jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shokirbek Shermukhamedov | Thana Maihom | Michael Probst
Source: Molecules (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Modelling the Impact of Argon Atoms on a WO3 Surface by Molecular Dynamics Simulations Machine learning potential energy functions can drive the atomistic dynamics of molecules, clusters, and condensed phases. They are amongst the first examples that showed how quantum mechanics together with machine learning can predict chemical reactions as well as material properties and even lead to new materials. In this work, we study the behaviour of tungsten trioxide (WO<sub>3</sub>) surfaces upon particle impact by employing potential energy surfaces represented by neural networks. Besides being omnipresent on tungsten surfaces exposed to air, WO<sub>3</sub> plays an important role in nuclear fusion experiments due to the preferred use of tungsten for plasma-facing components. In this instance, the formation of WO<sub>3</sub> is caused by the omnipresent traces of oxygen. WO<sub>3</sub> becomes a plasma-facing material, but its properties, especially concerning degradation, have hardly been studied. We employ molecular dynamics simulations to investigate sputtering, reflection, and adsorption phenomena occurring on WO<sub>3</sub> surfaces irradiated with Argon. The machine-learned potential energy function underlying the MD simulations is modelled using a neural network (NNP) trained from large sets of density functional theory calculations by means of the Behler-Parrinello method. The analysis focuses on sputtering yields for both oxygen and tungsten (W), for various incident energies and impact angles. An increase in Ar incident energy increases the sputtering yield of oxygen, with distinct features observed in different energy ranges. The sputtering yields of tungsten remain exceedingly low, even compared to pristine W surfaces. The ratios between the reflection, adsorption, and retention of the Ar atoms have been analyzed on their dependence of impact energy and incident end angles. We find that the energy spectrum of sputtered oxygen atoms follows a lognormal distribution and offers information about surface binding energies on the WO<sub>3</sub> surface. Tungsten ; Sputtering ; Molecular dynamics ; Tungsten trioxide ; Chemical physics ; Argon ; Materials science ; Reflection (computer programming) ; Density functional theory ; Adsorption ; Atomic physics ; Chemistry ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Physics ; Thin film ; Computer science ; Metallurgy Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Cooper Tezak | Jacob M. Clary | Sophie Gerits | Joshua Quinton | Benjamin Rich | Nicholas R. Singstock | Abdulaziz Alherz | Taylor J. Aubry | Struan Clark | R. B. Hurst | Mauro Del Ben | Christopher Sutton | Ravishankar Sundararaman | Charles B. Musgrave | Derek Vigil‐Fowler
Source: arXiv (Cornell University) (preprint, 2024-05-30)
Field: Materials Science — Materials Chemistry

BEAST DB: Grand-Canonical Database of Electrocatalyst Properties We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage. Electrocatalyst ; Grand canonical ensemble ; Database ; Computer science ; Chemistry ; Mathematics ; Statistics ; Physical chemistry Machine Learning in Materials Science
Authors: Çetin Kılıç | Sümeyra Güler-Kılıç
Source: The Journal of Chemical Physics (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their computational efficiency, scalability, and broad applicability. The training datasets for many such models are derived from density-functional theory calculations, typically using a semilocal exchange-correlation functional. As a result, long-range interactions such as London dispersion are often missing in these models. We investigate whether this missing component can be addressed by combining a graph deep learning potential with semiempirical dispersion models. We assess this combination by deriving the equations of state for layered pnictogen chalcohalides BiTeBr and BiTeI and performing crystal structure optimizations for a broader set of V-VI-VII compounds with various stoichiometries, many of which possess van der Waals gaps. We characterize the optimized crystal structures by calculating their x-ray diffraction patterns and radial distribution function histograms, which are also used to compute Earth mover's distances to quantify the dissimilarity between the optimized and corresponding experimental structures. We find that dispersion-corrected graph deep learning potentials generally (though not universally) provide a more realistic description of these compounds due to the inclusion of van der Waals attractions. In particular, their use results in systematic improvements in predicting not only the van der Waals gap but also the layer thickness in layered V-VI-VII compounds. Our results demonstrate that the combined potentials studied here, derived from a straightforward approach that neither requires fine-tuning the training nor refitting the potential parameters, can significantly improve the description of layered polar crystals. Pnictogen ; Graph ; Dispersion (optics) ; Computer science ; Physics ; Theoretical computer science ; Condensed matter physics ; Optics ; Superconductivity Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Solid-state spectroscopy and crystallography
Authors: Sungho Jeon | Hannah S. Nedzbala | Brittany L. Huffman | Adam J. Pearce | Carrie L. Donley | Xiaofan Jia | Gabriella P. Bein | Jihoon Choi | Nicolas Durand | Hala Atallah | Felix N. Castellano | Jillian L. Dempsey | James M. Mayer | Nilay Hazari | Eric A. Stach
Source: ChemRxiv (preprint, 2024-05-01)
Field: Materials Science — Materials Chemistry

Statistical Analysis of HAADF-STEM Images to Determine the Surface Coverage and Distribution of Immobilized Molecular Complexes The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system. Catalysis ; Homogeneous ; Surface modification ; Surface (topology) ; Convolutional neural network ; Materials science ; Biological system ; Selectivity ; Chemical physics ; Scanning transmission electron microscopy ; Nanotechnology ; Chemistry ; Chemical engineering ; Combinatorial chemistry ; Transmission electron microscopy ; Computer science ; Physical chemistry ; Organic chemistry ; Physics ; Mathematics ; Artificial intelligence ; Statistical physics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Koichiro Kato | Hiromu Matsumoto | Ryosuke KITA
Source: Journal of Computer Chemistry Japan (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Development of Machine Learning Models withFragment Molecular Orbital Calculation Data フラグメント分子軌道(FMO)法はタンパク質全体を量子化学計算可能な稀有な手法である．そして，FMO法によって得られるデータもまた，現状ではタンパク質系の量子化学計算データとして唯一無二のものとなっている．汎用ソフトウェアでは生成が困難なタンパク質の量子化学計算データとそれを用いた様々な機械学習モデルの開発は，近年活性化が著しいAI創薬に大きなインパクトを与えることが期待される．本稿では，筆者らのグループで進めているFMOデータを用いた機械学習モデル(原子電荷予測モデル，相互作用予測モデル，機械学習力場)の開発状況を概説する． Computer science ; Molecular orbital ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Huan He | J. Zhao | Jesper Byggmästar | Ru He | K. Nordlund | Chaohui He | Flyura Djurabekova
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Threshold Displacement Energy Map of Frenkel Pair Generation in &lt;i&gt;β&lt;/i&gt;-Ga2O3 From Machine-Learning-Driven Molecular Dynamics Simulations Displacement (psychology) ; Molecular dynamics ; Dynamics (music) ; Physics ; Energy (signal processing) ; Atomic physics ; Quantum mechanics ; Acoustics Ga2O3 and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: ChemRxiv (preprint, 2024-01-09)
Field: Materials Science — Materials Chemistry

Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes Recent decades have shown arising growth-on-demand of integrating the machine learning into all areas of chemistry and materials science. In this study, we consider one of the aspects of applying these technologies to gain advantage in the search for new knowledge extracted from experimental data obtained in ever-growing number of studies. The novelty detection approaches are aimed to identify the artefacts in these data that may be of importance in many direc- tions. The analysis of "outliers" in details of the synthesis in the research studies of garnet-structured solid electrolytes was chosen as the object of demonstration of one of the practical applications of this methodology. Particular attention was paid to the choice of precursors. The thermodynamic data such as the heat of formation from the pure oxides as well as the results of drop solution calorimetry for simple oxides were involved as the descriptors of the studied systems. The overall performance of novelty/outlier detection of all types of outliers was characterized for the data described varying the complexity of description using ROC-AUC statistics and was assessed to be 0.71 – 0.72 using the Area-Under-Curve statistics. It was found that all “outlier” compounds related to those as the result of using the rare precursors in synthesis were successfully identified. The complementary regression analysis was performed to elucidate the relationship between the data diversity and the complexity of data description. Outlier ; Novelty ; Computer science ; Anomaly detection ; Novelty detection ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods
Authors: Liheng Yu | Pengkun Wang | Zhe Zhao | Zhongchao Yi | Nan Sun | Di Wu | Yang Wang
Source:  (article, 2024-10-20)
Field: Materials Science — Materials Chemistry

XRDMamba: Large-scale Crystal Material Space Group Identification with Selective State Space Model In material science, the properties of crystalline materials largely depend on their structures, and space group is a key descriptor of crystal structure. With the rapid advancement of deep learning, the traditional artificial structure analysis method based on X-ray diffraction (XRD) has become cumbersome and is being gradually supplanted by neural networks. However, existing models are too simplistic and lack a comprehensive understanding of material structure. Our approach XRDMamba integrates chemical knowledge and presents a fresh crystal planes perspective on XRD data. We also introduce a knowledge-driven model for space group identification tasks. We have thoroughly analyzed our approach through numerous experiments, observing its SOTA performance and excellent generalization capabilities. The code is available in ~https://github.com/baigeiguai/XRDMamba. Group (periodic table) ; Space (punctuation) ; Scale (ratio) ; Identification (biology) ; Computer science ; State space ; Crystal (programming language) ; Physics ; Mathematics ; Statistics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Geochemistry and Geologic Mapping
Authors: Yifeng Tian | Soumendu Bagchi | Liam Myhill | Giacomo Po | Enrique Martínez | Yen Ting Lin | Nithin Mathew | Danny Pérez
Source: arXiv (Cornell University) (preprint, 2024-03-20)
Field: Materials Science — Materials Chemistry

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals. Dislocation ; Artificial intelligence ; Computer science ; Physics ; Psychology ; Statistical physics ; Condensed matter physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: Yanlei Kang | Zhenyu Duan | Tianlei Tong | Xiu-Rong Hu | Xiongtao Zhang | Hailong Hu | Zhong Li
Source: Crystals (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

An Enhanced Deep Learning-Based Pharmaceutical Crystal Detection with Regional Filtering In the process of pharmaceutical crystallization, the automatic detection of crystal shapes in images is important since controlling the morphology of the crystals improves the quality of pharmaceutical crystals. In this paper, a novel image detection method called RECDet is proposed. It leverages an automatically adapted binary image to bypass background regions, thereby reducing the detection field. In addition, the method greatly reduces the training time while improving the detection accuracy by using a specially designed detection box for the crystal shape. The performance of our model is evaluated through experimental analysis on a publicly available glutamate crystal dataset and a self-made entecavir pharmaceutical crystal dataset. Experimental results show that RECDet improves the accuracy of prediction bounding boxes by more than 2% compared to other popular models and achieves a classification accuracy of 98%. It can be used as a promising tool in the application of pharmaceutical crystallization control. Computer science ; Crystallization ; Crystal (programming language) ; Artificial intelligence ; Process (computing) ; Minimum bounding box ; Field (mathematics) ; Image (mathematics) ; Pattern recognition (psychology) ; Materials science ; Mathematics ; Chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Shuxin Zheng | Jiyan He | Chang Liu | Yu Shi | Ziheng Lu | Weitao Feng | Fusong Ju | Jiaxi Wang | Jianwei Zhu | Yaosen Min | He Zhang | Shidi Tang | Hongxia Hao | Peiran Jin | Chi Chen | Frank Noé | Haiguang Liu | Tie‐Yan Liu
Source: Nature Machine Intelligence (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Author Correction: Predicting equilibrium distributions for molecular systems with deep learning Statistical physics ; Computer science ; Physics Machine Learning in Materials Science
Authors: Efi-Maria Papia | Alex Kondi | Vassilios Constantoudis
Source:  (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Inverse design of Hexagonal Moiré Materials: Machine Learning for tunable pore properties Moiré pattern ; Hexagonal crystal system ; Inverse ; Materials science ; Computer science ; Optoelectronics ; Computer vision ; Crystallography ; Mathematics ; Geometry Machine Learning in Materials Science ; Thermal properties of materials ; Transition Metal Oxide Nanomaterials
Authors: Chaehwa Jeong | Juhyeok Lee | Hyesung Jo | SangJae Lee | KwangHo Lee | Colin Ophus | Peter Ercius | EunAe Cho | Yongsoo Yang
Source: arXiv (Cornell University) (preprint, 2024-11-04)
Field: Materials Science — Materials Chemistry

Atomic-scale 3D structural dynamics and functional degradation of Pt alloy nanocatalysts during the oxygen reduction reaction Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their surface and subsurface. However, systematic optimization remains incomplete, as it requires an atomic-scale understanding of these factors and their dynamics over potential cycling, as well as their relationship to ORR activity. Here, we implement neural network-assisted atomic electron tomography to measure the 3D atomic structural dynamics and their effects on the functional degradation of PtNi alloy catalysts. Our results reveal that PtNi catalysts undergo shape changes, surface alloying, and strain relaxation during cycling, which can be effectively mitigated by Ga doping. By combining geometry, local chemistry, and strain analysis, we calculated the changes in ORR activity over thousands of cycles and observed that Ga doping leads to higher initial activity and greater stability. These findings offer a pathway to understanding 3D atomic structural dynamics and their relation to ORR activity during cycling, paving the way for the systematic design of durable, high-efficiency nanocatalysts. Nanomaterial-based catalyst ; Alloy ; Degradation (telecommunications) ; Atomic units ; Scale (ratio) ; Materials science ; Chemistry ; Chemical engineering ; Metallurgy ; Computer science ; Physics ; Metal ; Engineering Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2024-02-12)
Field: Materials Science — Materials Chemistry

Cartesian atomic cluster expansion for machine learning interatomic potentials Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modelling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, and then use Clebsch-Gordan contraction to maintain rotational symmetry, which may introduce redundancies in representations and computational overhead. We propose an alternative: a Cartesian-coordinates-based atomic density expansion. This approach provides a complete set of polynormially indepedent features of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements and inter-atomic message passing. The resulting potential, named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability, and generalizability. We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys. Cluster (spacecraft) ; Cartesian coordinate system ; Interatomic potential ; Computer science ; Cluster expansion ; Physics ; Computational science ; Quantum mechanics ; Mathematics ; Molecular dynamics ; Geometry Machine Learning in Materials Science
Authors: M. Ghorbani | Ziyuan Li | Yao Qiu | Philippe Marcus | John R. Scully | Oumaïma Gharbi | Haowen Luo | Rajeev Gupta | Zhuoran Zeng | Hamish L. Fraser | Mitra L. Taheri | N. Birbilis
Source: arXiv (Cornell University) (preprint, 2024-05-09)
Field: Materials Science — General Materials Science

Current progress in corrosion of multi principal element alloys Whilst multi-principal element alloys (MPEAs) remain a promising class of materials owing to several attractive mechanical properties, their corrosion performance is also unique. In this concise review, we present an emerging overview of some of the general features related to MPEA corrosion, following a decade of work in the field. This includes highlighting some of the key aspects related to the electrochemical phenomena in MPEA corrosion, and the relevant future works required for a holistic mechanistic understanding. In addition, a comprehensive database of the reported corrosion performance of MPEAs is presented, based on works reported to date. The database is assembled to also allow users to undertake machine learning or their own data analysis, with a parsed representation of alloy composition, test electrolyte, and corrosion related parameters. Corrosion ; Principal (computer security) ; Element (criminal law) ; Current (fluid) ; Metallurgy ; Materials science ; Engineering ; Computer science ; Political science ; Electrical engineering ; Computer security Material Properties and Applications