jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryosuke Jinnouchi | Ferenc Karsai | Georg Kresse
Source: Chemical Science (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists. Yet, predicting even the absolute standard hydrogen electrode potential, the most fundamental reference for electrode potentials, proves to be extremely challenging. Here, we show that a hybrid functional incorporating 25% exact exchange enables quantitative predictions when statistically accurate phase-space sampling is achieved <i>via</i> thermodynamic integrations and thermodynamic perturbation theory calculations, utilizing machine-learned force fields and Δ-machine learning models. The application to seven redox couples, including molecules and transition metal ions, demonstrates that the hybrid functional can predict redox potentials across a wide range of potentials with an average error of 140 mV. Redox ; Molecule ; Electrode ; Standard hydrogen electrode ; Hydrogen ; Hydrogen molecule ; Chemistry ; Standard electrode potential ; Computational chemistry ; Reversible hydrogen electrode ; Chemical physics ; Electrochemistry ; Reference electrode ; Physical chemistry ; Inorganic chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods
Authors: Samar El‐Sanabary | Hanan Kouta | Mahmoud Shaban | Abdulrahman Alrumayh | A. I. Alateyah | Fahad N. Alsunaydih | Majed O. Alawad | Yasmine El-Taybany | Mohamed S. El‐Asfoury | Waleed H. El-Garaihy
Source: Heliyon (article, 2024-07-01)
Field: Materials Science — Biomaterials

A comparative study of machine learning and response surface methodologies for optimizing wear parameters of ECAP-processed ZX30 alloy Magnesium, valued for its lightweight, recyclability, and biocompatibility, faces challenges like its poor wear behavior and mechanical properties that limit its adaptation for a multitude of applications. In this study, various statistical analyses, and machine learning (ML) techniques were employed to optimize equal channel angular pressing (ECAP) process parameters for improving the wear behavior of Mg-3wt.% Zn-0.7 wt% Ca alloy. ECAP was conducted up to four passes via route Bc at 250 °C. Wear testing of both as-annealed (AA) and ECAP-processed alloys was performed using the dry ball-on-flat wear method under varying loads, speeds, and time. One pass (1P) and 4Bc-ECAP resulted in a notable uniform grain refinement of 86 % and 91 %, respectively, compared to the AA. X-ray diffraction (XRD) analysis confirmed a refined structure attributed to extensive dynamic recrystallization. Mechanical wear testing revealed a significant reduction in volume loss (VL), up to 56 % and 28.5 % after 1P and 4Bc samples, respectively, compared to the AA sample, supported by the observed texture intensity. The coefficient of friction (COF) stabilizes at 0.30-0.45, indicating low friction characteristics. Next, by adjusting wear load and speed through design of experiments (DOE), the wear output responses, VL and COF, were experimentally investigated. The output responses were predicted in the second step using ML, 3D response surface plots, and statistical analysis of variance (ANOVA). According to the regression model, the minimal VL was attained at a 5 N applied load. Also, the wear speed and VL at different passes are inversely proportional. On the other hand, the optimal COF was obtained at applied load about 2-3 N and 250 mm/s at different passes. The wear process variables were then optimized using different optimization techniques namely, genetic algorithm (GA), hybrid DOE-GA, and multi-objective genetic algorithm (MOGA) approaches. Materials science ; Alloy ; Magnesium alloy ; Response surface methodology ; Metallurgy ; Pressing ; Coefficient of friction ; Grain size ; Composite material ; Computer science ; Machine learning Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Johan Klarbring | Aron Walsh
Source: Chemistry of Materials (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Na Vacancy-Driven Phase Transformation and Fast Ion Conduction in W-Doped Na<sub>3</sub>SbS<sub>4</sub> from Machine Learning Force Fields Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na<sub>3</sub>PnCh<sub>4</sub> (Pn = P, Sb; Ch = S, Se) family has been studied for their high Na ion conductivity. The population of Na vacancies, which mediate ion diffusion in these materials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping and its impact on diffusion and phase stability, we trained a machine learning force field for Na<sub>3-<i>x</i></sub> W <sub><i>x</i></sub> Sb<sub>1-<i>x</i></sub> S<sub>4</sub> based on an equivariant graph neural network. Analysis of large-scale molecular dynamics trajectories shows that an increased Na vacancy population stabilizes the global cubic phase at lower temperatures with enhanced Na ion diffusion and that the explicit role of the substitutional W dopants is limited. In the global cubic phase, we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions. Evidence of correlated Na ion diffusion is also presented that underpins the suggested superionic nature of these materials. Vacancy defect ; Doping ; Ion ; Materials science ; Phase (matter) ; Transformation (genetics) ; Thermal conduction ; Condensed matter physics ; Optoelectronics ; Atomic physics ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Kentaro Kutsukake
Source: Journal of Crystal Growth (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Review of machine learning applications for crystal growth research The application of information science and technology has led to a paradigm shift in scientific and technological research and crystal growth is no exception. Various types of application research have been conducted, and research methods that combine real experiments and simulations with information techniques are becoming increasingly complex. In this paper, I focus on the application of information science and technology to the field of crystal growth. In the first half, I discuss the characteristics of process informatics, including applications to crystal growth, from the perspective of how it differs from material informatics. In the second half, by reviewing various application studies to crystal growth, I aim to highlight the characteristics and discuss future issues. Informatics ; Perspective (graphical) ; Crystal (programming language) ; Crystal growth ; Data science ; Field (mathematics) ; Computer science ; Nanotechnology ; Management science ; Engineering ethics ; Artificial intelligence ; Chemistry ; Materials science ; Engineering ; Mathematics ; Crystallography Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Hongshuo Huang | Rishikesh Magar | Amir Barati Farimani
Source: Journal of Chemical Information and Modeling (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Pretraining Strategies for Structure Agnostic Material Property Prediction In recent years, machine learning (ML), especially graph neural network (GNN) models, has been successfully used for fast and accurate prediction of material properties. However, most ML models rely on relaxed crystal structures to develop descriptors for accurate predictions. Generating these relaxed crystal structures can be expensive and time-consuming, thus requiring an additional processing step for models that rely on them. To address this challenge, structure-agnostic methods have been developed, which use fixed-length descriptors engineered based on human knowledge about the material. However, the fixed-length descriptors are often hand-engineered and require extensive domain knowledge and generally are not used in the context of learnable models which are known to have a superior performance. Recent advancements have proposed learnable frameworks that can construct representations based on stoichiometry alone, allowing the flexibility of using deep learning frameworks as well as leveraging structure-agnostic learning. In this work, we propose three different pretraining strategies that can be used to pretrain these structure-agnostic, learnable frameworks to further improve the downstream material property prediction performance. We incorporate strategies such as self-supervised learning (SSL), fingerprint learning (FL), and multimodal learning (ML) and demonstrate their efficacy on downstream tasks for the Roost architecture, a popular structure-agnostic framework. Our results show significant improvement in small data sets and data efficiency in the larger data sets, underscoring the potential of our pretrain strategies that effectively leverage unlabeled data for accurate material property prediction. Leverage (statistics) ; Computer science ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Flexibility (engineering) ; Artificial neural network ; Context (archaeology) ; Domain knowledge ; Deep learning ; Graph ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hui Zheng | Eric Sivonxay | Rasmus Christensen | Max C. Gallant | Ziyao Luo | Matthew J. McDermott | Patrick Huck | Morten M. Smedskjær | Kristin A. Persson
Source: npj Computational Materials (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity Abstract Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability. Ab initio ; Thermal diffusivity ; Materials science ; Computer science ; Database ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Zhongjie Huang | Zilong Chen | Jiawen Cheng | Jiaqi Zhang | Shu‐Yi Wang | Tingting Chen | Xiaodan Zhang | Huan Pang
Source: Advanced Functional Materials (article, 2024-12-15)
Field: Materials Science — Materials Chemistry

Recent Progress in High‐Throughput On‐Chip Synthesis, Screening, and Data‐Driven Optimization: Toward an Electrocatalyst Chip for Catalysis Universe Exploration Abstract Throughout history, the quest for a “magic crystal ball” to predict the future and uncover the world's greatest innovations has captivated human imagination. For scientists, the dream of a compact chip brimming with data and wisdom is equally tantalizing. Despite the meteoric rise of artificial intelligence, the research paradigm for electrocatalysts has lagged, advancing at a surprisingly sluggish pace. This review aims to promote electrocatalyst informatics and revolutionize high‐performance material discovery by integrating advanced combinatorial on‐chip synthesis, high‐throughput screening, and machine learning‐powered analysis and optimization. The synergistic progress in these fields, envisioning a future where a minimized “electrocatalyst chip” can effortlessly navigate complex chemical spaces within a “data fablab” is critically examined. This innovative paradigm promises to accelerate the discovery of crucial materials, offering tangible solutions to pressing global challenges in energy, environmental sustainability, and technological advancement. This strategy is believed to hold great potential to transform the landscape of catalyst discovery and unleash a new wave of scientific breakthroughs. Materials science ; Electrocatalyst ; Throughput ; Chip ; Nanotechnology ; Catalysis ; High-throughput screening ; Electrochemistry ; Computer science ; Organic chemistry ; Wireless ; Physics ; Telecommunications ; Bioinformatics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Xiang Huang | Shenghong Ju
Source: Journal of Applied Physics (preprint, 2024-05-06)
Field: Materials Science — Materials Chemistry

Tutorial: AI-assisted exploration and active design of polymers with high intrinsic thermal conductivity Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamentals and implementation of combining classical molecular dynamics simulation and machine learning (ML) for the development of polymers with high TC are comprehensively introduced. We begin by describing the core components of a universal ML framework, involving polymer data sets, property calculators, feature engineering, and informatics algorithms. Then, the process of constructing interpretable regression algorithms for TC prediction is introduced, aiming to extract the underlying relationships between microstructures and TCs for polymers. We also explore the design of sequence-ordered polymers with high TC using lightweight and mainstream active learning algorithms. Lastly, we conclude by addressing the current limitations and suggesting potential avenues for future research on this topic. Thermal conductivity ; Polymer ; Materials science ; Thermal ; Computer science ; Physics ; Thermodynamics ; Composite material Machine Learning in Materials Science
Authors: Mohammad Sherjeel Javed Khan | Lariyah Mohd Sidek | Pavitra Kumar | Sadiq Abdullah Abdo Alkhadher | Hidayah Basri | Mohd Hafiz Zawawi | Ahmed El‐Shafie | Ali Najah Ahmed
Source: International Journal of Biological Macromolecules (article, 2024-08-14)
Field: Chemistry — Organic Chemistry

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater Wastewater ; Hazardous waste ; Context (archaeology) ; Catalysis ; Pollutant ; Contamination ; Environmental science ; Computer science ; Chemistry ; Human health ; Biochemical engineering ; Pulp and paper industry ; Environmental chemistry ; Waste management ; Environmental engineering ; Organic chemistry ; Engineering Nanomaterials for catalytic reactions
Authors: Miaolei Zhou | Liangcai Su | Chen Zhang | Luming Liu | Yewei Yu | Xiuyu Zhang | Chun‐Yi Su
Source: IEEE Transactions on Circuits and Systems I Regular Papers (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Neural Network Based Iterative Learning Control for Dynamic Hysteresis and Uncertainties in Magnetic Shape Memory Alloy Actuator Magnetic shape memory alloy-based actuator (MSMA-BA) is constructed based on the strain mechanism of MSMA material and the magnetic effect of electric current. It can generate macroscopic deformation with micro-nano scale resolution. However, the rate-dependent and load-dependent hysteresis characteristics in MSMA-BA will reduce the positioning accuracy and hinder its applications. In this study, a long short-term memory (LSTM)-based U model with exogenous inputs is proposed to describe the complex dynamic hysteresis characteristics. Then, an LSTM-based iterative learning control (ILC) scheme is proposed to realize the reference trajectory tracking control of the MSMA-BA. Additionally, a dynamic expansion compression factor (DECF) is introduced in the controller to accelerate the convergence speed of system. The convergence of the proposed LSTM-based ILC scheme is analyzed with the consideration of state uncertainty, output disturbance, and the initial state error. It will promote the further applications of ILC in practical situations. Experiments are carried out on MSMA-BA to validate the effectiveness of the proposed method. The experimental results indicate that the proposed modeling and control methods exhibit excellent performance. Hysteresis ; Shape-memory alloy ; Actuator ; Artificial neural network ; Alloy ; Computer science ; Control (management) ; Iterative learning control ; Control theory (sociology) ; Materials science ; Artificial intelligence ; Physics ; Condensed matter physics ; Composite material Shape Memory Alloy Transformations
Authors: Ziyue Zou | Dedi Wang | Pratyush Tiwary
Source: Digital Discovery (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics We present a graph-based differentiable representation learning method from atomic coordinates for enhanced sampling methods to learn both thermodynamic and kinetic properties of a system. Bottleneck ; Artificial neural network ; Information bottleneck method ; Artificial intelligence ; Kinetics ; Computer science ; Graph ; Machine learning ; Chemistry ; Thermodynamics ; Statistical physics ; Physics ; Theoretical computer science ; Cluster analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mingze Zheng | Brandon C. Bukowski
Source: The Journal of Physical Chemistry C (article, 2024-04-26)
Field: Chemistry — Inorganic Chemistry

Probing the Role of Acid Site Distribution on the Water Structure in Aluminosilicate Zeolites: Insights from Molecular Dynamics Water plays a pivotal role in numerous chemical processes, especially in the production of fuels and fine chemicals derived from biobased feedstocks. Zeolites are porous catalysts used extensively due to their shape-selective adsorption and confinement interactions; however, the kinetics of zeolite-catalyzed reactions are significantly impacted by the presence of water, which may affect product selectivity and intrinsic rate constants depending on transition-state polarity. In this study, we employed machine learning force fields (MLFFs) to accelerate ab initio molecular dynamics (AIMD) simulations and enhance the phase space exploration of water configurations in a mode Brønsted acid zeolite, H-AFI. We interrogated the structure of adsorbed water based on the Si/Al ratio and acid site distribution to disentangle the impact of acid site density and distribution on water matrix organization as a function of water loading. We integrated adsorption thermodynamics, vibrational spectroscopy simulations, and local density maps to interrogate the spatial orientation of adsorbed water clusters and their degree of hydrogen bonding. Our analysis unveiled the intricate interplay between the zeolite structure, Brønsted acid site location, and water, where spatially disparate acid sites nucleate extended clusters that span siliceous regions of the zeolite. We found that the length scale of ordered water regions is directly related to the Si/Al ratio and spatial distribution of Al sites. These findings provide insights into the molecular-level structure of water in aluminosilicate micropores and demonstrate how acid sites can be used to control water activity, which has applications to heterogeneous catalysis and adsorptive separations. Zeolite ; Aluminosilicate ; Molecular dynamics ; Catalysis ; Chemical physics ; Adsorption ; Chemistry ; Brønsted–Lowry acid–base theory ; Nucleation ; Hydrogen bond ; Molecule ; Materials science ; Chemical engineering ; Computational chemistry ; Physical chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Spectroscopy and Quantum Chemical Studies ; Mesoporous Materials and Catalysis
Authors: Haider Ali Khan | Symeon Savvopoulos | Isam Janajreh
Source: Environmental Research (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Artificial neural network-assisted thermogravimetric analysis of thermal degradation in combustion reactions: A study across diverse organic samples Thermogravimetric analysis ; Thermodynamics ; Gibbs free energy ; Combustion ; Enthalpy ; Activation energy ; Materials science ; Kinetic energy ; Chemistry ; Process engineering ; Physical chemistry ; Organic chemistry ; Physics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Chemical Thermodynamics and Molecular Structure
Authors: Chen Chen | Jinzhou Zheng | Chaoqin Chu | Qinkun Xiao | Chaozheng He | Xi Fu
Source: Chinese Chemical Letters (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

An effective method for generating crystal structures based on the variational autoencoder and the diffusion model Autoencoder ; Diffusion ; Statistical physics ; Mathematics ; Computer science ; Algorithm ; Applied mathematics ; Materials science ; Physics ; Artificial intelligence ; Thermodynamics ; Artificial neural network X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Mineral Processing and Grinding
Authors: Peilun Han | Xiaoqian Shen | Boxiong Shen
Source: Fuel (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

A simulation study on NOx reduction efficiency in SCR catalysts utilizing a modern C3-CNN algorithm NOx ; Selective catalytic reduction ; Convolutional neural network ; Computational fluid dynamics ; Reduction (mathematics) ; Computer science ; Artificial neural network ; Channel (broadcasting) ; Algorithm ; Catalysis ; Computer simulation ; Simulation ; Ammonia ; Process engineering ; Chemistry ; Engineering ; Artificial intelligence ; Mathematics ; Aerospace engineering ; Telecommunications Catalytic Processes in Materials Science ; Machine Learning and ELM ; Neural Networks and Applications