jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunqi Cai | Jiangnan Li | Dong Wang
Source: arXiv (Cornell University) (preprint, 2024-10-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

NeuralMAG: Fast and Generalizable Micromagnetic Simulation with Deep Neural Nets Micromagnetics has made significant strides, particularly due to its wide-ranging applications in magnetic storage design. Numerical simulation is a cornerstone of micromagnetics research, relying on first-principle rules to compute the dynamic evolution of micromagnetic systems based on the renowned LLG equation, named after Landau, Lifshitz, and Gilbert. However, simulations are often hindered by their slow speed. Although Fast-Fourier transformation (FFT) calculations reduce the computational complexity to O(NlogN), it remains impractical for large-scale simulations. In this paper, we introduce NeuralMAG, a deep learning approach to micromagnetic simulation. Our approach follows the LLG iterative framework but accelerates demagnetizing field computation through the employment of a U-shaped neural network (Unet). The Unet architecture comprises an encoder that extracts aggregated spins at various scales and learns the local interaction at each scale, followed by a decoder that accumulates the local interactions at different scales to approximate the global convolution. This divide-and-accumulate scheme achieves a time complexity of O(N), significantly enhancing the speed and feasibility of large-scale simulations. Unlike existing neural methods, NeuralMAG concentrates on the core computation rather than an end-to-end approximation for a specific task, making it inherently generalizable. To validate the new approach, we trained a single model and evaluated it on two micromagnetics tasks with various sample sizes, shapes, and material settings. Artificial neural network ; Micromagnetics ; Deep neural networks ; Artificial intelligence ; Computer science ; Physics Magnetic Properties and Applications ; Computational Physics and Python Applications ; Electromagnetic Simulation and Numerical Methods
Authors: Afshin Farahbakhsh | Javad Mohebbi Najm Abad | Amin Hekmatmanesh | Heikki Handroos
Source:  (preprint, 2024-11-12)
Field: Chemistry — Electrochemistry

Evaluating the Performance of Ethanol Electrochemical Nanobiosensor through Machine for Predictive Analysis of Electric Current in Self-Powered Biosensors In this study, the focus is on ethanol nano biosensors based on alcohol oxidase (AOX) enzymatic reactions and the feasibility of generating electric current for bio batteries. The aim is to convert the latent energy in ethanol into electrical energy through the enzymatic oxidation process in the presence of AOX enzyme. The release of electrons and the creation of a potential difference make the use of ethanol as a bio fuel cell (BFC)/self-power biosensor in biologically sensitive systems feasible. To achieve this, glassy carbon electrodes were modified with gold nanoparticles to enhance conductivity, and the AOX enzyme was immobilized on the working electrode. The current generated through the enzymatic process was measured in various pH and analyte concentration conditions. Afterwards, machine learning models, including MLP, DNN, DT, and RF, were employed to assess the impact of parameters on electric current generation, evaluate the error rate, and compare the results. The results indicated that the MLP model was the most suitable method for predicting the electric current produced under different pH, temperature, and ethanol concentration values. These findings can be utilized to identify optimal conditions and increase the current output for use as a reliable energy source in self-powered biosensors. In conclusion, this study suggests a promising way to generate electricity by oxidizing ethanol with the AOX enzyme. The use of machine learning to analyze experimental data has provided insight into optimal conditions for maximizing electric current output for developing sustainable energy sources in biologically sensitive systems and bio battery technology. Current (fluid) ; Biosensor ; Nanotechnology ; Materials science ; Engineering ; Electrical engineering Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Dongbo Zhao | Yilin Zhao | Enhua Xu | Wenqi Liu | Paul W. Ayers | Shubin Liu | Dahua Chen
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates Simultaneous prediction of the molecular response properties, such as polarizability and the NMR shielding constant, at a low computational cost is an unresolved issue. We propose to combine a linear-scaling generalized energy-based fragmentation (GEBF) method and deep learning (DL) with both molecular and atomic information-theoretic approach (ITA) quantities as effective descriptors. In GEBF, the total molecular polarizability can be assembled as a linear combination of the corresponding quantities calculated from a set of small embedded subsystems in GEBF. In the new GEBF-DL(ITA) protocol, one can predict subsystem polarizabilities based on the corresponding molecular wave function (thus electron density and ITA quantities) and DL model rather than calculate them from the computationally intensive coupled-perturbed Hartree-Fock or Kohn-Sham equations and finally obtain the total molecular polarizability via a linear combination equation. As a proof-of-concept application, we predict the molecular polarizabilities of large proteins and protein aggregates. GEBF-DL(ITA) is shown to be as accurate enough as GEBF, with mean absolute percentage error &lt;1%. For the largest protein aggregate (&gt;4000 atoms), GEBF-DL(ITA) gains a speedup ratio of 3 compared with GEBF. It is anticipated that when more advanced electronic structure methods are used, this advantage will be more appealing. Moreover, one can also predict the NMR chemical shieldings of proteins with reasonably good accuracy. Overall, the cost-efficient GEBF-DL(ITA) protocol should be a robust theoretical tool for simultaneously predicting polarizabilities and NMR shieldings of large systems. Polarizability ; Macromolecule ; Molecular dynamics ; Electromagnetic shielding ; Nuclear magnetic resonance spectroscopy ; Wave function ; Molecule ; Speedup ; Density functional theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: A. D. Podobrazhnykh | T. A. Eremina | Ekaterina I. Marchenko | N. N. Eremin
Source: Moscow University Geology Bulletin (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Crystal-Chemical Indicators of Specification of Oxocentered Tetrahedra in Divalent Lead Minerals A complex crystal-chemical analysis of 1567 oxygen positions in 216 crystal structures of lead-containing minerals assigned to the classes silicates, phosphates, oxides, oxohalides, and a number of others has been performed. It is shown that the combined application of the bond valence method and analysis of geometric characteristics of Voronoi–Dirichlet polyhedra makes it possible to reliably distinguish between oxygen positions related to cation structure blocks and oxygen positions, for which a description based on an anion-centered approach is more reasonable. Numerical indicators for such separation of oxygen positions are substantiated, which can be used to analyze large arrays of structural data, using the machine learning approaches. Divalent ; Geology ; Geochemistry ; Crystal (programming language) ; Lead (geology) ; Tetrahedron ; Mineral ; Crystal chemistry ; Mineralogy ; Crystallography ; Crystal structure ; Chemistry ; Materials science ; Metallurgy ; Paleontology ; Computer science X-ray Diffraction in Crystallography ; Crystal Structures and Properties ; Railway Systems and Materials Science
Authors: Mathieu Garrigues | Victor Onofre | Noé Bosc-Haddad
Source: arXiv (Cornell University) (preprint, 2024-02-09)
Field: Chemistry — Physical and Theoretical Chemistry

Towards molecular docking with neutral atoms New computational strategies, such as molecular docking, are emerging to speed up the drug discovery process. This method predicts the activity of molecules at the binding site of proteins, helping to select the ones that exhibit desirable behavior and rejecting the rest. However, for large chemical libraries, it is essential to search and score configurations using fewer computational resources while maintaining high precision. In this work, we map the molecular docking problem to a graph problem, a maximum-weight independent set problem on a unit-disk graph in a physical neutral atom quantum processor. Here, each vertex represents an atom trapped by optical tweezers. The Variational Quantum Adiabatic Algorithm (VQAA) approach is used to solve the generic graph problem with two optimization methods, Scipy and Hyperopt. Additionally, a machine learning method is explored using the adiabatic algorithm. Results for multiple graphs are presented, and a small instance of the molecular docking problem is solved, demonstrating the potential for near-term quantum applications. Docking (animal) ; Energetic neutral atom ; Chemistry ; Medicine ; Ion Chemical Reactions and Mechanisms ; Porphyrin and Phthalocyanine Chemistry
Authors: Tian‐E Fan | Yanru Zhang | Hanyu Li | Hao-Ran Lei
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Structure Optimization of Uio-66 Supported Metal Nanoclusters Based on High-Dimensional Neural Network Nanoclusters ; Artificial neural network ; Materials science ; Metal ; Computer science ; Nanotechnology ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Tian‐E Fan | Hanyu Li | Yanru Zhang | Pingan Yang
Source: Surfaces and Interfaces (article, 2024-12-21)
Field: Materials Science — Materials Chemistry

Structure optimization of UiO-66 supported metal nanoclusters based on high-dimensional neural network Materials science ; Nanoclusters ; Artificial neural network ; Metal ; Nanotechnology ; Chemical engineering ; Metallurgy ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Alexandr Fonari | Garvit Agarwal | Subodh Tiwari | Casey N. Brock | Jacob Gavartin | Mathew D. Halls
Source: Computational Materials Science (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Robust and effective ab initio molecular dynamics simulations on the GPU cloud infrastructure using the Schrödinger Materials Science Suite Suite ; Cloud computing ; Molecular dynamics ; Ab initio ; Ab initio quantum chemistry methods ; Computational science ; Statistical physics ; Physics ; Computational chemistry ; Materials science ; Computer science ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Suman Hazra | Urvesh Patil | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2024-01-12)
Field: Materials Science — Materials Chemistry

Predicting The One-Particle Density Matrix With Machine Learning Two of the most widely used electronic structure theory methods, namely Hartree-Fock and Kohn-Sham density functional theory, both requires the iterative solution of a set of Schrödinger-like equations. The speed of convergence of such self-consistent field process depends on the complexity of the system under investigation and on the initial guess for the density matrix. An initial density matrix close to the ground-state one will effectively allow one to cut out many of the self-consistent steps necessary to achieve convergence. Here, we predict the density matrix of Kohn-Sham density functional theory by constructing a neural network, which uses the atomic positions as only information. Such neural network provides an initial guess for the density matrix far superior to any other recipes available, in particular for molecules with metallic bonds. Furthermore, the quality of such neural-network density matrix is good enough for the evaluation of interatomic forces. This allows us to run accelerated {\it ab-initio} molecular dynamics with little to no self-consistent steps. Density matrix ; Convergence (economics) ; Artificial neural network ; Matrix (chemical analysis) ; Density functional theory ; State-transition matrix ; Statistical physics ; Computer science ; Set (abstract data type) ; Field (mathematics) ; Algorithm ; Applied mathematics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Materials science ; Symmetric matrix ; Pure mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: P. V. Badikova | D. V. Zav’yalov | V. I. Konchenkov | D.V. Shein
Source:  (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

STUDYING THERMAL PROPERTIES OF PHOSPHORENE ON A SIO2 SUBSTRATE USING DEEP LEARNING METHODS This paper studies the stability and changes in thermal properties of multilayer black phosphorene near a silicon dioxide substrate using molecular dynamics methods. We use deep learning approaches to create a model of the particle interaction force field. The results show that at the interface of materials, substances lose their structure and partially diffuse. However, the further the subsequent phosphorene layers are from the interface, the more its crystalline structure is preserved. The obtained values of the sound velocity in phosphorene in the «armchair» and «zigzag» directions are 3715 m/s and 8415 m/s, respectively. Phosphorene ; Computer science ; Substrate (aquarium) ; Materials science ; Deep learning ; Thermal ; Optoelectronics ; Electronic engineering ; Artificial intelligence ; Physics ; Geology ; Engineering Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Karthik K. Srinivasan | Anjana Puliyanda | Vinay Prasad
Source: ChemRxiv (preprint, 2024-02-28)
Field: Chemistry — Spectroscopy

Identification of reaction network hypotheses for complex feedstocks from spectroscopic measurements with minimal human intervention In this work we detail an automated reaction network hypothesis generation protocol for processes involving complex feedstocks where information about the species and reactions involved is unknown. Our methodology is process agnostic and can be utilized in any reactive process with spectroscopic measurements that provide information on the evolution of the components in the mixture. We decompose the mixture spectra to obtain spectroscopic signatures of the individual components and use a 1-d convolutional neural network to automatically identify functional groups indicated by them. We employ atom-atom mapping to automatically recover reaction rules that are applied on candidate molecules identified from chemistry databases through fingerprint similarity. The method is tested on synthetic data and on spectroscopic measurements of lab-scale batch Hydrothermal Liquefaction (HTL) of biomass to determine the accuracy of prediction across datasets of varying complexities . Our methodology is able to identify reaction network hypotheses containing reaction networks close to the ground truth in the case of synthetic data and we are also able to recover candidate molecules and reaction networks close to the ones reported in previous literature studies for biomass pyrolysis. Identification (biology) ; Intervention (counseling) ; Computer science ; Computational biology ; Biological system ; Psychology ; Biology ; Botany Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Yu‐Xuan Ren | Dong Zheng | Chang Liu | Peng Jin | Yu Shi | Lin Huang | J. He | Shengjie Luo | Tao Qin | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data. Consistency (knowledge bases) ; Task (project management) ; Computer science ; Data consistency ; Artificial intelligence ; Distributed computing ; Engineering ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Madeeha Batool | Naveed Ahmed Azam | Jianshen Zhu | Kazuya Haraguchi | Liang Zhao | Tatsuya Akutsu
Source: arXiv (Cornell University) (preprint, 2024-09-06)
Field: Chemistry — Spectroscopy

A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors, multiple linear regression (MLR) and mixed integer linear programming (MILP). Selected descriptors based on a forward stepwise procedure enabled the simplest regression model, MLR, to achieve significantly good prediction accuracy compared to the existing approaches, achieving the accuracy in the range [0.7191, 0.9377] for 29 diverse datasets. By simulating these descriptors and learning models as MILPs, we inferred mathematically exact and optimal compounds with the desired AS, prescribed structures, and up to 50 non-hydrogen atoms in a reasonable time range [6, 1204] seconds. These findings indicate a strong correlation between the simple graph-theoretic descriptors and the AS of compounds, potentially leading to a deeper understanding of their AS without relying on widely used complicated chemical descriptors and complex machine learning models that are computationally expensive, and therefore difficult to use for inference. An implementation of the proposed approach is available at https://github.com/ku-dml/mol-infer/tree/master/AqSol. Solubility ; Aqueous solution ; Biochemical engineering ; Chemistry ; Computer science ; Process engineering ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography
Authors: A. Simon | Luc Belloni | Daniel Borgis | Martin Oettel
Source: arXiv (Cornell University) (preprint, 2024-11-11)
Field: Materials Science — Materials Chemistry

The orientational structure of a model patchy particle fluid: simulations, integral equations, density functional theory and machine learning We investigate the orientational properties of a homogeneous and inhomogeneous tetrahedral 4-patch fluid (Kern--Frenkel model). Using integral equations, either (i) HNC or (ii) a modified HNC scheme with simulation input, the full orientational dependence of pair and direct correlation functions is determined. Density functionals for the inhomogeneous problem are constructed via two different methods. The first, molecular density functional theory, utilizes the full direct correlation function and an isotropic hard-sphere bridge functional. The second method, a machine learning approach, uses a decomposition of the functional into an isotropic reference part and a mean-field orientational part, where both parts are improved by machine learning techniques. Comparison to simulation data at hard walls and around hard tracers show a similar performance of the two functionals. Machine learning strategies are discussed to eliminate residual differences, with the goal of obtaining machine-learning enhanced functionals for the general anisotropic fluid. Particle (ecology) ; Physics ; Density functional theory ; Classical mechanics ; Integral equation ; Statistical physics ; Mathematics ; Mathematical analysis ; Geology ; Quantum mechanics Pickering emulsions and particle stabilization
Authors: Jian Li | MingDe Shen | Zhiwei Zhou | RuiQiang Bai
Source: Research in Cold and Arid Regions (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

Data-driven modeling of failure envelope surface of ice materials The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering. In this investigation, six machine learning methods were utilized to predict the strength of the envelope surface of ice materials. The database for the ice strength was first established by collecting 1,481 testing data reported in the previous literatures. A quadric strength criterion was adopted to describe failure behaviors of ice materials under different conditions of material property and laboratory. Three model parameters in this strength criterion were forecasted by using six machine learning methods. The prediction capacities of six machine learning methods were evaluated by three statics indices, and the integrated simulation ability of six machine learning methods was arranged. Three machine learning algorithms were selected to be improved and optimized, and the simulation capacity of the three algorithms was further explored. The optimization results indicate that the improved potential of the Ensemble algorithm is much higher than that of the SVM algorithm and the GPR algorithm for predicting the ice strength. Computer science ; Machine learning ; Artificial intelligence Material Properties and Failure Mechanisms ; Coal and Coke Industries Research