jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aparna P. A. Subramanyam | Danny Pérez
Source: arXiv (Cornell University) (preprint, 2024-07-14)
Field: Materials Science — Materials Chemistry

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the fcc (Al), bcc (W), hcp (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions. Entropy (arrow of time) ; Interatomic potential ; Computer science ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Thermodynamics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science
Authors: Shuyao Lin | David Holec | Davide G. Sangiovanni | Thomas Leiner | L. Hultman | P.H. Mayrhofer | Nikola Koutná
Source: Research Square (preprint, 2024-10-30)
Field: Materials Science — Materials Chemistry

Shear-activated phase transformations of diborides via machine-learning potential molecular dynamics Molecular dynamics ; Shear (geology) ; Dynamics (music) ; Computer science ; Physics ; Statistical physics ; Materials science ; Composite material ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Inorganic Chemistry and Materials
Authors: Lukas M. Sigmund | Shree Sowndarya S. | Andreas E. Albers | Philipp Erdmann | Robert S. Paton | Lutz Greb
Source: Angewandte Chemie (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of <i>p</i>‐Block‐Atom‐Based Molecules Abstract “How strong is this Lewis acid?” is a question researchers often approach by calculating its fluoride ion affinity (FIA) with quantum chemistry. Here, we present FIA49k, an extensive FIA dataset with 48,986 data points calculated at the RI‐DSD‐BLYP‐D3(BJ)/def2‐QZVPP//PBEh‐3c level of theory, including 13 different p ‐block atoms as the fluoride accepting site. The FIA49k dataset was used to train FIA‐GNN, two message‐passing graph neural networks, which predict gas and solution phase FIA values of molecules excluded from training with a mean absolute error of 14 kJ mol −1 ( r 2 =0.93) from the SMILES string of the Lewis acid as the only input. The level of accuracy is notable, given the wide energetic range of 750 kJ mol −1 spanned by FIA49k. The model's value was demonstrated with four case studies, including predictions for molecules extracted from the Cambridge Structural Database and by reproducing results from catalysis research available in the literature. Weaknesses of the model are evaluated and interpreted chemically. FIA‐GNN and the FIA49k dataset can be reached via a free web app ( www.grebgroup.de/fia‐gnn ). Fluoride ; Chemistry ; Lewis acids and bases ; Molecule ; Ion ; Atom (system on chip) ; Block (permutation group theory) ; Quantum chemistry ; Catalysis ; Computational chemistry ; Inorganic chemistry ; Computer science ; Mathematics ; Organic chemistry ; Combinatorics Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Carbon dioxide utilization in catalysis
Authors: Xuan-Bac Nguyen | Apoorva Bisht | Benjamin Thompson | Hugh Churchill | Khoa Luu | Samee U. Khan
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning Detecting two-dimensional (2D) materials in silicon chips presents a significant challenge in the field of quantum machines due to the difficulty of data collection. Specifically, among thousands of flakes, not all flakes are useful or well-annotated, resulting in noisy and hard samples within the dataset, which challenges the deep neural network (DNN) to learn. To address this problem, we propose a novel method for identifying quantum 2D flakes even when there is a high rate of missing annotations in the input images. In particular, we first propose a new mechanism for automatically detecting false negative flakes that are missing annotations. Second, we introduce an attention-based loss function to mitigate the negative impact of these unannotated flakes on the DNNs. The experimental results demonstrate that our method outperforms previous approaches. Computer science ; Identification (biology) ; Quantum ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Dimas Kurniawan | Armansyah Armansyah
Source: Inform Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi (article, 2024-07-31)
Field: Chemistry — Analytical Chemistry

Classification of Crude Palm Oil Quality Using Artificial Neural Networks Based on Chemical Components Palm oil, known as Crude Palm Oil (CPO), is a flagship product in the palm oil industry, playing a crucial role in the economies of various tropical countries such as Indonesia. This study aims to classify the quality of CPO based on chemical components using the Artificial Neural Networks (ANN) method with a backpropagation algorithm. The research data was obtained from PT. Perkebunan Lembah Bhakti (PLB) 2 from January to October 2023, consisting of 225 entries with five main chemical variables: impurity level, moisture content, free fatty acid (FFA) level, Deterioration of Bleachability Index (DOBI), and carotenoids. The data preprocessing stage involved transforming and normalizing the data using the Z-score method. The ANN model used has a 5-5-2 architecture with ReLU activation functions for the input and hidden layers and a Softmax function for the output layer. Model evaluation was conducted using accuracy metrics, which showed that the ANN model could classify CPO quality with an accuracy of 97.78% on the test data. The research results show that ANN can classify CPO quality with a high level of accuracy. This indicates that this method has great potential for use in the industry to improve CPO quality assessment. The improvement in accuracy and validity of the ANN classification results has significant implications for the industry. With high accuracy, ANN can reduce human errors in quality assessment, speed up the process, and increase the consistency of assessment results. This is crucial because consistent quality assessment can enhance operational efficiency and reduce production costs. Palm oil ; Artificial neural network ; Crude oil ; Palm ; Oil mill ; Quality (philosophy) ; Pulp and paper industry ; Artificial intelligence ; Biochemical engineering ; Environmental science ; Chemistry ; Computer science ; Petroleum engineering ; Engineering ; Food science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Xinyi Wang | Hanna Breunig | Peng Peng
Source: ChemRxiv (preprint, 2024-08-01)
Field: Chemistry — Inorganic Chemistry

Broad Range Material-to-System Screening of Metal-Organic Frameworks for Hydrogen Storage Using Machine Learning Hydrogen is pivotal in the transition to sustainable energy systems, serving essential roles in both power generation and industrial applications. Metal-organic frameworks have emerged as promising candidates for efficient hydrogen storage. However, identifying promising candidates for deployment is challenging due to the vast number of synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for different scales and durations of hydrogen storage applications. While previous screenings of the MOF database have predominantly emphasized high hydrogen capacities under cryogenic conditions, we identify that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, 99.72% exhibit optimal cost-efficiency within a temperature range of 180 K to 250 K and 98.9% achieve this at pressure of 150 bar. Furthermore, we characterize correlations between system cost and material properties, identifying key promising features of MOFs for low-cost systems including low densities (<1 g/cm³), high surface areas (>1500 m²/L), large void fractions (>0.6), and high pore volumes (>0.8 cm³/g). Software deployment ; Metal-organic framework ; Hydrogen storage ; Process engineering ; Computer science ; Hydrogen ; Materials science ; Raw material ; Minification ; Environmental science ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Jinwoong Nam | C.T. Ramasamy | Daniel E. Raser | Gustavo L. Barbosa Couto | Lydia Thies | David Hibbitts | Fuat E. Celik
Source: The Journal of Physical Chemistry C (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Predicting the Enthalpy of Hydrocarbon Radicals Adsorbed on Pt(111) Using Molecular Fingerprints and Machine Learning The reliable prediction of properties for the adsorbates, including their enthalpy, has been a long-standing challenge as a first key step in studying surface reactions. It is especially difficult when large adsorbates are involved as the interactions between the adsorbates and surface atoms are complex. Here, we developed machine learning (ML) models for the prediction of the formation enthalpy of various C2 to C6 hydrocarbon adsorbates on the Pt(111) surface based on 384 density functional theory calculations. Focusing on larger and more intricate adsorbates, two-thirds of the total species were C6 species. Four molecular descriptors that represent the valency and bonding of individual carbons within the adsorbates were generated without intensive computation. They were subsequently used as the features of the ML models with three linear and four nonlinear algorithms. The models were developed with 30 different samplings of train/test sets, and their results were statistically analyzed to ensure the performance of the models. Nonlinear models, especially kernel ridge regression and extreme gradient boosting, outperformed linear models with lower absolute errors. The top two accurate models, based on these algorithms, also displayed remarkable robustness in predicting various species. Employing ensemble average voting with these two models, we achieved the lowest mean absolute error of 0.94 kcal/molC. Finally, ML was used to estimate the formation enthalpy of 3115 hydrocarbon adsorbates on Pt(111), highlighting the promise of these methods to study more complicated reaction networks. Enthalpy ; Hydrocarbon ; Chemistry ; Valency ; Adsorption ; Nonlinear system ; Robustness (evolution) ; Kernel (algebra) ; Algorithm ; Computer science ; Thermodynamics ; Physical chemistry ; Mathematics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Ayu Irie | Akihide Koura | Kohei Shimamura | Fuyuki Shimojo
Source: Journal of Physics Condensed Matter (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Phase behavior and atomic dynamics in Rb<sub>x</sub>Na<sub>1–x</sub>: insights from machine learning interatomic potentials based on ab initio molecular dynamics Liquid alkali metal alloys have garnered significant attention because of their potential applications in coolant systems and batteries, driven by the need for environmental conservation and technological development. However, research on these complex systems is limited, necessitating a deeper understanding to ensure their safe and effective utilization. This study presents a comprehensive investigation of the factors that determine the phase diagram of Rb<sub><i>x</i></sub>Na1-x. By reproducing the experimental results using the thermodynamic integration method and machine learning interatomic potentials based on<i>ab initio</i>molecular dynamics simulations, we uncovered the delicate balance between the energy and entropy contributions that influence the phase stability of these liquid metal alloys. Further analysis of the liquid phase revealed the crucial roles of volume and atomic mass. Additionally, the coordination numbers of the atoms revealed distinct clustering behaviors, where Na atoms tended to avoid proximity to other Na atoms, whereas Rb atoms exhibited a strong tendency to cluster together. Moreover, the diffusion dynamics further illustrated the asymmetry in the behavior of Rb and Na, with Rb showing increased diffusion at higher concentrations and Na exhibiting higher diffusion at lower concentrations. These findings offer significant insights into the phase stability and the dynamic and structural properties of these complex liquid metal alloys. Molecular dynamics ; Interatomic potential ; Ab initio ; Dynamics (music) ; Ab initio quantum chemistry methods ; Chemistry ; Chemical physics ; Physics ; Atomic physics ; Molecular physics ; Computational chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; History and advancements in chemistry
Authors: Matthew S. Johnson | David H. Bross | Judit Zádor
Source: ChemRxiv (preprint, 2024-09-27)
Field: Materials Science — Materials Chemistry

Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows Workflow ; Kinetics ; Quantum chemistry ; Quantum ; Computer science ; Chemical kinetics ; Chemistry ; Surface (topology) ; Nanotechnology ; Materials science ; Reaction mechanism ; Physics ; Catalysis ; Mathematics ; Database ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Declan Galvin | Eduardo Aguilar-Bejarano | David M. Rogers | Simon Woodward | Ender Özcan | Patrick J. Guiry | Grazziela P. Figueredo
Source: ChemRxiv (preprint, 2024-05-13)
Field: Chemistry — Inorganic Chemistry

Describing Chiral Ligands in Palladium-catalyzed Decarboxylative Asymmetric Allylic Allylation: A Critical Comparison of Three Machine Learning Approaches Despite its current popularity, machine learning (ML) applied to asymmetric catalysis remains underexplored. Present strategies include direct use of existing descriptors (e.g. those originally formulated for medicinal chemistry), the development of new bespoke steric and electronic descriptors, or the use of molecular graphs. This method diversity, in the absence of user guidelines, makes selecting an optimal ML algorithm unclear. The fact that asymmetric catalysis data sets are frequently small also make interpretable ML of chiral ligand understanding difficult to realize. Herein, we present an exhaustive evaluation of reaction representations in combination with different machine learning algorithms (including linear regression, random forests, gradient boosting, and graph neural networks) using a realistic-size database compromising 103 palladium- catalyzed decarboxylative asymmetric allylic alkylation (DAAA). This database consists of the combination of three different Trost-type ligands with 54 different substrates. It is concluded that our new bespoke steric and electronic descriptors offer the best performance, while overcoming the problem of interpretability of using existing topo-electronic descriptors, and the problem of data requirements of Graph Neural Networks. Allylic rearrangement ; Palladium ; Catalysis ; Chemistry ; Combinatorial chemistry ; Computer science ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Asymmetric Synthesis and Catalysis
Authors: Declan Galvin | Eduardo Aguilar-Bejarano | David M. Rogers | Simon Woodward | Ender Özcan | Patrick J. Guiry | Grazziela P. Figueredo
Source: ChemRxiv (preprint, 2024-05-13)
Field: Chemistry — Inorganic Chemistry

Describing Chiral Ligands in Palladium-catalyzed Decarboxylative Asymmetric Allylic Allylation: A Critical Comparison of Three Machine Learning Approaches Despite its current popularity, machine learning (ML) applied to asymmetric catalysis remains underexplored. Present strategies include direct use of existing descriptors (e.g. those originally formulated for medicinal chemistry), the development of new bespoke steric and electronic descriptors, or the use of molecular graphs. This method diversity, in the absence of user guidelines, makes selecting an optimal ML algorithm unclear. The fact that asymmetric catalysis data sets are frequently small also make interpretable ML of chiral ligand understanding difficult to realize. Herein, we present an exhaustive evaluation of reaction representations in combination with different machine learning algorithms (including linear regression, random forests, gradient boosting, and graph neural networks) using a realistic-size database compromising 103 palladium- catalyzed decarboxylative asymmetric allylic alkylation (DAAA). This database consists of the combination of three different Trost-type ligands with 54 different substrates. It is concluded that our new bespoke steric and electronic descriptors offer the best performance, while overcoming the problem of interpretability of using existing topo-electronic descriptors, and the problem of data requirements of Graph Neural Networks. Allylic rearrangement ; Palladium ; Catalysis ; Chemistry ; Combinatorial chemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Asymmetric Synthesis and Catalysis
Authors: A. Maevskiy | Alexandra Carvalho | Emil Sataev | Volha Turchyna | Keian Noori | Aleksandr Rodin | A. H. Castro Neto | A. Ustyuzhanin
Source: arXiv (Cornell University) (preprint, 2024-11-11)
Field: Materials Science — Materials Chemistry

Predicting ionic conductivity in solids from the machine-learned potential energy landscape Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics. Energy landscape ; Conductivity ; Energy (signal processing) ; Ionic bonding ; Computer science ; Materials science ; Environmental science ; Chemistry ; Physics ; Mathematics ; Thermodynamics ; Statistics ; Ion ; Physical chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications
Authors: Shaodong Zhang | Pan Chen | Pingyang Zhang | Lei Wei | Xuping Wang | Bing Liu | Yuanyuan Zhang | Xianshun Lv | Tingting Du
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Theoretical Investigation on the Dynamic Thermal Transport Properties of Graphene Foam by Machine-Learning Molecular Dynamics Simulations Graphene ; Molecular dynamics ; Materials science ; Thermal ; Dynamics (music) ; Graphene foam ; Nanotechnology ; Mechanics ; Physics ; Chemistry ; Graphene nanoribbons ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Phase Equilibria and Thermodynamics