jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sen Xu | Li‐Ling Wu | Yi Fan | Yufeng Liu | Xiongzhi Zeng | Zhenyu Li
Source: The Journal of Physical Chemistry C (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

Hydrocarbon Species on the Cu(111) Surface Studied with a Neural Network Potential Hydrocarbon species are involved in various surface processes, such as graphene growth on Cu surfaces. A systematic examination of their structures, stability, and reactivity from first principles is essential for understanding the atomic mechanisms of these processes. However, this approach has a substantial computational cost. In this study, we train an accurate Cu–C–H neural network (NN) potential using a homemade deep potential learning platform, DPTorch, which exhibits good linear scalability over thousands of CPU cores and multiple GPUs. The obtained NN potential can accurately reproduce the reaction energy and activation barrier for elementary reactions with hydrocarbon species involved. With such an accurate NN potential, the stochastic surface walking global optimization algorithm is then used to explore stable hydrocarbon structures on the Cu(111) surface. It turns out that hydrogen plays an important role in stabilizing small carbon ring structures. Free energy surfaces are constructed via enhanced sampling using NN potential-based molecular dynamics simulations, which gives a revision to statistically not well-converged free energy barriers predicted previously using ab initio molecular dynamics. At the same time, new pathways are found for both CH and C2 dissociation reactions. These results provide valuable insights into the chemistry of hydrocarbons on the Cu surface. Artificial neural network ; Surface (topology) ; Hydrocarbon ; Biological system ; Chemistry ; Computer science ; Artificial intelligence ; Biology ; Mathematics ; Organic chemistry ; Geometry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Chemical Physics Studies
Authors: Shanglin Wu | Shisheng Zheng | Wentao Zhang | Mingzheng Zhang | Shunning Li | Feng Pan
Source: ChemRxiv (preprint, 2024-05-22)
Field: Materials Science — Materials Chemistry

Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts Copper-based electrocatalysts, which hold great promise in selectively reducing CO2 into multicarbon products, have attracted a lot of recent interest, both experimentally and theoretically. While many studies have suggested a strong dependence of catalytic selectivity on the concentration of the *CO reaction intermediate on Cu surface, it remains challenging for a direct experimental probe of the CO coverage. This necessitates a reliable computational method that can accurately establish the theoretical coverage-dependent phase diagram of CO adsorbates on the catalyst. Here we propose a scheme composed of density functional theory (DFT) calculations, machine-learning force fields (MLFF) and graph neural networks (GNN) as a solution. This method enables a fast screening of 7 million adsorption configurations based on a small set of DFT data, with a balance between accuracy and efficiency tuned by the combinatorial use of MLFF and GNN models. We have investigated 8 different Cu facets, and discovered that the high-index facets such as (310), (210) and (322) exhibit a much higher CO coverage than the low-index counterparts such as (111), leading to an increased opportunity for C-C coupling for the former. Our results can provide a new perspective for the understanding of the fundamental role of CO coverage on Cu surface for electrochemical CO2 reduction. Facet (psychology) ; Materials science ; Computer science ; Psychology ; Social psychology Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Neha Neha | Manya Aggarwal | Aashutosh Soni | Tarak Karmakar
Source: The Journal of Physical Chemistry B (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Polymorph-Specific Solubility Prediction of Urea Using Constant Chemical Potential Molecular Dynamics Simulations Molecular dynamics simulations offer a robust approach to understanding the material properties within a system. Solubility is defined as the analytical composition of a saturated solution expressed as a proportion of designated solute in a designated solvent, according to IUPAC. It is a critical property of compounds and holds significance across numerous fields. Various computational techniques have been explored for determining solubility, including methods based on chemical potential determination, enhanced sampling simulation, and direct coexistence simulation, and lately, machine learning-based methods have shown promise. In this investigation, we have utilized Constant Chemical Potential Molecular Dynamics, a method rooted in direct coexistence simulation, to predict the solubility of urea polymorphs in aqueous solution. The primary purpose of using this method is to overcome the limitation of the direct simulation method by maintaining a constant chemical potential for a sufficiently long time. Urea is chosen as a prototypical system for our study, with a particular focus on three of its polymorphs. Our approach effectively discriminates between the polymorphs of urea based on their respective solubility values; polymorph III is found to have the highest solubility, followed by forms IV and I. Solubility ; Molecular dynamics ; Aqueous solution ; Chemistry ; Thermodynamics ; Solvent ; Urea ; Constant (computer programming) ; Molecule ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Computer science ; Physics Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Analytical Chemistry and Chromatography
Authors: Ke Liu | Kaifan Yang | Shangde Gao
Source: Neural Computing and Applications (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

A periodicity aware transformer for crystal property prediction Computational Science and Engineering ; Property (philosophy) ; Computer science ; Transformer ; Machine learning ; Electrical engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Joonhee Kang | Byung‐Hyun Kim | Min Ho Seo | Jehyun Lee
Source: Current Applied Physics (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Sampling rare events using nanostructures for universal Pt neural network potential Sampling (signal processing) ; Artificial neural network ; Rare events ; Nanostructure ; Computer science ; Nanotechnology ; Deep neural networks ; Materials science ; Artificial intelligence ; Mathematics ; Statistics ; Telecommunications ; Detector Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Applications
Authors: Christopher Ikechukwu Ekeocha | Ikechukwu Nelson Uzochukwu | Ikenna Benedict Onyeachu | Ini‐Ibehe Nabuk Etim | Emeka E. Oguzie
Source: The Journal of Physical Chemistry A (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives This study aimed to predict the selected antipyrine compounds' inhibitory efficiencies and anticorrosion properties in a hydrochloric acid (HCl) environment. Molecular descriptors and input variables were obtained using density functional theory (DFT), and the variance inflation factor (VIF) was employed to reduce redundant variables, leading to the selection of seven quantum chemical descriptors as input variables. Using machine learning techniques such as <i>K</i>-nearest neighbor (KNN) and artificial neural network (ANN), a predictive model was built for 39 antipyrine compounds with known corrosion inhibition efficiencies for carbon and low alloy steel in hydrochloric acid solutions. The models' predictive capability was assessed using cross-validation, with the ANN model showing superior performance, achieving a coefficient of determination (<i>R</i><sup>2</sup>) value of 0.715 compared to 0.548 for the KNN model. Performance metrics such as the mean square error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) further confirmed the superiority of the ANN model over the KNN model. The corrosion inhibition efficiencies (CIEs) of the selected antipyrine compounds ranged from 68.78 to 99.79%, with compound A1 demonstrating the highest CIE of 99.79% and compound A3 the lowest, as evaluated by the ANN model. Analysis of Fukui index parameters obtained from the Mulliken population analysis suggested that the nucleophilic and electrophilic sites play a crucial role in the interactions between the inhibitor and the metal atom through electron donor-acceptor interactions. Moreover, the energy of adsorption (<i>E</i><sub>ads</sub>) in kcal·mol<sup>-1</sup> decreased in the order of A1 (-187.8) > A2 (-132.0) > A2 (-84.4), with the high negative value of <i>E</i><sub>ads</sub> indicating strong and spontaneous adsorption. Further analysis using radial distribution functions and molecular dynamics simulations revealed that inhibitor A1 exhibited predominantly chemisorption, inhibitor A2 showed a mixed type, and inhibitor A3 demonstrated predominantly physisorption, aligning well with the results of the predictive studies. Computer science ; Machine learning ; Artificial intelligence ; Algorithm Corrosion Behavior and Inhibition ; Inorganic and Organometallic Chemistry ; Concrete Corrosion and Durability
Authors: Jakub Martinka | Marek Pederzoli | Mario Barbatti | Pavlo O. Dral | Jiřı́ Pittner
Source: The Journal of Chemical Physics (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

A simple approach to rotationally invariant machine learning of a vector quantity Unlike with the energy, which is a scalar property, machine learning (ML) prediction of vector or tensor properties poses the additional challenge of achieving proper invariance (covariance) with respect to molecular rotation. For the energy gradients needed in molecular dynamics (MD), this symmetry is automatically fulfilled when taking analytic derivative of the energy, which is a scalar invariant (using properly invariant molecular descriptors). However, if the properties cannot be obtained by differentiation, other appropriate methods should be applied to retain the covariance. Several approaches have been suggested to properly treat this issue. For nonadiabatic couplings and polarizabilities, for example, it was possible to construct virtual quantities from which the above tensorial properties are obtained by differentiation and thus guarantee the covariance. Another possible solution is to build the rotational equivariance into the design of a neural network employed in the model. Here, we propose a simpler alternative technique, which does not require construction of auxiliary properties or application of special equivariant ML techniques. We suggest a three-step approach, using the molecular tensor of inertia. In the first step, the molecule is rotated using the eigenvectors of this tensor to its principal axes. In the second step, the ML procedure predicts the vector property relative to this orientation, based on a training set where all vector properties were in this same coordinate system. As the third step, it remains to transform the ML estimate of the vector property back to the original orientation. This rotate-predict-rotate (RPR) procedure should thus guarantee proper covariance of a vector property and is trivially extensible also to tensors such as polarizability. The RPR procedure has an advantage that the accurate models can be trained very fast for thousands of molecular configurations, which might be beneficial where many training sets are required (e.g., in active learning). We have implemented the RPR technique, using the MLatom and Newton-X programs for ML and MD, and performed its assessment on the dipole moment along MD trajectories of 1,2-dichloroethane. Covariance ; Invariant (physics) ; Rotational invariance ; Tensor (intrinsic definition) ; Eigenvalues and eigenvectors ; Scalar (mathematics) ; Computer science ; Property (philosophy) ; Applied mathematics ; Mathematics ; Algorithm ; Artificial intelligence ; Physics ; Pure mathematics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Feng Chen | Mengsheng Zhang | Weihua Huang | Harse Sattar | Lianbo Guo
Source: Foods (article, 2024-07-22)
Field: Chemistry — Analytical Chemistry

Laser-Induced Breakdown Spectroscopy–Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model's robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications. VNIR ; Laser-induced breakdown spectroscopy ; Artificial intelligence ; Macro ; Pattern recognition (psychology) ; Spectroscopy ; Biological system ; Machine learning ; Computer science ; Remote sensing ; Chemistry ; Hyperspectral imaging ; Physics ; Biology ; Geology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Laser-induced spectroscopy and plasma
Authors: J S Li | Zaiqiao Meng | Shangsong Liang
Source:  (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Towards Deep Generative Backmapping of Coarse-Grained Molecular Systems Coarse-graining (CG) is a widely used technique for simplifying complex molecular systems in molecular dynamics simulations. It involves mapping fine-grained (FG) atoms to several CG beads, which enables the study of larger systems and longer simulation timescales. However, this simplification results in the loss of atomistic details. To address this, a technique reverse to CG named backmapping is designed to recover the FG structures from CG beads. Traditional methods of backmapping yield poor reconstructions and often necessitate computationally costly refinement processes. Recently proposed deep learning approaches have shown promise in directly generating atomistic structures with high accuracy. One of the most advanced models deploys a conditional variational auto-encoder (VAE) to encode the FG uncertainties into an invariant latent space and decode them back to FG geometries via equivariant convolutions. Motivated by the recent advancements of diffusion models, we explore an alternative approach to capture the multimodal distribution of FG structures. In this study, we present CGDiff, an equivariant CG-conditional diffusion model for generative backmapping. It models the underlying distribution by condensing the FG information into geometric diffusion processes. Conditioned on CG beads, our model progressively refines the coordinates of FG atoms through equivariant graph neural networks. We evaluate the performance of CGDiff against baselines by conducting experiments on two public datasets. To further promote research in this field, we additionally provide a demo interactive web service for generative backmapping, available at https://huggingface.co/spaces/jsli47/mlbackmap. Computer science ; Generative grammar ; Artificial intelligence Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Ruslan Akhmerov | Irina Piyanzina | Oleg V. Nedopekin | Volker Eyert
Source: Computational Materials Science (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

A neural-network potential for aluminum Stacking-fault energy ; Work (physics) ; Stacking fault ; Materials science ; Deformation (meteorology) ; Melting point ; Stacking ; Phase diagram ; Artificial neural network ; Statistical physics ; Thermodynamics ; Computer science ; Phase (matter) ; Artificial intelligence ; Dislocation ; Chemistry ; Physics ; Microstructure ; Metallurgy Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Julian Mentges | Daniel Bischoff | Brigitte Walla | Dirk Weuster‐Botz
Source: Crystals (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

In Situ Microscopy with Real-Time Image Analysis Enables Online Monitoring of Technical Protein Crystallization Kinetics in Stirred Crystallizers Controlling protein crystallization processes is essential for improving downstream processing in biotechnology. This study investigates the combination of machine learning-based image analysis and in situ microscopy for real-time monitoring of protein crystallization kinetics. The experimental research is focused on the batch crystallization of an alcohol dehydrogenase from Lactobacillus brevis (LbADH) and two selected rational crystal contact mutants. Technical protein crystallization experiments were performed in a 1 L stirred crystallizer by adding polyethyleneglycol 550 monomethyl ether (PEG 550 MME). The estimated crystal volumes from online microscopy correlated well with the offline measured protein concentrations in solution. In addition, in situ microscopy was superior to offline data if amorphous protein precipitation occurred. Real-time image analysis provides the data basis for online estimation of important batch crystallization performance indicators like yield, crystallization kinetics, crystal size distributions, and number of protein crystals. Surprisingly, one of the LbADH mutants, which should theoretically crystallize more slowly than the wild type based on molecular dynamics (MD) simulations, showed better crystallization performance except for the yield. Thus, online monitoring of scalable protein crystallization processes with in situ microscopy and real-time image analysis improves the precision of crystallization studies for industrial settings by providing comprehensive data, reducing the limitations of traditional analytical techniques, and enabling new insights into protein crystallization process dynamics. In situ ; Crystallization ; Kinetics ; Microscopy ; Materials science ; Chemical engineering ; Chemistry ; Optics ; Engineering ; Physics ; Organic chemistry Enzyme Structure and Function ; Crystallization and Solubility Studies ; Protein purification and stability
Authors: Maotao Liu | Yifan Yang | Qun Liu | Li Liu | Guoyin Wang
Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

A Knowledge-Driven Self-Supervised Approach for Molecular Generation Due to the great successes of Graph Neural Networks (GNN) in numerous fields, growing research interests have been devoted to applying GNN to molecular learning tasks. The molecule structure can be naturally represented as graphs where atoms and bonds refer to nodes and edges respectively. However, the atoms are not haphazardly stacked together but combined into various spatial geometries. Meanwhile, since chemical reactions mainly occur in substructures such as functional groups, the substructure plays a decisive role in the molecule's properties. Therefore, directly applying GNN to molecular representation learning could ignore the molecular spatial structure and the substructure properties which in turn degrades the performance of downstream tasks. In this paper, we propose Knowledge-Driven Self-Supervised Model for Molecular Representation Learning (KSMRL) to address above problems. The KSMRL consists of two major pathways: (1) the Spatial Information (SI) based pathway which preserves the spatial information of molecular structure, (2) the Subgraph Constraint (SC) based pathway which retains the properties of substructures into the molecular representation. In this manner, both the atomic level and substructure level information can be included in modeling. According to the experimental results on multiple datasets, the proposed KSMRL can generate discriminative molecular representations. In molecular generation tasks, KSMRL combined with Autoregressive Flow (AF) models or Discrete Flow (DF) models outperforms the state-of-the-art baselines over all datasets. In addition, we demonstrate the effectiveness of KSMRL with property optimization experiments. To indicate the ability of predicting specified potential Drug-Target Interactions (DTIs), a case study for discriminating the interactions between molecule generated by KSMRL and targets is also given. Substructure ; Artificial intelligence ; Computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: B. Sachin | Charitha M. Rao | C. Durga Prasad | Amit K. Tiwari | Ravikiran Ravikiran | T. Raghavendra | Abhiram Eswaran | R. Madhusudhana
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2024-08-26)
Field: Materials Science — Biomaterials

Interactive design and development of an intelligent vision-driven 3D printed precision sorting mechanism for silk cocoons Sorting ; SILK ; Mechanism (biology) ; Computer science ; Engineering drawing ; Human–computer interaction ; Artificial intelligence ; Computer vision ; Architectural engineering ; Computer graphics (images) ; Engineering ; Algorithm Silk-based biomaterials and applications ; Industrial Vision Systems and Defect Detection
Authors: Qi Kong | Yasushi Shibuta
Source: Journal of Physics Condensed Matter (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Predicting materials properties with generative models: applying generative adversarial networks for heat flux generation In the realm of materials science, the integration of machine learning techniques has ushered in a transformative era. This study delves into the innovative application of generative adversarial networks (GANs) for generating heat flux data, a pivotal step in predicting lattice thermal conductivity within metallic materials. Leveraging GANs, this research explores the generation of meaningful heat flux data, which has a high degree of similarity with that calculated by molecular dynamics simulations. This study demonstrates the potential of artificial intelligence (AI) in understanding the complex physical meaning of data in materials science. By harnessing the power of such AI to generate data that is previously attainable only through experiments or simulations, new opportunities arise for exploring and predicting properties of materials. Generative grammar ; Adversarial system ; Flux (metallurgy) ; Generative adversarial network ; Heat flux ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Heat transfer ; Thermodynamics ; Metallurgy ; Deep learning Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals