jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyeok Jin Ko | Min Ha Cho | Hong‐Kyu Kim | Yong‐Seok Choi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Li-Ion Transport Kinetics of Li10gep2s12 Solid Electrolyte and its Response to Isovalent Cation Substitutions (X = C, Co, Pb, Ti, Sb, Nb, Mn, Bi): Density Functional Theory and Machine-Learning-Assisted Molecular Dynamics Study Density functional theory ; Electrolyte ; Kinetics ; Ion ; Materials science ; Chemistry ; Inorganic chemistry ; Crystallography ; Physical chemistry ; Computational chemistry ; Electrode ; Physics ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal and Kinetic Analysis
Authors: Rodolfo S. M. Freitas | Cheng Chen | Xi Jiang
Source:  (preprint, 2024-01-23)
Field: Materials Science — Materials Chemistry

Liquid synthetic fuels design guided by chemical structure: A machine learning perspective Physicochemical properties of synthetic fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning (ML) models are constructed to discover intrinsic chemical structure-properties relationships. The models are trained using data from molecular dynamics (MD) simulations. The fuel structure is represented by molecular descriptors. Such a symbolic representation of the fuel molecule allows to link important features of the fuel composition with key properties of fuel utilization. The results show that the present approach can predict accurately the fuel properties of a wide range of pressure and temperature conditions. Perspective (graphical) ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Shubin Liu
Source: UNC Libraries (article, 2024-09-14)
Field: Chemistry — Physical and Theoretical Chemistry

Harvesting Chemical Understanding with Machine Learning and Quantum Computers It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of in silico simulations in the next few decades. Computer science ; Quantum chemical ; Human–computer interaction ; Chemistry Various Chemistry Research Topics
Authors: Andrey A. Stepashkin | Nikita Yu Nikitin | Pavel Peretyagin | Diluz Chukov
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — General Materials Science

Statistical Analysis and Prediction of Mechanical Properties of Composite Materials Based on Thermoplastic Polymers Reinforced with Continuous Carbon Fibers Based on Convolutional Neural Network Composite material ; Materials science ; Composite number ; Thermoplastic composites ; Thermoplastic ; Convolutional neural network ; Thermoplastic polymer ; Polymer ; Statistical analysis ; Computer science ; Artificial intelligence ; Mathematics ; Statistics Material Properties and Applications ; Engineering Technology and Methodologies ; Engineering Diagnostics and Reliability
Authors: Yasufumi Sakai | Naoki Matsumura | Atsuki Inoue | Hiroshi Kawaguchi | Dang Van Thang | Atsushi Ishikawa | Árni Björn Höskuldsson | Egill Skúlason
Source:  (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Active Learning for Graph Neural Networks Training in Catalyst Energy Prediction The electrification of major industrial processes constitutes an important step in reducing global carbon emissions. Thus, the identification of materials able to serve as catalysts for these processes is of great interest. To this end, materials informatics is increasingly being used to accelerate material discovery. The reactivity of a given material is determined by its electronic structure, i.e., by the availability and distribution of electrons, which nowadays is calculated using computationally demanding quantum mechanics-based density functional theory (DFT) simulations, wherein the total electronic energy is a key observable. Recently, the use of graph neural networks (GNNs) has been proposed as a viable path towards faster energy prediction. However, obtaining enough labeled data for GNN training is challenging due to the extensive DFT calculations required. In this work, we propose active learning for GNN training, aiming to improve the energy prediction accuracy of GNNs with less labeled training data. In the search for materials, promising materials are discovered from a vast array of material data, each with diverse structures and energies. Therefore, GNNs for material search should be trained on data that captures this diversity. To sample such data our method calculates the Euclidean distances among the expanded features, which include the structure and energy information of the material data that are sampled to generate the training data. Then, the data used for GNN training are sampled from the candidates in the order of the longest of the minimum distances calculated for each feature. We demonstrate the superiority of our proposed method using the energy prediction GNNs of PaiNN and EquiformerV2 on various catalyst datasets, including the published Open Catalyst 2022 dataset. When compared to existing active learning methods, GNNs trained with the proposed active learning method achieve a lower mean absolute error in predicted energy using less training data under all experimental conditions. Computer science ; Artificial neural network ; Training (meteorology) ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Machine Learning and Algorithms ; Topic Modeling
Authors: Shengxi Huang
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Designer 2D Materials and Machine-Learning Assisted Characterization 2D materials offer enormous opportunities to build designer structures with widely tunable properties. The precise atomic engineering and quick characterization approach are critical to advance the application of designer 2D materials. This talk will introduce new optical features of designer 2D materials [1-4] with a focus on atomic substitution in monolayer WS 2 [1]. The engineering of 2D materials presents unique opportunities for optoelectronic device and quantum information platform. When designing optoelectronic devices of 2D materials, spectroscopic permittivity of 2D material is a key parameter. While ellipsometry has been used to measure permittivity, it requires simple device structure, non-trivial parameter fitting, and special setup. This talk will also present a new machine-learning assisted approach to measure permittivity of 2D materials embedded in complex device structures without model fitting, which can facilitate a quick, accurate, and in-situ characterization of 2D and other thin film materials [5]. References [1] Q. Qian, et al. “Photoluminescence Induced by Substitutional Nitrogen in Single-Layer Tungsten Disulfide,” ACS Nano , 16 (5), 7428-7437 (2022). [2] K. Zhang, et al. “Enhancement of van der Waals Interlayer Coupling Through Polar Janus MoSSe,” Journal of the American Chemical Society , 142 (41), 17499-17507 (2020). [3] K. Zhang, et al. “Spectroscopic signatures of interlayer coupling in Janus MoSSe/MoS2 heterostructures,” ACS Nano , 15 (9), 14394-14403 (2021). [4] Q. Qian, et al. “Chirality-Dependent Second Harmonic Generation of MoS2 Nanoscroll with Enhanced Efficiency,” ACS Nano , 14 (10), 13333–13342 (2020). [5] Z. Wang, et al. “Measuring complex refractive index through deep-learning-enabled optical reflectometry,” 2D Materials , 10, 025025 (2023). Characterization (materials science) ; Materials science ; Janus ; Nanophotonics ; Permittivity ; Optoelectronics ; Monolayer ; Heterojunction ; Exciton ; Photoluminescence ; Nanotechnology ; Ellipsometry ; Thin film ; Computer science ; Dielectric ; Physics ; Condensed matter physics 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Guikai Zheng | Min Zhu | Zijian Xu | Chao Liu | Hao Yin | Peng Sun
Source:  (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Progress of machine learning potentials for material atomic simulation How to simulate material phenomena of large time-scales and system with high accuracy at low computational cost is the driving force for the development of simulation techniques in computational materials science. The study of complex large-scale natural phenomena needs the support of fast and accurate computational methods. Since the data-driven machine learning potential has been proposed in 2007, it has attracted wide attention because of its advantages of high precision and high computational efficiency. In this review, we present the key construction process of machine learning potential and its application in materials science, discuss the current challenges, and point out future directions. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Source:  (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

Bridging physics and machine learning in material design and optimisation? Lightweight materials have become integral in diverse sectors such as transportation, energy, and healthcare. Their varied microstructures and properties present significant potential for applications from load-bearing components to multifunctional structures. However, a major challenge lies in the heterogeneous material properties and vast design space of materials, impeding effective design and optimisation. My talk will address this challenge in two parts. Firstly, I will explore mechanics-based approaches to model the failure of materials. This will encompass a wide range of scenarios, from fracture, crushing behaviour, ballistic impact to liquid-solid impact of materials. Secondly, I will showcase the application of machine learning approaches for the design of porous architected materials, focusing on optimisation strategies. By bridging mechanics and machine learning, our work aims to unlock new possibilities in material design and optimisation. Bridging (networking) ; Physics ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Yihan Zhao | Cheng Yang
Source:  (article, 2024-07-05)
Field: Materials Science — Materials Chemistry

A Physically Inspired Graph Neural Network for Crystal Properties Prediction Crystal is a kind of material composed of unit cells arranged in a periodic pattern in three-dimensional space. Predicting properties is a crucial task in crystal materials research. Accurate representation of the structure and precise property prediction are essential for this task. Graph-based representations of structures have been widely adopted to model atoms and bonds, and significant advancements have been made using graph learning methods. In this work, we propose a novel approach that leverages physical knowledge to enhance the modeling of interatomic relationships. Specifically, we replace the traditional Euclidean distance with interatomic potential energy and incorporate approximate kinetic energy into the features of atomic nodes. These enhanced representations are then integrated into our proposed neural network, named PBCGNN. Our method is experimentally evaluated on the JARVIS datasets, demonstrating that this physics-inspired approach yields improved performance compared to multiple existing methods. Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Joe G. Greener
Source: arXiv (Cornell University) (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

Reversible molecular simulation for training classical and machine learning force fields The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories. Here we improve this approach by explicitly calculating gradients using a reverse-time simulation with effectively constant memory cost and a computation count similar to the forward simulation. The method is applied to learn all-atom water and gas diffusion models with different functional forms, and to train a machine learning potential for diamond from scratch. Comparison to ensemble reweighting indicates that reversible simulation can provide more accurate gradients and train to match time-dependent observables. Training (meteorology) ; Force field (fiction) ; Computer science ; Artificial intelligence ; Molecular dynamics ; Psychology ; Machine learning ; Cognitive science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Mass Spectrometry Techniques and Applications
Authors: Mudassir Hussain Tahir | Naeem‐Ul‐Haq Khan | Khalid M. Elhindi
Source: International Journal of Quantum Chemistry (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates ABSTRACT This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease. Computer science ; Artificial intelligence ; Database ; Machine learning ; Information retrieval Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Computational Drug Discovery Methods
Authors: liu yunwu | Ruisheng Zhang | Tongfeng Li | Jing Jiang | Jun Ma | Yongna Yuan | Ping Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular Representation Contrastive Learning Via Transformer Embedding to Graph Neural Networks Embedding ; Transformer ; Computer science ; Artificial intelligence ; Feature learning ; Graph ; Artificial neural network ; Natural language processing ; Theoretical computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Dexin Fu | Xianjun Liang | Keliang Liu
Source:  (article, 2024-03-15)
Field: Chemistry — Spectroscopy

Machine learning method for molecular dynamics simulation of interactions between low-temperature plasma and drugs This study aims to explore the interaction between low-temperature plasma and drug molecules and perform molecular dynamics simulations with the help of machine learning methods. We selected 200 drug samples for experiments, and the experimental environment was specific plasma conditions, including temperature, density, ionization degree and plasma type. By observing at successive time points, we record the rate at which drug molecules change, the types and number of chemical bonds affected, the amount of drug molecules remaining and the new compounds that may be produced. In addition, we considered other possible influencing factors such as pH, solvent type, relative humidity and temperature in the laboratory. Machine learning models are trained to predict the dynamics and reaction trends of drug molecules with plasma. Preliminary results show that machine learning methods demonstrate a high degree of accuracy and effectiveness in predicting and understanding complex interactions between plasmas and drugs. Molecular dynamics ; Computer science ; Dynamics (music) ; Plasma ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry Mass Spectrometry Techniques and Applications
Authors: Wei Nong | Ruiming Zhu | Kedar Hippalgaonkar
Source: ChemRxiv (preprint, 2024-08-08)
Field: Materials Science — Materials Chemistry

CrySPR: A Python interface for implementation of crystal structure pre-relaxation and prediction using machine-learning interatomic potentials The functional properties of crystalline inorganic materials in a variety of applications including, but not limited to, catalysts, batteries, solar cells, electronics, fundamentally depend on their crystal structures. Discovery of novel materials could be transformative for these fields. In the past few decades, the computational science community has developed crystal structure prediction (CSP) methods with the goal to find the exact symmetry-constrained atomic arrangements in the periodic unit cell, which are globally and/or locally energetically favorable: finding the globally minimal or locally minimal crystal structure for a given chemical formula. The implementation of CSP typically involves an iteration procedure with at least two components: the sampling of the potential energy surface (PES) for generating raw/unrelaxed crystal structures, and the subsequent local energy minimization of generated structures. The latter part is typically carried out through computationally expensive density functional (DFT) calculations. A non-exhaustive but representative list of available CSP codes includes USPEX, CALYPSO, AIRSS, XtalOpt, IM2ODE; due to the nature of DFT calculations, this CSP process can be very time-consuming. Recent rapid advances of pre-trained machine-learning interatomic potentials (ML-IAPs) based on data from DFT calculations, such as, M3GNet, CHGNet and MACE (amongst others) have significantly accelerated the process of local energy minimization but have not thoroughly been tested on CSP tasks. The realization of local energy minimization using ML-IAPs, referred to as pre-relaxation when compared with using DFT calculations, plays a critical role in the CSP implementation. We present here, CrySPR, which stands for Crystal Structure Pre-Relaxation and PRediction, which is specifically designed to serve as a Python package that provides user-friendly application programming interfaces (APIs), functionalities and utilities for crystal structure generation, pre-relaxation of structures using ML-IAPs and structure prediction. The codes are open-source and have been released to the Python Package Index (PyPI). Python (programming language) ; Computer science ; Computational science ; Relaxation (psychology) ; Interface (matter) ; Interatomic potential ; Artificial intelligence ; Programming language ; Chemistry ; Operating system ; Computational chemistry ; Psychology ; Molecular dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Mashkoor Alam | Samina Husain
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hyperparameter Tuned Machine Learning Predictions of Specific Capacitance of Conducting Polymers and Their Composites for High Performance Advanced Supercapacitors Hyperparameter ; Supercapacitor ; Capacitance ; Materials science ; Polymer ; Composite material ; Computer science ; Artificial intelligence ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Emma Bucher | Christopher Rau | Quan Phuong
Source: Academic Journal of Earth Sciences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING-DRIVEN HIGH-THROUGHPUT SCREENING OF CATALYST CANDIDATES FOR CO2 HYDROGENATION The escalating levels of carbon dioxide (CO2) in the atmosphere, primarily due to human activities, pose significant challenges to global climate stability. CO2 hydrogenation emerges as a promising technology to mitigate these emissions by converting CO2 into valuable hydrocarbons and fuels, thus providing a sustainable energy pathway. Catalysts are essential for enhancing the efficiency and selectivity of this process; however, the discovery of effective catalysts is complicated by the vast array of potential materials and the intricate interactions that influence catalytic performance. Traditional methods of catalyst screening are often laborious and resource-intensive, necessitating innovative approaches to expedite the discovery process. This paper explores the integration of machine learning (ML) techniques into high-throughput screening (HTS) methodologies to facilitate the rapid evaluation of thousands of catalyst candidates for CO2 hydrogenation. By leveraging ML algorithms, we can analyze extensive datasets, identify performance patterns, and prioritize promising candidates for experimental validation. The findings demonstrate that ML-driven HTS not only accelerates catalyst discovery but also optimizes resource utilization, paving the way for more efficient solutions to combat CO2 emissions. Throughput ; High-throughput screening ; Catalysis ; Computer science ; Chemistry ; Organic chemistry ; Operating system ; Biochemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions