jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zixing Xu | Wantong Zhang | Nianrong Sun | Chunhui Deng
Source: TrAC Trends in Analytical Chemistry (article, 2024-04-23)
Field: Chemistry — Spectroscopy

Magnetic mesoporous materials and MALDI-TOF MS in peptidomics: From enrichment techniques to disease applications Nanotechnology ; Mesoporous material ; Personalized medicine ; Computational biology ; Computer science ; Identification (biology) ; Precision medicine ; High resolution ; Biochemical engineering ; Chemistry ; Bioinformatics ; Biology ; Medicine ; Engineering ; Materials science ; Biochemistry ; Pathology Advanced Proteomics Techniques and Applications ; Advanced biosensing and bioanalysis techniques ; Peptidase Inhibition and Analysis
Authors: Thana Maihom | Jarinya Sittiwong | Michael Probst | Bundet Boekfa | Chularat Wattanakit | Jumras Limtrakul
Source: Journal of Catalysis (article, 2024-07-14)
Field: Chemistry — Inorganic Chemistry

Predicting transition state and activation energies in n-hexane cracking over zeolites: Combined DFT calculations and estimations with the SISSO method Chemistry ; Hexane ; Cracking ; Fluid catalytic cracking ; Catalysis ; Computational chemistry ; Transition state theory ; State (computer science) ; Thermodynamics ; Transition state ; Activation energy ; Physical chemistry ; Organic chemistry ; Kinetics ; Reaction rate constant ; Quantum mechanics Zeolite Catalysis and Synthesis ; NMR spectroscopy and applications ; Advanced NMR Techniques and Applications
Authors: Zheng Cheng | Hangrui Bi | Siyuan Liu | Junmin Chen | Alston J. Misquitta | Kuang Yu
Source: Journal of Chemical Theory and Computation (article, 2024-06-18)
Field: Materials Science — Materials Chemistry

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This study introduces a comprehensive asymptotic parameter database encompassing atomic multipole moments, polarizabilities, and dispersion coefficients. Leveraging active learning, our database comprehensively represents protein fragments with up to 8 heavy atoms, capturing their conformational diversity with merely 78,000 data points. Additionally, the E(3) neural network (E3NN) is employed to predict the asymptotic parameters directly from the local geometry. The E3NN models demonstrate exceptional accuracy and transferability across all asymptotic parameters, achieving an <i>R</i><sup>2</sup> of 0.999 for both protein fragments and 20 amino acid dipeptide test sets. The long-range electrostatic and dispersion energies can be obtained using the E3NN-predicted parameters, with an error of 0.07 and 0.02 kcal/mol, respectively, when compared to symmetry-adapted perturbation theory (SAPT). Therefore, our force fields demonstrate the capability to accurately describe long-range interactions in proteins, paving the way for next-generation protein force fields. Force field (fiction) ; Multipole expansion ; Range (aeronautics) ; Artificial neural network ; Differentiable function ; Transferability ; Computer science ; Perturbation theory (quantum mechanics) ; Electrostatics ; Statistical physics ; Chemistry ; Physics ; Biological system ; Artificial intelligence ; Machine learning ; Mathematics ; Materials science ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Feifei Yu | Xia Xiang | Canhui Xu | Xiaotao Zu | Shuanglin Hu
Source: International Journal of Hydrogen Energy (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

Mechanical properties and hydrogen diffusion behaviors of metal hydrides from molecular dynamics simulations Molecular dynamics ; Hydrogen ; Diffusion ; Metal ; Materials science ; Chemical physics ; Dynamics (music) ; Chemistry ; Thermodynamics ; Computational chemistry ; Metallurgy ; Physics ; Organic chemistry Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Advanced Chemical Physics Studies
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: The Journal of Physical Chemistry C (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Prediction of the Diffusion Coefficient through Machine Learning Based on Transition-State Theory Descriptors Nanoporous materials serve as very effective media for storing and separating small molecules. To design the best materials for a given application based on adsorption, one usually assesses the equilibrium performance by using key thermodynamic quantities such as Henry constants or adsorption loading values. To go beyond standard methodologies, we probe here the transport effects occurring in the material by studying the self-diffusion coefficients of xenon inside the nanopores of the framework materials. We find good correlations between the diffusion coefficients and the pore aperture size as well as other geometrical and energetic descriptors. We used extensive molecular dynamics simulations to calculate the diffusion coefficient of xenon in 4873 MOFs from the CoRE MOF 2019 database, the first large-scale database of transport properties published at this scale. Based on these data, we present a tool to quickly evaluate the diffusion energy barrier that proved to be very correlated to the diffusion rate. This descriptor, alongside other geometrical characterizations, was then used to build a machine learning model that can predict the xenon diffusion coefficients in MOFs. The final trained model is quite accurate and shows a root-mean-square error on the log10 of the diffusion coefficient equal to 0.25. Diffusion ; Transition (genetics) ; State (computer science) ; Transition state theory ; Statistical physics ; Effective diffusion coefficient ; Computer science ; Machine learning ; Artificial intelligence ; Thermodynamics ; Chemistry ; Physics ; Algorithm ; Classical mechanics Machine Learning in Materials Science ; Grey System Theory Applications ; Environmental Impact and Sustainability
Authors: Mohammed Al‐Fahdi | Ming Hu
Source: Journal of Applied Physics (article, 2024-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic devices such as high electron mobility transistors, heat dissipation has become a crucial design consideration due to the ultrahigh heat flux that has a negative effect on devices' performance and their lifetime. Therefore, thermal transport performance enhancement is required to adapt to the device size reduction. β-Ga2O3 has recently gained significant scientific interest for future power devices because of its inherent material properties such as extremely wide bandgap, outstanding Baliga's figure of merit, large critical electric field, etc. This work aims to use a machine learning approach to search promising substrates or heat sinks for cooling β-Ga2O3, in terms of high interfacial thermal conductance (ITC), from large-scale potential structures taken from existing material databases. With the ITC dataset of 1633 various substrates for β-Ga2O3 calculated by full density functional theory, we trained our recently developed convolutional neural network (CNN) model that utilizes the fused orbital field matrix (OFM) and composition descriptors. Our model proved to be superior in performance to traditional machine learning algorithms such as random forest and gradient boosting. We then deployed the CNN model to predict the ITC of 32 716 structures in contact with β-Ga2O3. The CNN model predicted the top 20 cubic and noncubic substrates with ITC on the same level as density functional theory (DFT) results on β-Ga2O3/YN and β-Ga2O3/MgO interfaces, which has the highest ITC of 1224 and 1211 MW/m2K, respectively, among the DFT-ITC datasets. Phonon density of states, group velocity, and scattering effect on high heat flux transport and consequently increased ITC are also investigated. Moderate to high phonon density of states overlap, high group velocity, and low phonon scattering are required to achieve high ITC. We also found three Magpie descriptors with strong Pearson correlation with ITC, namely, mean atomic number, mean atomic weight, and mean ground state volume per atom. Calculations of such descriptors are computationally efficient, and therefore, these descriptors provide a new route for quickly screening potential substrates from large-scale material pools for high-performance interfacial thermal management of high-electron mobility transistor devices. Throughput ; Convolutional neural network ; Substrate (aquarium) ; Computer science ; High-throughput screening ; Materials science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Biology ; Biochemistry ; Telecommunications Ga2O3 and related materials ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Lai Wei | Q. Li | Sadman Sadeed Omee | Jianjun Hu
Source: Computational Materials Science (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Towards quantitative evaluation of crystal structure prediction performance Computer science ; Set (abstract data type) ; Strengths and weaknesses ; Field (mathematics) ; Quality (philosophy) ; Data mining ; Similarity (geometry) ; Machine learning ; Source code ; Code (set theory) ; Energy (signal processing) ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Nan Dai | Tingting Yu | Fangzheng Chen | Dan Song | Song Wang | Kai Zhang | Xiaoying Liu | Wenxin Dong | Yuxin Zhang | Hongye Li
Source: Journal of environmental chemical engineering (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Alkali-modified hydrothermal carbon-La@D composite with adjustable surface characteristics for efficient adsorption of Tetracycline Adsorption ; Composite number ; Hydrothermal circulation ; Alkali metal ; Tetracycline ; Carbon fibers ; Chemistry ; Activated carbon ; Chemical engineering ; Materials science ; Composite material ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Xinyi Zhao | Zihan Liu | Yuai Duan | Zhenzhen Xu | Xiao Feng | Zhong‐Feng Li | Tianyu Han
Source: Chemical Engineering Journal (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Constructing fingerprint sweat pore database based on donor–acceptor luminescent materials for personal identification Fingerprint (computing) ; Identification (biology) ; Luminescence ; Acceptor ; Materials science ; Computer science ; Computer security ; Optoelectronics Luminescence and Fluorescent Materials ; Forensic Fingerprint Detection Methods ; Polydiacetylene-based materials and applications
Authors: Ye Ding | Jing Huang
Source: International Journal of Molecular Sciences (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field. Plug-in ; Computer science ; Molecular dynamics ; Force field (fiction) ; Thermodynamic integration ; Computational science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Isabel Cooley | Samuel Boobier | Jonathan D. Hirst | Elena Besley
Source: Communications Chemistry (article, 2024-05-08)
Field: Chemistry — Inorganic Chemistry

Machine learning insights into predicting biogas separation in metal-organic frameworks Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R<sup>2</sup> values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets. Metal-organic framework ; Biogas ; Separation (statistics) ; Materials science ; Environmental science ; Artificial intelligence ; Process engineering ; Computer science ; Machine learning ; Chemistry ; Engineering ; Waste management ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Catalytic Processes in Materials Science
Authors: Yashar Shahedi | Mohsen Zandi | Mandana Bimakr
Source: Heliyon (article, 2024-10-01)
Field: Chemistry — Analytical Chemistry

A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90 % accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties. Machine learning ; Artificial intelligence ; Algorithm ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Anas Siddiqui | Nicholas D. M. Hine
Source: npj Computational Materials (article, 2024-08-03)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys Abstract Machine Learned Interatomic Potentials (MLIPs) combine the predictive power of Density Functional Theory (DFT) with the speed and scaling of interatomic potentials, enabling theoretical spectroscopy to be applied to larger and more complex systems than is possible with DFT. In this work, we train an MLIP for quaternary Transition Metal Dichalcogenide (TMD) alloy systems of the form Mo 1− x W x S 2−2 y Se 2 y , using the equivariant Neural Network (NN) MACE. We demonstrate the ability of this potential to calculate vibrational properties of alloy TMDs including phonon spectra for pure monolayers, and Vibrational Density of States (VDOS) and first-order Raman spectra for alloys across the range of x and y . We show that we retain DFT level accuracy while greatly extending feasible system size and extent of sampling over alloy configurations. We are able to characterize the first-order Raman active modes across the whole range of concentration, particularly for the “disorder-induced” modes. Raman spectroscopy ; Density functional theory ; Interatomic potential ; Materials science ; Phonon ; Transition metal ; Alloy ; Molecular dynamics ; Molecular physics ; Condensed matter physics ; Computational chemistry ; Physics ; Chemistry ; Optics 2D Materials and Applications ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Debdipto Acharya | Omar Abou El Kheir | Davide Campi | Marco Bernasconi
Source: Scientific Reports (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe$$_2$$-like superlattices for phase change memories Superlattices made of alternating blocks of the phase change compound Sb[Formula: see text]Te[Formula: see text] and of TiTe[Formula: see text] confining layers have been recently proposed for applications in neuromorphic devices. The Sb[Formula: see text]Te[Formula: see text]/TiTe[Formula: see text] heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb[Formula: see text]Te[Formula: see text] suffers from a low data retention due to a low crystallization temperature T[Formula: see text]. Substituting Sb[Formula: see text]Te[Formula: see text] with a phase change compound with a higher T[Formula: see text], such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe[Formula: see text] superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe[Formula: see text] heterostructures in neuromorphic devices with superior data retention. Superlattice ; Kinetics ; Crystallization ; Materials science ; Phase-change memory ; Phase change ; Phase (matter) ; Crystallography ; Thermodynamics ; Chemistry ; Physics ; Optoelectronics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Yong Qiu | Letian Chen | Xu Zhang | Dehai Ping | Yun Tian | Zhen Zhou
Source: AIChE Journal (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

A universal machine learning framework to automatically identify high‐performance covalent organic framework membranes for CH<sub>4</sub>/H<sub>2</sub> separation Abstract A universal machine learning framework is proposed to predict and classify membrane performance efficiently and accurately, achieved by combining classical density functional theory and string method. Through application of this framework, we conducted high‐throughput computations under industrial conditions, utilizing an extensive database containing nearly 70,000 covalent organic framework (COF) structures for CH 4 /H 2 separation. The best‐performing COF identified surpasses the materials reported in the previously documented MOF and COF databases, exhibiting an impressive adsorption selectivity for CH 4 /H 2 exceeding 82 and a membrane selectivity reaching as high as 248. More impressively, some of the best candidates identified from this framework have been verified through previous experimental works. Furthermore, the automated machine learning framework and its corresponding scoring system not only enable rapid identification of promising membrane materials from a vast material space but also contribute to a comprehensive understanding of the governing mechanisms that determine separation performance. Membrane ; Covalent bond ; Covalent organic framework ; Separation (statistics) ; Computer science ; Chemistry ; Artificial intelligence ; Machine learning ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Frederick Stein | Jürg Hutter
Source: The Journal of Chemical Physics (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Massively parallel implementation of gradients within the random phase approximation: Application to the polymorphs of benzene The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its Resolution-of-the-Identity (RI) approach, it is a very accurate and more cost-effective method than most other wavefunction-based correlation methods. For widespread applications, efficient implementations of nuclear gradients for structure optimizations and data sampling of machine learning approaches are required. We report a well scaling implementation of RI-RPA nuclear gradients on massively parallel computers. The approach is applied to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and estimations of error bars. Random phase approximation ; Extrapolation ; Massively parallel ; Computer science ; Scaling ; Statistical physics ; Wave function ; Computational science ; Parallel computing ; Algorithm ; Physics ; Mathematics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced NMR Techniques and Applications