jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Viejay Z. Ordillo | Kōji Shimizu | Darwin Barayang Putungan | Alexandra B. Santos-Putungan | Satoshi Watanabe | Rizalinda L de Leon | Joey D. Ocon | Karl Ezra Pilario | Allan Abraham B. Padama
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

Two-stage feature selection for machine learning-aided DFT-based surface reactivity study on single-atom alloys Abstract This paper presents a feature-centric strategy for predicting adsorption energies of key CO 2 reduction reaction (CO 2 RR) adsorbates, CO and H species, utilizing density functional theory-based calculations for eight adsorption sites and considering alloying effects of nine transition metals at single-atom concentrations. Here, we explore a class of materials consisting of a majority host metal where individual atoms of a different element are dispersed called single-atom alloys (SAA). A total of eight feature selection methods are assessed within Gradient Boosting Regression and Linear Regression models. This study proposes a practical and effective two-stage approach that narrows down the initial 86 features to subsets of 10 and 7 for CO and H adsorption energy predictions, respectively, with the arithmetic mean of valence electrons (VE-am) feature consistently emerging as highly influential, validated through permutation and Shapley additive explanations-based feature importance analyses. The models exhibit robust performance on unseen data, indicating their generalization capability. The findings emphasize VE-am as a potential key machine learning feature for CO 2 RR on SAA surfaces and underline the effectiveness of the feature-centric approach in understanding feature impacts in machine learning models for CO 2 RR on SAA systems. Additionally, while other features based on structural, electronic and elemental properties may not individually impact the model significantly, their collective contribution plays a vital role in achieving more accurate adsorption energy predictions. Materials science ; Atom (system on chip) ; Surface (topology) ; Feature selection ; Selection (genetic algorithm) ; Feature (linguistics) ; Stage (stratigraphy) ; Reactivity (psychology) ; Artificial intelligence ; Computer science ; Geometry ; Mathematics ; Parallel computing Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Alex Heilman | Claire Schlesinger | Qimin Yan
Source: arXiv (Cornell University) (preprint, 2024-06-05)
Field: Materials Science — Materials Chemistry

Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals Modern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, and elasticity tensors, are computationally expensive to produce ab initio for crystalline systems; however, with greater availability of such data in large material property databases, we now have a sufficient target space to begin training equivariant models in the prediction of such properties. Here we explicitly develop spherical harmonic decompositions of these tensorial properties using their general symmetries. We then apply three distinct E(3)-equivariant convolutional structures to the prediction of the components of these decompositions, allowing us to predict the aforementioned tensorial quantities in an equivariant manner and compare performance. We further report results testing the transferability of these predictive models between different tensorial target sets. Equivariant map ; Tensor (intrinsic definition) ; Graph ; Pure mathematics ; Computer science ; Mathematics ; Materials science ; Theoretical computer science Material Properties and Failure Mechanisms
Authors: Jia Zhao | Taixi Feng | Guimin Lu
Source: International Journal of Minerals Metallurgy and Materials (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Understanding the local structure and thermophysical behavior of Mg-La liquid alloys via machine learning potential Alloy ; Thermodynamics ; Enthalpy ; Molecular dynamics ; Structure factor ; Shape-memory alloy ; Local structure ; Materials science ; Chemistry ; Metallurgy ; Chemical physics ; Crystallography ; Computational chemistry ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Mai Le | Alan Yao | A. Boen Zhang | Hieu Trung Le | Zhaoyang Chen | Xuqing Wu | Lihong Zhao | Jiefu Chen
Source: IEEE journal on multiscale and multiphysics computational techniques (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Expediting Ionic Conductivity Prediction of Solid-State Battery Electrodes Using Machine Learning Solid-state batteries can offer enhanced safety and potentially higher energy density compared to traditional lithium-ion batteries. However, their power density remains a challenge due to limited ionic conductivity in composite electrodes caused by non-ideal microstructures. Laborious experimental processes and time-consuming data analysis algorithms are obstacles to establishing structure–performance correlations and optimizing electrode microstructure. In this paper, we present a machine learning approach to predict the effective conductivity of a composite electrode based on scanning electron microscopy images, using binary images composed of conductive and non-conductive regions and an ionic conductivity value of the conductive region. We show that our proposed method is two orders of magnitude more efficient than conventional numerical schemes such as the finite difference method. Expediting ; Ionic conductivity ; Battery (electricity) ; Conductivity ; Electrode ; Computer science ; Materials science ; Machine learning ; Chemistry ; Engineering ; Electrolyte ; Systems engineering ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Jakub K. Sowa | Peter J. Rossky
Source: Journal of Chemical Theory and Computation (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra The use of machine learning (ML) algorithms in molecular simulations has become commonplace in recent years. There now exists, for instance, a multitude of ML force field algorithms that have enabled simulations approaching ab initio level accuracy at time scales and system sizes that significantly exceed what is otherwise possible with traditional methods. Far fewer algorithms exist for predicting rotationally equivariant, tensorial properties such as the electric polarizability. Here, we introduce a kernel ridge regression algorithm for machine learning of the polarizability tensor. This algorithm is based on the bond polarizability model and allows prediction of the tensor components at the cost similar to that of scalar quantities. We subsequently show the utility of this algorithm by simulating gas phase Raman spectra of biphenyl and malonaldehyde using classical molecular dynamics simulations of these systems performed with the recently developed MACE-OFF23 potential. The calculated spectra are shown to agree very well with the experiments and thus confirm the expediency of our algorithm as well as the accuracy of the used force field. More generally, this work demonstrates the potential of physics-informed approaches to yield simple yet effective machine learning algorithms for molecular properties. A priori and a posteriori ; Raman spectroscopy ; Computer science ; Spectral line ; Machine learning ; Artificial intelligence ; Computational chemistry ; Chemistry ; Pattern recognition (psychology) ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Allison Harris | Juan A. Nepomuceno
Source: Journal of Physics B Atomic Molecular and Optical Physics (article, 2024-01-23)
Field: Chemistry — Spectroscopy

A data-driven machine learning approach for electron-molecule ionization cross sections Abstract Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning (ML) algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient ML model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that with training on as few as 10 molecular datasets, the network is able to predict the experimental cross sections of additional molecules with an accuracy similar to experimental uncertainties in existing data. As the number of training molecular datasets increased, the network’s predictions became more accurate and, in the worst case, were within 30% of accepted experimental values. In many cases, predictions were within 10% of accepted values. Using a network trained on datasets for 25 different molecules, we present predictions for an additional 27 molecules, including alkanes, alkenes, molecules with ring structures, and DNA nucleotide bases. Ionization ; Electron ; Atomic physics ; Electron ionization ; Molecule ; Physics ; Computer science ; Nuclear physics ; Ion ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kasim Toprak
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-01-16)
Field: Materials Science — Materials Chemistry

Understanding neural network tuned Langevin thermostat effect on predicting thermal conductivity of graphene-coated copper using nonequilibrium molecular dynamics simulations Abstract Copper has always been used in thermoelectric applications due to its extensive properties among metals. However, it requires further improving its heat transport performance at the nanosized applications by supporting another high thermal conductivity material. Herein, copper was coated with graphene, and the neural network fitting was employed for the nonequilibrium molecular dynamics simulations of graphene-coated copper nanomaterials to predict thermal conductivity. The Langevin thermostat that was tuned with a neural network fitting (NNF), which makes up the backbone of deep learning, generated the temperature difference between the two ends of the models. The NNF calibrated the Langevin thermostat damping constants that helped to control the temperatures precisely. The buffer and thermostat lengths were also analyzed, and they have considerable effects on the thermostat temperatures and a significant impact on the thermal conductivity of the graphene-coated copper. Regarding thermal conductivity, the four different shapes of vacancy defect concentrations and their locations in the graphene sheets were further investigated. The vacancy between the thermostats significantly decreases the thermal conductivity; however, the vacancy defect in thermostats does not have a similar effect. When the graphene is placed between two copper blocks, the thermal conductivity decreases drastically, and it continues to drop when the sine wave amplitude on the graphene sheet increases. Thermostat ; Thermal conductivity ; Materials science ; Graphene ; Copper ; Vacancy defect ; Molecular dynamics ; Langevin dynamics ; Thermodynamics ; Condensed matter physics ; Nanotechnology ; Composite material ; Metallurgy ; Physics ; Computational chemistry ; Statistical physics ; Chemistry Thermal properties of materials ; nanoparticles nucleation surface interactions ; Heat Transfer and Optimization
Authors: Siyu Liu | Tongqi Wen | Arvind Pattamatta | David J. Srolovitz
Source: arXiv (Cornell University) (preprint, 2024-01-31)
Field: Materials Science — Materials Chemistry

A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models inherently encompass an expansive and comprehensive materials knowledge database, far exceeding the capabilities of individual researcher. Nonetheless, devising methods to harness the knowledge embedded within LLMs for the design and discovery of novel materials remains a formidable challenge. We introduce a general approach for addressing materials classification problems, which incorporates LLMs, prompt engineering, and deep learning. Utilizing a dataset of metallic glasses as a case study, our methodology achieved an improvement of up to 463% in prediction accuracy compared to conventional classification models. These findings underscore the potential of leveraging textual knowledge generated by LLMs for materials especially in the common situation where datasets are sparse, thereby promoting innovation in materials discovery and design. Workflow ; Computer science ; Artificial intelligence ; Natural language processing ; Deep learning ; Database Machine Learning in Materials Science
Authors: Yang Zhang | Yuanyuan Bai | Jin Xu | Rufei Wei
Source: International Journal of Hydrogen Energy (article, 2024-11-16)
Field: Materials Science — Materials Chemistry

Prediction of electrochemical properties of La–Y–Ni-based hydrogen storage alloys based on machine learning Hydrogen storage ; Materials science ; Electrochemistry ; Computer science ; Metallurgy ; Chemistry ; Physical chemistry ; Alloy ; Electrode Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Fuel Cells and Related Materials
Authors: Luis Yepes Llorente | Joseph Morlier | Saketh Sridhara | Krishnan Suresh
Source: Structural and Multidisciplinary Optimization (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

A hybrid machine learning and evolutionary approach to material selection and design optimization for eco-friendly structures Engineering design process ; Selection (genetic algorithm) ; Environmentally friendly ; Material selection ; Biomimetics ; Computer science ; Biochemical engineering ; Process (computing) ; Material Design ; Artificial intelligence ; Engineering ; Machine learning ; Mathematical optimization ; Mechanical engineering ; Mathematics ; Materials science ; Biology ; Ecology Material Selection and Properties ; BIM and Construction Integration ; Design Education and Practice
Authors: Zhihong Wu | Lei Zhou | Peng‐Fei Hou | Yuyan Liu | Taoli Guo | Jincheng Liu
Source: ChemRxiv (preprint, 2024-08-22)
Field: Materials Science — Materials Chemistry

Catalytic Large Atomic Model (CLAM): A Machine-Learning-Based Interatomic Potential Universal Model Catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional Density Functional Theory (DFT) methods. To address this, we present the Catalytic Large Atomic Model (CLAM), a machine-learning-based interatomic potential designed for heterogeneous catalysis. Trained on a comprehensive dataset that includes metals, alloys, oxides, clusters, zeolites, 2D materials, and small molecules, CLAM ensures high accuracy across diverse catalytic systems. We also introduce a "local fine-tuning" algorithm that enhances the model’s applicability by accelerating structural optimizations and transition state searches while maintaining precision. Additionally, CLAM facilitates rapid reaction network construction and efficient kinetic analysis. This work advances computational catalysis by providing a universal and robust tool for catalyst design and mechanism exploration. Catalysis ; Work (physics) ; Density functional theory ; Interatomic potential ; Computer science ; Mechanism (biology) ; Biological system ; Complex system ; Molecular dynamics ; Nanotechnology ; Biochemical engineering ; Materials science ; Chemical physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Physics ; Thermodynamics ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ankit Anuragi | Ankit Das | Akash Baski | Vinay Maithani | Sankha Mukherjee
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning predicted inelasticity in defective two-dimensional transition metal dichalcogenides using SHAP analysis The manipulation of crystallographic defects in 2H-transition metal dichalcogenides (2H-TMDCs), whether pre- or post-synthesis, has garnered significant interest recently, as it holds the promise of tuning the thermal, chemical, and electronic properties of these materials. However, such desirable improvements often come at the cost of deteriorated elastic and inelastic properties, which may lead to serious concerns considering mechanical reliability issues. Therefore, persistent efforts are needed to explore the effects of energetically favorable vacancies on the mechanical properties of 2D TMDCs for an effective tuning of material properties for versatile applications. In this context, machine learning models trained on data based on molecular models can not only provide fast, efficient material models but also unearth crucial structure-property relations. However, such efforts are at an early stage of development. In this study, machine learning and deep learning techniques are used to analyze the mechanical properties of 2D transition metal dichalcogenides (TMDCs) in both pristine and defect forms. The goal is to predict failure stress, strain to failure, and strength based on chirality and strain. Various crystallographic defects were considered, and extensive molecular dynamics simulations were performed. XGBoost and densely connected neural network (DenseNet) algorithms were used to make accurate state-of-the-art predictions, and comparative evaluation and Shapley value analysis of both models are presented to improve interpretability. Transition metal ; Materials science ; Chemistry ; Nanotechnology ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: John N. El Berch | Maya Salem | Giannis Mpourmpakis
Source: Nanoscale (review, 2024-12-02)
Field: Materials Science — Materials Chemistry

Advances in simulating dilute alloy nanoparticles for catalysis Dilute alloy (DA) catalysts, including single-atom alloys (SAAs), which are comprised of trace amounts of an active promoter metal dispersed on the surface of a selective host metal, offer exceptional activity and selectivity while utilizing precious metals more efficiently. Although most SAA and DA applications have focused on partial hydrogenation and oxidation reactions, their use has steadily expanded into more complex thermo-, photo-, and electro-catalytic processes. This progress has been largely driven by mechanistic insights derived from computational chemistry and is expected to accelerate with the advancement of artificial intelligence. This minireview discusses novel advances in simulating SAAs and DAs for catalysis applications, including <i>ab initio</i> calculations, multiscale modeling, and machine learning. Emphasis is placed on the impact of reaction conditions, promoter ensembles, and nanoparticle morphology on the stability and catalytic performance of SAAs and DAs. Finally, a perspective is offered on potential future directions of SAA and DA simulations and their extension to other systems with distinct, well-defined active sites. Alloy ; Nanoparticle ; Materials science ; Catalysis ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Min Xia | Yu Zhang | Hongwei Song | Ya Jia | Minghui Yang
Source: The Journal of Physical Chemistry A (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Predicting Rate Constants of Hydrogen Abstraction Reactions between OH/HO<sub>2</sub> and Alkanes by Machine Learning Models The hydrogen abstraction reactions by small radicals from fuel molecules play an important role in the oxidation of fuels. However, experimental measurements and/or theoretical calculations of their rate constants under combustion conditions are very challenging due to their high reactivity. Machine learning offers a promising approach to predicting thermal rate constants. In this work, three machine learning methods, XGB, FNN, and XGB-FNN hybrid algorithms, were employed to train and predict the rate constants of the hydrogen abstraction reactions between alkanes and OH/HO<sub>2</sub>. Six descriptors were selected according to the Pearson correlation coefficients, the importance of descriptors, and the clustering heat map. It was proven that the XGB-FNN model is the most robust. The constructed XGB-FNN model achieved an average deviation of 89.13% for the alkanes + OH reactions and 190.93% for the alkanes + HO<sub>2</sub> reactions on their respective prediction sets. The model was also used to predict the rate constants of the reactions involving larger alkanes, demonstrating its extrapolation capability. Furthermore, the model has the ability to distinguish the reactivity of the reactions with the hydrogen atom abstracted at different sites of alkane. Abstraction ; Hydrogen atom abstraction ; Reaction rate constant ; Reactivity (psychology) ; Work (physics) ; Hydrogen ; Combustion ; Chemistry ; Radical ; Relation (database) ; Thermodynamics ; Computer science ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Kinetics ; Data mining ; Quantum mechanics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Combustion Engine Technologies
Authors: Christos Stavrogiannis | Vasilis Tsioulos | Filippos Sofos
Source: Applied Research (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

A hybrid molecular dynamics/machine learning framework to calculate the viscosity and thermal conductivity of Ar, Kr, Xe, O, and Ν Abstract In this paper, molecular dynamics (MD) simulations and machine learning (ML) methods are combined to obtain the transport properties, such as viscosity and thermal conductivity, of five basic elements, which are computationally hard to obtain at the nanoscale and extremely demanding to estimate accurately through an experimental procedure. Starting from an experimental database from literature sources, we extend the ( P ‐ T ) space on which the transport properties are calculated by employing MD simulations and ML predictions, in a synergistic mode. Results refer to all fluid states (gas, liquid, supercritical), under ambient and supercritical conditions, suggesting an alternative path that can be accurately followed to bypass expensive experiments and costly numerical simulations. Nine different ML algorithms are exploited and assessed on their prediction ability, with tree‐based architectures achieving increased accuracy on the implied data set. The proposed computational platform runs fast in a common python Jupyter environment, both for MD and ML, and can be adjusted and extended for the calculation of material properties both in interpolation and extrapolation applications. Extrapolation ; Thermal conductivity ; Python (programming language) ; Supercritical fluid ; Molecular dynamics ; Computer science ; Interpolation (computer graphics) ; Viscosity ; Algorithm ; Statistical physics ; Computational science ; Materials science ; Computational physics ; Thermodynamics ; Chemistry ; Physics ; Mathematics ; Artificial intelligence ; Computational chemistry ; Mathematical analysis Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics