jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Revanth Mattey | Susanta Ghosh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks Balanced flow ; Artificial neural network ; Separable space ; Field (mathematics) ; Phase (matter) ; Computer science ; Flow (mathematics) ; Mechanics ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis ; Pure mathematics Solidification and crystal growth phenomena ; Model Reduction and Neural Networks ; Fluid Dynamics and Turbulent Flows
Authors: Igor V. Polyakov | Alexandra V. Krivitskaya | Maria G. Khrenova
Source: Moscow University Chemistry Bulletin (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

The Structure and Dynamics of the Enzyme–Substrate Complex of N-Acetylaspartylglutamate Synthase According to the Computer Simulation Data N-Acetylaspartylglutamate (NAAG) is the most common dipeptide in brain cells, which is synthesized using the enzyme N-acetylaspartylglutamate synthase. In this study, we utilize bioinformatics methods to predict the protein structure based on the primary sequence of the coding gene, classical molecular dynamics to obtain a stable protein complex with N-acetylaspartate and glutamate ligands within the trajectory, and machine learning methods to analyze, describe, and select potential reactive and nonreactive conformations of the model system describing the enzyme–substrate complex. Molecular dynamics trajectories are obtained for a set of selected conformations within the framework of the method of combined quantum and classical molecular mechanics, and the active site of the protein–ligand complex and potential reaction mechanism are characterized. Chemistry ; Substrate (aquarium) ; Enzyme ; Molecular dynamics ; Dynamics (music) ; ATP synthase ; Biochemistry ; Computational chemistry ; Physics ; Ecology Enzyme Structure and Function ; Protein Structure and Dynamics ; Biochemical and Molecular Research
Authors: Marcin Kirsz | Ciprian G. Pruteanu | Peter I. C. Cooke | Graeme J. Ackland
Source: arXiv (Cornell University) (preprint, 2024-05-08)
Field: Materials Science — Materials Chemistry

Understanding solid nitrogen through machine learning simulation We construct a fast, transferable, general purpose, machine-learning interatomic potential suitable for large-scale simulations of $N_2$. The potential is trained only on high quality quantum chemical molecule-molecule interactions, no condensed phase information is used. The potential reproduces the experimental phase diagram including the melt curve and the molecular solid phases of nitrogen up to 10 GPa. This demonstrates that many-molecule interactions are unnecessary to explain the condensed phases of $N_2$. With increased pressure, transitions are observed from cubic ($α-N_2$), which optimises quadrupole-quadrupole interactions, through tetragonal ($γ-N_2$) which allows more efficient packing, through to monoclinic ($λ-N_2$) which packs still more efficiently. On heating, we obtain the hcp 3D rotor phase ($β-N_2$) and, at pressure, the cubic $δ-N_2$ phase which contains both 3D and 2D rotors, tetragonal $δ^\star-N_2$ phase with 2D rotors and the rhombohedral $ε-N_2$. Molecular dynamics demonstrates where these phases are indeed rotors, rather than frustrated order. The model does not support the existence of the wide range of bondlengths reported for the complex $ι-N_2$ phase. The thermodynamic transitions involve both shifts of molecular centres and rotations of molecules. We simulate these phase transitions between finding that the onset of rotation is rapid whereas motion of molecular centres is inhibited and the cause of the observed sluggishness of transitions. Routine density functional theory calculations give a similar picture to the potential. Computer science ; Nitrogen ; Artificial intelligence ; Machine learning ; Environmental science ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Wantae Kim | Weon‐Gyu Lee | Hong-Eun An | Hiroyasu Furukawa | WooSeok Jeong | Sung‐Chul Kim | Jeffrey R. Long | Sohee Jeong | Jung‐Hoon Lee
Source: ChemRxiv (preprint, 2024-10-24)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Assisted Design of Metal–Organic Frameworks for Hydrogen Storage: A High-Throughput Screening and Experimental Approach Various theoretical approaches, including big data and high-throughput screening techniques, have been explored in developing new materials due to their significant potential time-saving advantages. However, it remains a significant challenge to experimentally realize new materials that are predicted. In this study, we propose a novel materials design strategy that utilizes machine-learning (ML) techniques to predict new porous materials that show promise for hydrogen storage and are likely to be feasible to synthesize. By leveraging ML techniques and metal−organic framework (MOF) databases, we are able to predict the synthesizability of MOF structures. This is evidenced by the successful synthesis of a new vanadium-based MOF that exhibits excellent performance for cryogenic H2 storage. Notably, the total gravimetric and volumetric H2 uptakes are as high as 9.0 wt % and 50.0 g/L at 77 K and 150 bar. This ML-assisted materials design offers an efficient and promising approach for developing hydrogen storage materials. Throughput ; High-throughput screening ; Computer science ; Hydrogen storage ; Process engineering ; Artificial intelligence ; Nanotechnology ; Biochemical engineering ; Computer architecture ; Hydrogen ; Materials science ; Chemistry ; Engineering ; Telecommunications ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Tomoyuki Tsuyama | Takeshi Kaneshita | Akira Matsui | Kazuhiko Ochiai | Hiroaki Tanaka | Rie Kondo | T. Fukushima | Hirotada Ohashi | Atsushi Hashimoto | Yoshishige Okuno | Jian‐Gang Zhu
Source: arXiv (Cornell University) (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

Driving force of atomic ordering in Fe$_{1-x}$Pt$_{x}$, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations We report the mechanisms of atomic ordering in Fe$_{1-x}$Pt$_{x}$ alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the density of states indicated that coherence in local potentials in the ordered phase brings energy gain over the disordered phases, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at a finite temperature. The model trained using the DFT dataset with spin polarization exhibited quantitatively good agreement with previous experiments and thermodynamic calculations across a wide range of Pt compositions, whereas the model without spin significantly underestimated the transition temperature. Through this study, we clarified that spin polarization is essential for accurately accounting for the ordered phase in Fe-Pt bimetallic alloys, even above the Curie temperature, possibly because of the remaining short-range spin order. Density functional theory ; Monte Carlo method ; Statistical physics ; Interatomic potential ; Molecular dynamics ; Physics ; Materials science ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Narendra Bandaru | Kalyani Vallabhudasu | Ch. Venkata Reddy | K. Uday Kumar
Source:  (review, 2024-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrating Machine Learning Techniques in Supercapacitor Research: A Comprehensive Review This study examines supercapacitors using machine learning. Each model is tested using Random Forest, Support Vector Machine, Regression, and ANN. This article examines data selection and collection procedures to assure accuracy. It also shows how doping materials, surface area, and hole size affect supercapacitor efficiency. Case examples show how machine learning may improve material quality, predict capacitance, and boost supercapacitor efficiency. Well-known examples are used to investigate machine learning methods for capacitance prediction. The graph shows the values of the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for several models. Research shows that ANN-based models are more correct. This piece talks about the problems that come up when you try to use machine learning to make supercapacitors work better. Instead of focusing on controlled lab conditions, think about how accurate and clear the model is, how simple and reliable the data is, and how the results apply to real-life situations. This piece talks about the newest advances in supercapacitor machine learning. It's necessary to use complex math, visual observation, self-detection of materials, and quantum computers. The study shows that machine learning could make it faster to make supercapacitors. In a busy workplace, it's important to set goals and work together. Computer science ; Supercapacitor ; Artificial intelligence ; Machine learning ; Capacitance Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Hongyu Yan | Qi Dai | Yong Wei | Ming‐Han Chen | Hanning Chen
Source: arXiv (Cornell University) (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

PDMD: Potential-free Data-driven Molecular Dynamics for Variable-sized Water Clusters Conventional molecular dynamics (MD) simulation approaches, such as ab initio MD and empirical force field MD, face significant trade-offs between physical accuracy and computational efficiency. This work presents a novel Potential-free Data-driven Molecular Dynamics (PDMD) framework for predicting system energy and atomic forces of variable-sized water clusters. Specifically, PDMD employs the smooth overlap of atomic positions descriptor to generate high-dimensional, equivariant features before leveraging ChemGNN, a graph neural network model that adaptively learns the atomic chemical environments without requiring a priori knowledge. Through an iterative self-consistent training approach, the converged PDMD achieves a mean absolute error of 7.1 meV/atom for energy and 59.8 meV/angstrom for forces, outperforming the state-of-the-art DeepMD by ~80% in energy accuracy and ~200% in force prediction. As a result, PDMD can reproduce the ab initio MD properties of water clusters at a tiny fraction of its computational cost. These results demonstrate that the proposed PDMD offers multiple-phase predictive power, enabling ultra-fast, general-purpose MD simulations while retaining ab initio accuracy. Dynamics (music) ; Variable (mathematics) ; Molecular dynamics ; Statistical physics ; Computer science ; Physics ; Chemistry ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Jike Wang | Hao Luo | Rui Qin | Mingyang Wang | Meijing Fang | Odin Zhang | Qiaolin Gou | Qun Su | Chao Shen | Ziyi You | Xiaozhe Wan | Liwei Liu | Chang‐Yu Hsieh | Tingjun Hou | Yu Kang
Source: ChemRxiv (preprint, 2024-07-09)
Field: Materials Science — Materials Chemistry

3D Molecular Pocket-based Generation with Token-only Large Language Model Designing innovative molecular structures tailored to specific protein targets represents a fundamental challenge in drug discovery. Most existing approaches based on graph neural networks for generating three-dimensional (3D) molecules within protein pockets often produce molecules with invalid configurations, suboptimal drug-like qualities and limited synthesizability, while also requiring extended generation times. To address these challenges, we present 3DSMILES-GPT, a fully language-model-driven framework for 3D molecular generation. Initially, leveraging the architecture of large language models, we treat both two-dimensional (2D) and 3D molecular representations as linguistic expressions and pre-train the model on an extensive dataset. This approach enables the model to comprehensively understand the 2D and 3D characteristics of large-scale molecules. Subsequently, we fine-tune the model on protein pocket and molecule pair data, followed by reinforcement learning to further optimize the biophysical and chemical properties of the generated molecules. Experimental results demonstrate that, compared to existing methods, 3DSMILES-GPT generates molecules with superior metrics, such as Vina docking score. Notably, it achieves a 33% enhancement in the quantitative estimation of drug-likeness (QED) compared to current models. Additionally, the generation speed is significantly improved, with a threefold increase over the fastest existing methods. This innovative approach highlights the potential of 3DSMILES-GPT to revolutionize the generation of drug-like molecules, offering both improved efficacy and efficiency in drug discovery process. Computer science ; Drug discovery ; Security token ; Process (computing) ; Artificial intelligence ; Graph ; Reinforcement learning ; Language model ; Machine learning ; Biological system ; Theoretical computer science ; Bioinformatics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Safa El Gharbi | Kamel Landolsi | Fraj Echouchene
Source:  (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Optimizing Hydrogen Production from NaBH4 Hydrolysis Using BBD, ANN, and Genetic Algorithm This study investigates the optimization of hydrogen generation rate (HGR) by NaBH4 hydrolysis using a comprehensive approach integrating experimental design, response surface methodology (RSM), artificial neural networks (ANN), and genetic algorithms (GA). Catalyst synthesis and Box-Behnken design were used to investigate the effects of temperature, stirring rate, NaBH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> and catalyst masses, and NaOH concentration on HGR. The RSM-built quadratic regression model highlighted significant effects of temperature, NaBH4 mass, and catalyst mass on HGR. ANN modeling demonstrated strong predictive performance, explaining 98% of the variance with a mean absolute percentage error (MAPE) of 0.065, root mean square error (RMSE) of 304.06, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.98. GA optimization indicated that the maximum HGR of 6912 mL/min.gcata can be achieved at 55°C, 150 mg NaBH4, 10 mg catalyst, and 0.7 mol/L NaOH concentration. In addition, the activation energy for NaBH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> hydrolysis was determined to be 49.72 kJ/mol, which is in agreement with literature values. Computer science ; Genetic algorithm ; Production (economics) ; Hydrogen production ; Algorithm ; Hydrogen ; Chemistry ; Machine learning ; Organic chemistry Hydrogen Storage and Materials
Authors: Ajeet Kumar Yadav | Meher K. Prakash | Pradipta Bandyopadhyay
Source: ChemRxiv (preprint, 2024-10-25)
Field: Materials Science — Materials Chemistry

Physics-based Machine Learning to Predict Hydration Free Energies for Small Molecules with a minimal number of descriptors: Interpretable and Accurate Hydration free energy (HFE) of molecules is a fundamental property having impor- tance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Ma- chine learning (ML) models are increasingly being used to predict HFE. Although the accuracy of ML models for datasets for small molecules is impressive, these models suffer from lack of interpretability. In this work, we have developed a physics-based ML model with only six descriptors, which is both accurate and fully interpretable, and applied it to a database for small molecule HFE, FreeSolv. We have evaluated the electrostatic energy by an approximate closed form of the Generalized Born (GB) model and polar surface area. In addition, we have logP and hydrogen bond acceptor and donors as descriptors along with the number of rotatable bonds. We have used different ML models such as random forest and extreme gradient boosting. The best result from these models has a mean absolute error of only 0.74 kcal/mol. The main power of this model is that the descriptors have clear physical meaning and it was found that the descriptor describing the electrostatics and the polar surface area, followed by the hydrogen bond donors and acceptors, are the most important factors for the calculation of hydration free energy. Artificial intelligence ; Machine learning ; Physics ; Statistical physics ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Angéline Lafleur | Soo-hyon Phark
Source: arXiv (Cornell University) (preprint, 2024-10-17)
Field: Materials Science — Materials Chemistry

Automated classification of individual atoms on surfaces using machine learning Leveraging scanning tunneling microscopy (STM) for atomic-scale fabrication has led to many advancements such as the creation of atomic electron-spin qubit structures on surfaces. However, the time-consuming and tedious nature of this process calls for improvements, and this study explores the use of machine learning (ML) to automate certain steps, notably identifying appropriate atomic candidates for the structures. We classify titanium and iron atoms on a magnesium oxide (MgO) surface, which are prototypical on-surface spin qubit candidates, showing distinct topographic and spectroscopic features depending on the bonding sites of the MgO surface. Employing a semi-automated computer vision process, we train a convolutional neural network with STM topographic images and scanning tunneling spectroscopy (STS) curves of several hundred atoms. After training, the topography model achieves an 86% validation accuracy in classifying 200 new images, and the STS model, a 100% accuracy for a sample size of 100 atoms. This method extends its applicability to various nanoscopic measurements, including atomically resolved imaging and local probing of electronic properties, offering a promising approach for classifying atoms and molecules on surfaces. Artificial intelligence ; Computer science ; Machine learning Machine Learning in Materials Science ; History and advancements in chemistry
Authors: Andrea Angeletti | Luca Leoni | Dario Massa | Luca Pasquini | Stefanos Papanikolaou | Cesare Franchini
Source: arXiv (Cornell University) (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Hydrogen Diffusion in Magnesium Using Machine Learning Potentials: a comparative study Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to address with standard ab initio techniques. This work addresses this challenge by employing accelerated machine learning (ML) molecular dynamics simulations through active learning. We conduct a comparative study of different ML-based interatomic potential schemes, including VASP, MACE, and CHGNet, utilizing various training strategies such as on-the-fly learning, pre-trained universal models, and fine-tuning.By considering different temperatures and concentration regimes, we obtain hydrogen diffusion coefficients and activation energy values which align remarkably well with experimental results, underlining the efficacy and accuracy of ML-assisted methodologies in the context of diffusive dynamics. Particularly, our procedure significantly reduces the computational effort associated with traditional transition state calculations or ad-hoc designed interatomic potentials. The results highlight the limitations of pre-trained universal solutions for defective materials and how they can be improved by fine-tuning. Specifically, fine-tuning the models on a database produced during on-the-fly training of VASP ML force-field allows the retrieving of DFT-level accuracy at a fraction of the computational cost. Magnesium ; Hydrogen ; Diffusion ; Materials science ; Chemical physics ; Psychology ; Artificial intelligence ; Computer science ; Chemistry ; Physics ; Thermodynamics ; Metallurgy ; Quantum mechanics Hydrogen Storage and Materials ; Aluminum Alloy Microstructure Properties
Authors: Atsushi Togo | Atsuto Seko
Source: arXiv (Cornell University) (preprint, 2024-01-31)
Field: Materials Science — Materials Chemistry

On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity The application of first-principles calculations for predicting lattice thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized phonon Boltzmann equation, has gained increasing popularity. In this calculation, the determination of force constants through first-principles calculations is critical for accurate LTC predictions. For material exploration, performing first-principles LTC calculations in a high-throughput manner is now expected, although it requires significant computational resources. To reduce computational demands, we integrated polynomial machine learning potentials on-the-fly during the first-principles LTC calculations. This paper presents a systematic approach to first-principles LTC calculations. We designed and optimized an efficient workflow that integrates multiple modular software packages. We applied this approach to calculate LTCs for 103 compounds of the wurtzite, zincblende, and rocksalt types to evaluate the performance of the polynomial machine learning potentials in LTC calculations. We demonstrate a significant reduction in the computational resources required for the LTC predictions. Lattice (music) ; Thermal conductivity ; On the fly ; Training (meteorology) ; Polynomial ; Condensed matter physics ; Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Physics ; Mathematics ; Thermodynamics ; Mathematical analysis ; Meteorology ; Operating system Thermal properties of materials ; Machine Learning in Materials Science
Authors: Jia Zhao | Zhitao Song | Guimin Lu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Electrochemistry

Unrevealing Electroreduction Mechanism and Thermodynamic Behavior on the Nacl-Kcl-Mgcl2-Lacl3 Melt: Electrochemical Technologies and Dpmd Simulations Electrochemistry ; Mechanism (biology) ; Thermodynamics ; Materials science ; Chemistry ; Chemical engineering ; Electrode ; Physical chemistry ; Physics ; Engineering Electrochemical Analysis and Applications ; Ionic liquids properties and applications ; Chemical and Physical Properties in Aqueous Solutions