jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Isao Kitagawa
Source: Journal of Computer Chemistry Japan (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The Analysis of Dissociation Process of Carbonic Acid in Aqueous by Using Molecular Dynamics with Machine Learning Potential For example， dissolved carbon dioxide and carbonic acid in aqueous solution are important compounds for life activities， serving as a buffer system for pH regulation in the body. Theoretical calculations for carbon dioxide and carbonic acid， and the transition states， which are the states mediated by water molecules have been vigorously performed， however， there are limitations in terms of computational speed and resources for exploring various conformations and reaction mechanisms in the water molecule network through ab initio calculations. The machine learning potential (MLP) was created by using the DeepMD framework， which is one of the MLPs that have been reported to be applied in various systems. We analyzed the conformation of the dissociation process of carbonic acid by using the Nudged Elastic Band method in the molecular dynamics (MD) calculations. The energy difference between MLP-MD and ab-initio MD (AIMD) at the beginning and the end of the reaction was 0.109 kcal/mol/atoms. Looking at the trend of the barrier value with respect to the number of water molecules in the water molecular network， the barrier value to decrease with the increase in the number of water molecules has similar tendency of the previous quantum chemical and AIMD calculations. Carbonic acid ; Dissociation (chemistry) ; Aqueous solution ; Molecular dynamics ; Chemistry ; Computational chemistry ; Computer science ; Chemical physics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Gavin Nop | Micah Mundy | Durga Paudyal | Jonathan D. Smith
Source: arXiv (Cornell University) (preprint, 2024-07-25)
Field: Materials Science — Materials Chemistry

Predicting quantum materials properties using novel faithful machine learning embeddings Machine Learning (ML) is accelerating the progress of materials prediction and classification, with particular success in CGNN designs. While classical ML methods remain accessible, advanced deep networks are still challenging to build and train. We introduce two new adaptations and refine two existing ML networks for generic crystalline quantum materials properties prediction and optimization. These new models achieve state-of-the-art performance in predicting TQC classification and strong performance in predicting band gaps, magnetic classifications, formation energies, and symmetry group. All networks easily generalize to all quantum crystalline materials property predictions. To support this, full implementations and automated methods for data handling and materials predictions are provided, facilitating the use of deep ML methods in quantum materials science. Finally, dataset error rates are analyzed using an ensemble model to identify and highlight highly atypical materials for further investigations. Computer science ; Quantum ; Artificial intelligence ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Sara Tkaczyk | Johannes Karwounopoulos | Andreas Schöller | H. Lee Woodcock | Thierry Langer | Stefan Boresch | Marcus Wieder
Source: ChemRxiv (preprint, 2024-03-19)
Field: Materials Science — Materials Chemistry

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have limited range of applicability and are significantly slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. In this study, we systematically investigate the accuracy, robustness, and efficiency of free energy calculations between a MM reference and a neural network target potential (ANI-2x) using free energy perturbation (FEP) and non-equilibrium (NEQ) switching simulation in vacuum. We investigate the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution. We compare the results to multistage equilibrium free energy calculations (MFES) for an established dataset. Our results demonstrate that free energy calculations between NNPs and MM potentials should not be performed using FEP but require NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between MM and NNPs are highly efficient, robust, and trivial to implement. Molecular dynamics ; Robustness (evolution) ; Energy (signal processing) ; Free energy perturbation ; Work (physics) ; Potential energy ; Statistical physics ; Outlier ; Artificial neural network ; Perturbation (astronomy) ; Physics ; Computer science ; Computational physics ; Chemistry ; Classical mechanics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Protein Structure and Dynamics
Authors: Jin Xiao | Yingfeng Zhang | Bowen Li | Handong Wang | John Z. H. Zhang | Tong Zhu
Source: ChemRxiv (preprint, 2024-12-25)
Field: Materials Science — Materials Chemistry

Machine Learning Based Density Functional Method for Chemical Reactions I: Organic Reactions The accurate computation of reaction energy and reaction barrier height is indispensable for the investigation of chemical reaction mechanisms and rates. In this research, we employ the Deep post-Hartree Fock (DeePHF) model in conjunction with the local density matrix derived from different base quantum chemistry methods (HF, PBE, B3LYP, M06-2X and $\omega$B97M-V). Through extensive training on high-precision small molecule reaction data, including reaction structures and high-precision NEB (Nudged Elastic Band) trajectories, our model showcases better performance across various reaction barrier height datasets compared to the common density functional theory(DFT). The precision of the model rivals that of the double hybrid functionals. Notably, DeePHF maintains high accuracy even when applied to small molecules, medium-sized molecules, and standard benchmark reactions datasets, displaying its substantial advantage in exploring reaction potential energy profiles. Benchmark (surveying) ; Density functional theory ; Molecule ; Density matrix ; Quantum chemistry ; Base (topology) ; Computational chemistry ; Quantum ; Matrix (chemical analysis) ; Chemistry ; Computer science ; Biological system ; Materials science ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Source: AM&P Technical Articles (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Machine Learning for Materials: Panel at IMAT 2024 Abstract This article describes highlights from a panel held at IMAT 2024. Representatives from industry, government, and academia discussed the potential that artificial intelligence and machine learning offer the materials science and manufacturing communities. David Furrer, Pratt &amp; Whitney, moderated the panel. The panelists were Seth Kimble, ASM International; Joshua Stuckner, NASA Glenn Research Center; James E. Saal, Citrine Informatics; and S. Mohadeseh Taheri-Mousavi, Carnegie Mellon University. Each panelist provided their perspective on how AI and ML are impacting current and future materials design and manufacturing processes. Artificial intelligence ; Computer science ; Machine learning Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Wangyu Hu | Guanlin Yang | Hexiang Peng | Jian Huang | Hongjian Chen | Shifang Xiao
Source: Research Square (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

Atomic perspective on plasticity mechanism of ionic-covalent systems from machine learning molecular dynamics simulations Mechanism (biology) ; Perspective (graphical) ; Molecular dynamics ; Ionic bonding ; Covalent bond ; Computer science ; Plasticity ; Cognitive science ; Chemical physics ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Computational chemistry ; Artificial intelligence ; Psychology ; Ion ; Thermodynamics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Crystallography and molecular interactions
Authors: Susmita Tripathy | Surajit Das | Shweta Jindal | Raghunathan Ramakrishnan
Source: arXiv (Cornell University) (preprint, 2024-05-30)
Field: Materials Science — Materials Chemistry

Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spectra of organic molecules originating from the $K$-shell ionization energies of carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed the training dataset through high-throughput calculations of $K$-shell core-electron binding energies (CEBEs) for 12,880 small organic molecules in the bigQM7$ω$ dataset, employing the $Δ$-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy within the $Δ$-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn--Sham eigenvalues. When applied to 208 combinatorially substituted uracil molecules larger than those in the training set, our analyses suggest that the models may not provide quantitatively accurate predictions of CEBEs but offer a strong linear correlation relevant for virtual high-throughput screening. We present the dataset and models as the Python module, ${\tt cebeconf}$, to facilitate further explorations. Molecule ; Space (punctuation) ; Organic molecules ; Chemical space ; Spectral line ; X-ray photoelectron spectroscopy ; X-ray ; Materials science ; Computer science ; Chemistry ; Physics ; Optics ; Nuclear magnetic resonance ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Various Chemistry Research Topics
Authors: Sampreeti Bhattacharya | Arkaprava Roy
Source: UNC Libraries (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods Lanthanide ; Stability (learning theory) ; Species richness ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Machine learning ; Geology ; Ion ; Organic chemistry ; Paleontology Machine Learning in Materials Science
Authors: Qizhen Du | Feng Wang | Hui Huang | Heng Fong Wan | Xiaoyu Wu | Chengkun Wu
Source:  (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Exploring Natural Language Processing Model Acceleration in Molecular Dynamics Simulation Using High-Performance Computing and Machine Learning In molecular dynamics simulations, the integral methods used to calculate molecular trajectories, such as the Verlet integral method, involve significant computational costs. On the premise of ensuring the accuracy of simulation, how to effectively reduce the amount of computation is always a challenging problem. This paper applies Natural Language Processing models to simulate molecular motion trajectories in molecular dynamics, integrating the MPI programming model and machine learning on a high-performance computing platform to achieve MPI parallelization during model training and inference. The results indicate that different types of neural network architectures have a significant impact on inference performance and accuracy. When using the deep spatio-temporal networks model, the mean absolute error is 0.0032. At an atomic scale of 3M, the parallel efficiency with 1024 computational nodes exceeds 90%, demonstrating excellent parallel performance. Acceleration ; Computer science ; Dynamics (music) ; Artificial intelligence ; Computational science ; Physics Machine Learning in Materials Science
Authors: Rui Bai | Zhiwei Chen | Kaicheng Yang
Source: Transactions on Environment Energy and Earth Sciences (article, 2024-12-12)
Field: Materials Science — Ceramics and Composites

Machine Learning-Based Flexural Strength Prediction of Layered Ceramic Materials The flexural strength of layered ceramic materials plays an important role in its application, and are affected by many structural parameters and process factors. At present, the experimental methods of layered ceramic materials are inefficient, and it is impossible to systematically analyze the effect of structural parameters and process factors on the bending strength of silicon nitride ceramics. In this paper, a machine learning model based on 3 classical algorithms is successfully established, and its bending strength is predicted with 5 indexes. According to the SHAP diagram, the material of layered ceramics has the greatest influence on the bending strength, followed by the sintering temperature, this article provides the basis and reference for the preparation of layered ceramic materials with high bending strength. Flexural strength ; Materials science ; Ceramic ; Bending ; Composite material ; Sintering ; Silicon nitride ; Layer (electronics) Advanced ceramic materials synthesis ; Advanced materials and composites ; MXene and MAX Phase Materials
Authors: Yuncheng Du
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Bayesian Optimization Enhanced Neural Networks for Predicting Metal-Organic Framework Morphology: A Zif-8 Synthesis Case Study Metal-organic framework ; Morphology (biology) ; Bayesian optimization ; Artificial neural network ; Bayesian network ; Bayesian probability ; Computer science ; Artificial intelligence ; Biological system ; Materials science ; Chemistry ; Geology ; Organic chemistry ; Biology ; Paleontology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Zhihao Xing | Rodolfo S. M. Freitas | Xi Jiang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Neural Network Potential-Based Molecular Investigation of Thermal Decomposition Mechanisms of Ethylene and Ammonia Ammonia ; Ethylene ; Thermal decomposition ; Decomposition ; Thermal ; Chemistry ; Biochemical engineering ; Materials science ; Chemical engineering ; Process engineering ; Organic chemistry ; Engineering ; Thermodynamics ; Physics Chemical Thermodynamics and Molecular Structure ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Muhammad Zeeshan Farooq
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Biomaterials

Combustion Phases of Magnesium Alloys Based on Predicted Heating Rate Using Machine Learning Magnesium ; Combustion ; Materials science ; Metallurgy ; Thermodynamics ; Chemistry ; Physics ; Physical chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; High Temperature Alloys and Creep
Authors: Jonathan S. Van Buskirk | Gordon G. C. Peterson | Daniel C. Fredrickson
Source: ChemRxiv (preprint, 2024-06-27)
Field: Materials Science — Materials Chemistry

Machine-Learning-based Investigation of Atomic Packing Effects: Chemical Pressures at the Extremes of Intermetallic Complexity Intermetallic phases represent a domain of emergent behavior, in which atoms can combine into complex geometrical arrangements with repeat patterns containing thousands of atoms or even long-range order incompatible with a 3D unit cell. The formation of such arrangements points to unexplained driving forces that, if understood, could be harnessed in materials design. DFT-Chemical Pressure (CP) analysis has emerged as an approach to visualizing how atomic packing tensions can drive complexity and create potential functionality. However, its applications have hitherto been limited in scope by the dependence on electronic structure calculations. In this Article, we develop a Machine Learning (ML)-based implementation of the CP approach, drawing on DFT-CP schemes collected in the Intermetallic Reactivity Database. To illustrate its capabilities, we explore one of the quintessential instances of intermetallic complexity, Mg2Al3. The structure’s ML-derived CP scheme points to an origin in simple matching rules for the assembly of Frank-Kasper polyhedra. Intermetallic ; Materials science ; Metallurgy ; Computer science Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Advanced materials and composites
Authors: Joseph Razzell Hollis | Jennifer L. Lavers | Alexander L. Bond
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

The Use of Vibrational Spectroscopy and Supervised Machine Learning for Chemical Identification of Ingested Plastic Pollution Identification (biology) ; Spectroscopy ; Plastic pollution ; Pollution ; Environmental science ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Environmental chemistry ; Microplastics ; Physics ; Biology ; Botany ; Ecology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Anjana S. Desai | Aparna Ashok | Shivali Amit Wagle | Neeru Bhagat | Zhadyra Ashirova | Zhannat Abdrasulova | Nurshat Abdolla | Zhazira Mukazhanova | Alibek Ydyrys | Ainur Seilkhan
Source: Research Square (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

Wound Healing of Boiologically and Chemically Syntehsized Gold Nanoparticles through Scratch Assay and Neural Network Modeling Cytotoxicity ; Wound healing ; Aloe vera ; Colloidal gold ; Nanoparticle ; MTT assay ; Scratch ; Wound care ; Nanotechnology ; Materials science ; Chemistry ; In vitro ; Traditional medicine ; Medicine ; Biochemistry ; Surgery Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection ; Wound Healing and Treatments
Authors: Kai Yuan | Shuai Zhou | Ning Li | Tianyan Li | Bowen Ding | Danhuai Guo | Yingjin Ma
Source: arXiv (Cornell University) (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

Fault-tolerant Quantum Chemical Calculations with Improved Machine-Learning Models Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [Ref: J. Comput. Chem. 2023, 44, 1174], we further propose 1) the improve ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; 2) the idea of coded computation, i.e. the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and 3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, and owing primarily profit in fault tolerance that aiming at the automated quantum chemical calculations for both ground and excited states. Computer science ; Computation ; Excited state ; Benchmark (surveying) ; Quantum ; Density functional theory ; Quantum chemical ; Coding (social sciences) ; Fault tolerance ; Time-dependent density functional theory ; Algorithm ; Mathematical optimization ; Distributed computing ; Chemistry ; Mathematics ; Quantum mechanics ; Computational chemistry ; Physics ; Molecule Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Yilin Zheng | Zhigong Song | Zhuo Li
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Generative Machine Learning Model for the 3d Reconstruction of Material Microstructure and Performance Evaluation Generative grammar ; Microstructure ; Computer science ; Generative model ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced X-ray and CT Imaging
Authors: Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Some Approaches for Theoretical Search of Novel Phosphor Materials Based on First-Principles Calculations and Machine Learning For theoretical search of novel rare earth (RE)-doped or transition metal (TM)-doped phosphor materials, it is important to optimize the combination of luminescent ions and host crystals. For this purpose, information on the structure-property relationships such as the structure-energy relationship is indispensable. Accordingly, prediction of the absorption and emission energies for hypothetical materials is essentially important. In our group, we conduct research to obtain such information by three approaches. (1) Analysis of detailed electronic structures based on first-principles calculations, (2) Prediction of emission and absorption energies based on first-principles calculations and machine learning, (3) Creation of structure-property maps based on systematic first-principles calculations. In this talk, these approaches will be explained in detail and some of our results will be also presented. Phosphor ; Computer science ; Artificial intelligence ; Materials science ; Optoelectronics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Martin Nováček | Jan Řezáč
Source: ChemRxiv (preprint, 2024-08-09)
Field: Materials Science — Materials Chemistry

PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a ∆-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a ∆-ML method that synergizes the semiempir- ical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML poten- tial applied as a universal correction. The method demonstrates superior perfor- mance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML’s accuracy and robustness. Its practical application is facili- tated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml. Transferability ; Robustness (evolution) ; Benchmarking ; Scalability ; Computer science ; Quantum chemistry ; Quantum chemical ; Quantum ; Computational science ; Algorithm ; Machine learning ; Chemistry ; Artificial intelligence ; Computational chemistry ; Molecule ; Quantum mechanics ; Physics ; Organic chemistry ; Database Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics