jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifan He | Rui Sun | Shihao Tang | Zilong Xu | Long Hou | Chaohao Hu | Yuqin Liu | Chengying Tang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Design and Preparation of Novel Fe-Si-B-P-C-Nb Amorphous /Nanocrystalline Alloys Guided by Machine Learning Nanocrystalline material ; Amorphous solid ; Materials science ; Chemical engineering ; Metallurgy ; Nanotechnology ; Crystallography ; Chemistry ; Engineering Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Xinyuan Xu | Li‐Gao Liu | Li‐Cheng Xu | Shuo‐Qing Zhang | Xin Hong
Source: ChemRxiv (preprint, 2024-11-28)
Field: Chemistry — Organic Chemistry

Proximity Learning-Enabled Ligand Prediction for Ni-Catalyzed Atroposelective Suzuki-Miyaura Cross-Coupling: Leveraging Pd Catalysis Knowledge for Ni Discovery The rational design of novel molecular catalysts often confronts challenges due to complex structure-performance relationships. Emerging data-driven approaches provide revolutionary solutions, yet the application of machine learning to new catalyst development inevitably faces a low-data regime, with limited effective structure-performance modeling available. In this study, we present a proximity learning strategy to facilitate knowledge transfer from well-documented Pd catalysis to novel, underexplored Ni systems. By synergistically modeling extensive palladium catalysis data with limited nickel/SadPhos data, our approach accurately predicted novel SadPhos ligands, enabling the first atroposelective nickel-catalyzed Suzuki-Miyaura cross-coupling reaction. The synthetic utility of the machine learning-predicted ligand was further demonstrated in the broad synthetic scope, gram-scale synthesis, and precise control of dual axial chiralities in ternaphthalene through the sequential coupling under Ni and Pd catalysis. Additionally, density functional theory calculations were employed to reveal the reaction mechanism and stereochemical model of this new catalytic system, validating the proposed mechanistic connection between Ni and Pd. This work demonstrates how machine learning models can effectively leverage mechanistic connectivity, applying extensive structure-performance relationship data from the literature to predict new catalysts, providing a novel strategy for the rational design of molecular catalysts from a few-shot learning perspective. Catalysis ; Coupling (piping) ; Ligand (biochemistry) ; Chemistry ; Combinatorial chemistry ; Computer science ; Materials science ; Organic chemistry ; Biochemistry ; Receptor Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality
Authors: Olena Lenchuk | Jochen Rohrer | Karsten Albe
Source: Advanced Engineering Materials (article, 2024-05-05)
Field: Materials Science — Materials Chemistry

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena A machine‐learning interatomic potential for Mo–Si alloys based on the atomic cluster expansion formalism is presented, its performance is validated, and it is applied for studying interface phenomena. Structural parameters, elastic constants, and melting temperatures of the crystalline body‐centered cubic Mo, diamond Si, and stable Mo–Si alloys (Mo 3 Si, Mo 5 Si 3 , and MoSi 2 ) are calculated and compared to experimental values. Using the trained potential defect, formation energies are calculated and the thermodynamic stability of various Mo x Si y alloys is discussed with focus on Mo 3 Si. Finally, the intermixing between Mo and Si phases is studied by performing interface simulations of Mo|Si. The crystallization behavior of the Mo 3 Si phase provides additional evidence for the off‐stoichiometric composition of this intermetallic phase. Materials science ; Interface (matter) ; Thermodynamics ; Interatomic potential ; Statistical physics ; Molecular dynamics ; Physics ; Chemistry ; Computational chemistry ; Surface tension Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Intermetallics and Advanced Alloy Properties
Authors: Yumi Morishita | Misato Yarimizu | M. Kaneko | Azusa Muraoka
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring and Proposing of Donor Molecules for Fullerene-Based Organic Thin-Film Solar Cells Using Machine Learning Fullerene ; Organic solar cell ; Organic molecules ; Materials science ; Molecule ; Thin film ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Polymer Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Organic Electronics and Photovoltaics
Authors: Joshua Ojih | Mohammed Al‐Fahdi | Yagang Yao | Jianjun Hu | Ming Hu
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Correction: Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage Correction for ‘Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage’ by Joshua Ojih et al. , J. Mater. Chem. A , 2024, 12 , 8502–8515, https://doi.org/10.1039/D3TA06190F. Computer science ; Throughput ; Graph theory ; Graph ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Combinatorics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Radioactive element chemistry and processing
Authors: Mingjun Han | Yukai Zhang | Taotao Yu | Guodong Du | ChiYung Yam | Ho-Kin Tang
Source: arXiv (Cornell University) (preprint, 2024-10-24)
Field: Materials Science — Materials Chemistry

Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using the K-nearest neighbors algorithm. Two training strategies are explored: one based on experimental data, and the other based on the offset between molecular dynamics (MD) simulations and experimental measurements. The latter approach yields a substantial improvement in predictive accuracy, achieving a mean unsigned error (MUE) of 0.64 kcal/mol. Feature analysis identifies molecular geometry and topology as the most critical factors in predicting alchemical free energy, supporting the established theory that surface tension is a key determinant. Furthermore, the feature analysis of offset results highlights the relevance of charge distribution within the system, which correlates with the inaccuracies in force fields employed in MD simulations and may provide guidance for improving force field designs. These results suggest that machine learning approaches can effectively capture the complex features governing solvation free energy, offering novel pathways for enhancing predictive accuracy. Feature (linguistics) ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Machine learning ; Chemical physics ; Chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: John Isaac Enriquez | Harry Handoko Halim | Takahiro Yamasaki | M. Michiuchi | Kouji Inagaki | Masaaki Geshi | Ikutaro Hamada | Yoshitada Morikawa
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Origin of the Surface Facet Dependence in the Thermal Degradation of the Diamond (111) and (100) Surfaces in Vacuum Investigated by Machine Learning Molecular Dynamics Simulations Facet (psychology) ; Molecular dynamics ; Degradation (telecommunications) ; Diamond ; Surface (topology) ; Thermal ; Chemical physics ; Materials science ; Condensed matter physics ; Composite material ; Chemistry ; Physics ; Computational chemistry ; Engineering ; Thermodynamics ; Geometry ; Psychology ; Electronic engineering ; Mathematics Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques ; Surface and Thin Film Phenomena
Authors: Debmalya Ray
Source: Research Square (preprint, 2024-01-23)
Field: Materials Science — Materials Chemistry

Material Type Prediction Using Machine Learning Techniques Abstract In materials science, traditional experimental and computational approaches require the investment of enormous amounts of time and resources, and the experimental conditions limit the use of these methods. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary waste of time and resources. Type (biology) ; Artificial intelligence ; Computer science ; Machine learning ; Geology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Shuhao Ma | Dechang Li | Xuejin Li | Guoqing Hu
Source: The Journal of Chemical Physics (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Neural network-assisted model of interfacial fluids with explicit coarse-grained molecular structures Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical droplets and exhibit a complex behavior due to their nonlinear, multiphase, and multicomponent nature. The development of accurate coarse-grained (CG) models for such systems poses significant challenges, as these models must effectively capture the intricate many-body interactions, both inter- and intramolecular, arising from atomic-level phenomena, and account for the diverse density distributions and fluctuations at the interface. In this study, we use advanced machine learning techniques incorporating force matching and diffusion probabilistic models to construct a robust CG model of interfacial fluids. We evaluate our model through simulations in various settings, including the water-air interface, bulk decane, and dipalmitoylphosphatidylcholine monolayer membranes. Our results show that our CG model accurately reproduces the essential many-body and interfacial properties of interfacial fluids and proves effective across different CG mapping strategies. This work not only validates the utility of our model for multiscale simulations, but also lays the groundwork for future improvements in the simulation of complex interfacial systems. Dipalmitoylphosphatidylcholine ; Computer science ; Complex fluid ; Nonlinear system ; Interface (matter) ; Molecular dynamics ; Multiscale modeling ; Complex system ; Water model ; Materials science ; Statistical physics ; Biological system ; Nanotechnology ; Membrane ; Chemistry ; Physics ; Artificial intelligence ; Mechanics ; Computational chemistry ; Wetting Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Yannik Köster | Julian Kimmig | Stefan Zechel | Ulrich S. Schubert
Source: STAR Protocols (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

Protocol for creating representations of molecular structures using a polymer-specific decoder To supply chemical structures of polymers for machine learning applications, decoding is necessary. Here, we present a protocol for generating polymer fingerprints (PFPs), which are representations of molecular structures, using a polymer-specific decoder. We outline steps for downloading, installing, and basic application of the software. Moreover, we present procedures for processing and analyzing polymer structure data and the preparation for integration into machine learning methods. On this basis, we explain how artificial neural networks can be utilized to predict polymer properties. For complete details on the use and execution of this protocol, please refer to Köster et al.<sup>1</sup>. Protocol (science) ; Computer science ; Theoretical computer science ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yu Zhang | Xintong Liu | Qiao Shi | Yongxiao Qu | Yongchao Hao | Yuequn Fu | Jianyang Wu | Zhisen Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Mechanical Properties and Cage Transformations in Co2-Ch4 Heterohydrates: A Molecular Dynamics and Machine Learning Study Cage ; Dynamics (music) ; Molecular dynamics ; Computer science ; Biological system ; Statistical physics ; Chemistry ; Physics ; Engineering ; Structural engineering ; Computational chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Boron and Carbon Nanomaterials Research
Authors: Yeting Zheng | Wei-Hong Yeo | Andrea Tomassini | Colin E. Hayter | Hao F. Zhang | Yang Zhang | Françisco M. Raymo
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Spectroscopic Single-Molecule Discrimination of BODIPY Fluorophores with Deep Learning BODIPY ; Molecule ; Photochemistry ; Chemistry ; Computer science ; Artificial intelligence ; Fluorescence ; Physics ; Optics ; Organic chemistry Luminescence and Fluorescent Materials ; Advanced Fluorescence Microscopy Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Kazuma Ito | Tatsuya Yokoi | Katsutoshi Hyodo | Hideki Mori
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Grain Size Effects on the Deformation of Α-Fe Nanopolycrystals: Massively Large-Scale Molecular Dynamics Simulations Using Machine Learning Interatomic Potential Molecular dynamics ; Massively parallel ; Deformation (meteorology) ; Scale (ratio) ; Grain size ; Dynamics (music) ; Materials science ; Interatomic potential ; Statistical physics ; Chemical physics ; Computer science ; Computational science ; Parallel computing ; Composite material ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Hossein Shirani | Seyed Majid Hashemianzadeh
Source: Research Square (preprint, 2024-02-26)
Field: Chemistry — Electrochemistry

Quantum-level machine learning calculations to predict the PES of Selegiline Abstract Many drug molecules contain functional groups, resulting in a torsional barrier corresponding to rotation around the bond linking the fragments. In medicinal chemistry and pharmaceutical sciences, inclusive of drug design studies, the exact calculation of the potential energy surface of these molecular torsions is extremely important and precious. Machine learning, including deep learning, is currently one of the most rapidly evolving tools in computer-aided drug discovery and molecular simulations. In this work, we used ANI-1x neural network potential as a quantum-level machine learning to predict the PESs of the Selegiline antiparkinsonian drug molecule. Also, DFT calculations at the wB97X/6-31G(d) level of theory have been used to study the structural parameters and vibrational normal modes of the Selegiline molecule. We succeeded in calculating the vibrational frequencies, electronic energy and optimization of the molecular structure of the Selegiline using the ANI-1x dataset in a very short computing cost. From this perspective, we expect the ANI-1x dataset applied in this work to be appreciably efficient and effective in computational structure-based drug design studies. Selegiline ; Quantum ; Quantum chemical ; Computer science ; Physics ; Medicine ; Quantum mechanics ; Internal medicine Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Xianglong Du | W. W. Shao | Chenglong Bao | Linfeng Zhang | Jun Cheng | Fujie Tang
Source: arXiv (Cornell University) (preprint, 2024-07-22)
Field: Chemistry — Analytical Chemistry

Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures and distinct interfacial water structures, theoretical calculations using molecular dynamics (MD) simulations are required. These MD simulations typically need relatively long trajectories (a few nanoseconds) to achieve reliable SFG response function calculations via the dipole-polarizability time correlation function. However, the requirement for long trajectories limits the use of computationally expensive techniques such as ab initio MD (AIMD) simulations, particularly for complex solid-water interfaces. In this work, we present a pathway for calculating vibrational spectra (IR, Raman, SFG) of solid-water interfaces using machine learning (ML)-accelerated methods. We employ both the dipole moment-polarizability correlation function and the surface-specific velocity-velocity correlation function approaches to calculate SFG spectra. Our results demonstrate the successful acceleration of AIMD simulations and the calculation of SFG spectra using ML methods. This advancement provides an opportunity to calculate SFG spectra for the complicated solid-water systems more rapidly and at a lower computational cost with the aid of ML. Interface (matter) ; Spectroscopy ; Molecular spectroscopy ; Materials science ; Computer science ; Molecule ; Computational science ; Chemical physics ; Chemistry ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alegre-Requena, Juan V. | Dalmau, David
Source: DIGITAL.CSIC (Spanish National Research Council (CSIC)) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automation of computational chemistry and machine learning workflows Resumen del trabajo presentado al Seminario del Institut de Química Computacional i Catàlisi (Universidad de Girona), celebrado el 29 de febrero de 2024. Workflow ; Automation ; Computational model ; Robustness (evolution) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Felix Pultar | Moritz Thuerlemann | Igor Gordiy | Eva Doloszeski | Sereina Riniker
Source: arXiv (Cornell University) (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution We present design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechanical/molecular-mechanical (QM/MM) simulations. Substitution of a computationally expensive QM Hamiltonian by a NNP with the same accuracy largely reduces the computational cost and enables efficient sampling in prospective MD simulations, the main limitation faced by traditional QM/MM set-ups. The model relies on the recently introduced anisotropic message passing (AMP) formalism to compute atomic interactions and encode symmetries found in QM systems. AMP is shown to be highly efficient in terms of both data and computational costs, and can be readily scaled to sample systems involving more than 350 solute and 40'000 solvent atoms for hundreds of nanoseconds using umbrella sampling. The performance and broad applicability of our approach are showcased by calculating the free-energy surface of alanine dipeptide, the preferred ligation states of nickel phosphine complexes, and dissociation free energies of charged pyridine and quinoline dimers. Results with this ML/MM approach show excellent agreement with experimental data. In contrast, free energies calculated with static high-level QM calculations paired with implicit solvent models or QM/MM MD simulations using cheaper semi-empirical methods show up to ten times higher deviation from the experimental ground truth and sometimes even fail to reproduce qualitative trends. Artificial neural network ; Resolution (logic) ; Computer science ; Biological system ; Artificial intelligence Machine Learning in Materials Science
Authors: Shengyu Zhang
Source: Theoretical and Natural Science (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Physics Models and Machine Learning in Molecular Dynamics Abstract. Tackling the complexity of particle interactions, this investigation introduces a unified computational methodology employing Moldy, Gnuplot, and Visual Molecular Dynamics (VMD). Inspired by the n-body problem's persistent intrigue, specifically the three-body dynamics within molecular systems, we leverage Molecular Dynamics (MD) simulations to forecast and scrutinize the nuanced behavior of atomic and molecular entities. Moldy, a flexible MD software, facilitated the simulation of particle trajectories and interactions across diverse scenarios, with meticulous documentation of the resulting data. For visual analytics and insight extraction, Gnuplot crafted detailed plots depicting kinetic and potential energies alongside other thermodynamic metrics over temporal scales. The integration of VMD culminated in our analysis by vividly portraying the molecular motions, enhancing comprehension of emergent patterns. This study not only endorses Moldy's precision in MD simulations but also highlights the potent alliance among simulation, analysis, and visualization in elucidating intricate particle interactions. In summary, our work underscores the efficacy of the Moldy-Gnuplot-VMD triad as a formidable resource for scientists engaged in molecular motion prediction and analysis, paving fresh paths in computational physics and chemistry research. Dynamics (music) ; Computer science ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics Machine Learning in Materials Science