polymers and applications ; Neuroscience and Neural Engineering
Authors: Alex J. Lee | Joshua A. Rackers | William P. Bricker
Source: Biophysical Journal (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Applications of machine-learned electron densities of nucleic acids Electron ; Electron density ; Statistical physics ; Ab initio ; Computer science ; Scale (ratio) ; Quantum ; Basis (linear algebra) ; Physics ; Biological system ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Various Chemistry Research Topics
Authors: Gregor Maier | Jan Hamaekers | Dominik-Sergio Martilotti | Benedikt Ziebarth
Source: arXiv (Cornell University) (preprint, 2023-08-18)
Field: Materials Science — Ceramics and Composites

Predicting Properties of Oxide Glasses Using Informed Neural Networks Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young's modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models. Extrapolation ; Artificial neural network ; Machine learning ; Property (philosophy) ; Artificial intelligence ; Computer science ; Set (abstract data type) ; Sodium oxide ; Materials science ; Oxide ; Mathematics ; Statistics Glass properties and applications ; Cultural Heritage Materials Analysis
Authors: Thossaporn Wijakmatee | 耕彦 織田 | Yasuhiko Orita | 裕介 下山 | Yusuke Shimoyama
Source: Institutional Repositories DataBase (IRDB) (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Prediction of surface-modified iron oxide nanoparticles extraction from reaction field using solubility parameters and machine learning Extraction (chemistry) ; Solubility ; Nanoparticle ; Iron oxide nanoparticles ; Field (mathematics) ; Iron oxide Machine Learning in Materials Science ; Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods