jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. Chandran | K. Chockalingam | S. Umar Sherif | K.J. Nagarajan
Source: Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (article, 2024-12-09)
Field: Materials Science — Biomaterials

Predicting and optimizing the wear behavior of centrifugal cast β-TCP/Mg–2Zn–1Mn alloys: A hybrid artificial neural network—Grey relational analysis approach This study investigates the wear behavior of temporary implant such as plates and screws made by Mg–2Zn–1Mn alloy reinforced with β-tricalcium phosphate (β-TCP) under both dry and wet sliding conditions. Grey relational analysis (GRA) and an artificial neural network (ANN) model were utilized to predict wear and optimize performance, considering process parameters such as β-TCP content (0%, 2.5%, 5%), load (20, 40, 60 N), sliding speeds (2, 4, 6 m/s), and sliding distances (800, 1600, 2400 m). The wear rate ( W s ) and coefficient of friction (COF) were evaluated using an orthogonal array L27 to optimize both dry and wet conditions. The optimal process parameters obtained through GRA for dry conditions are 5% β-TCP, 20 N, 4 m/s, and 1600 m, while for wet conditions, they are 0% β-TCP, 20 N, 2 m/s, and 2400 m. The experiment was conducted to calculate W s and COF using the above process parameters. The values of W s and COF are 0.010149 mm³/m and 0.109 for dry conditions, and 0.001143 mm³/m and 0.128 for wet conditions. The ANN predicted values for dry conditions are 0.010471 mm³/m for W s and 0.11045 for COF, with error percentages of 3.17% and 1.33%, respectively. Similarly, for wet conditions, the predicted values are 0.001107 mm³/m for W s and 0.125 for COF, with error percentages of 3.13% and 2.77%. The ANOVA results for the Grey relational grade (GRG) show that load was the most significant factor influencing wear performance, contributing 51.91% in dry conditions and 34.69% in wet conditions. The close alignment of predictions from both models, with overall error percentages below 3.17%, demonstrates their reliability and effectiveness. Field emission scanning electron microscope (FESEM) analysis of worn surfaces under optimal conditions provided insights into the wear mechanisms. This study confirms that GRA and ANN models are highly effective for predicting and optimize the wear behavior. Grey relational analysis ; Artificial neural network ; Centrifugal pump ; Materials science ; Computer science ; Metallurgy ; Artificial intelligence ; Mechanical engineering ; Engineering ; Mathematics ; Statistics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Anton Alekseev | Anton Ber | Artur Kadurin | Kuzma Khrabrov | Sergey Nikolenko | D. Yu. Protasov | Egor Rumiantsev | Ilya Shenbin | Mikhail Shirokikh | Alexander Telepov | Artem Tsypin | Elena Tutubalina | Konstantin Ushenin
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials Artificial neural network ; Benchmark (surveying) ; Quantum chemistry ; Quantum ; Molecule ; Quantum chemical Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Computational Drug Discovery Methods
Authors: Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2024-05-15)
Field: Chemistry — Physical and Theoretical Chemistry

Response Matching for generating materials and molecules Machine learning has recently emerged as a powerful tool for generating new molecular and material structures. The success of state-of-the-art models stems from their ability to incorporate physical symmetries, such as translation, rotation, and periodicity. Here, we present a novel generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Consequently, any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching to such response is closely related to score matching in diffusion models. By employing the combination of a machine learning interatomic potential and random structure search as the denoising model, RM exploits the locality of atomic interactions, and inherently respects permutation, translation, rotation, and periodic invariances. RM is the first model to handle both molecules and bulk materials under the same framework. We demonstrate the efficiency and generalization of RM across three systems: a small organic molecular dataset, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration. Matching (statistics) ; Computer science ; Mathematics ; Statistics Various Chemistry Research Topics ; Conducting polymers and applications
Authors: Oleh Yasniy | Volodymyr Iasnii | Oleh Pastukh | Iryna Didych | Sergiy Fedak | Sofia Fedak | Lubov Tsymbaliuk
Source: Procedia Structural Integrity (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Estimation of shape memory alloys functional properties by methods of artificial intelligence The functional properties of the pseudoelastic alloy were predicted by machine learning methods, namely, the dependence of the dissipated energy and the strain range of NiTi alloy on the number of loading cycles. This study used a multilayered neural network perceptron (MLP) architecture and a random forest method. In particular, the correlation coefficient was 98% when modelling the dependence of the dissipated energy and the strain range of NiTi alloy on the number of loading cycles by the neural network method. The results obtained are in agreement with the experimental data. The random forests method was found to give the lowest prediction error of 3.9% and 7% in the test set of W d - N and Δ ε - N dependences, respectively. In comparison, the error of the neural network method was 5.5% and 7.5%, respectively. Estimation ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Statistics ; Mathematics ; Engineering Shape Memory Alloy Transformations
Authors: Jichen Li | Lisanne Knijff | Zhan‐Yun Zhang | Linnéa Andersson | Chao Zhang
Source: ChemRxiv (preprint, 2024-11-20)
Field: Materials Science — Materials Chemistry

PiNN: equivariant neural network suite for modelling electrochemical systems Electrochemical energy storage and conversion play an increasingly important role in electrification and sustainable development across the globe. A key challenge therein is to understand, control, and design electrochemical energy materials at atomistic precision. This requires inputs from molecular modelling powered by machine learning (ML) techniques. In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along with PiNet2-dipole for dipole and charge predictions as well as PiNet2-chi for generating atom-condensed charge response kernels. By benchmarking publicly accessible datasets of small molecules, crystalline materials, and liquid electrolytes, we found that the equivariant PiNet2 shows significant improvements over the original PiNet architecture and provides a state-of-the-art overall performance. Furthermore, leveraging on plug-ins such as PiNNAcLe for an adaptive learn-on-the-fly workflow in generating ML potentials and PiNNwall for modelling heterogeneous electrodes under external bias, we expect PiNN to serve as a versatile and high-performing ML-accelerated platform for molecular modelling of electrochemical systems. Python (programming language) ; Computer science ; Materials science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Rosela Yesenia Guadalupe Silva-Molina | Marco A. Jimenez-Valencia | Alejandro Castellanos-Jaramillo | Francisco-Adrián Duarte-Alcaráz | Juan Miguel Castellanos-Jaramillo | Arnulfo Castellanos-Moreno
Source: Physica Scripta (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Solution to the schrödinger equation for bound states of polar molecules using shallow neural networks Abstract The time independent Schrödinger equation can be solved analytically in very few cases. This is why scientists rely on approximate numerical methods. One that is yet to be a part of the everyday toolbox for physicists are neural networks. With it, it is possible to calculate eigenfunctions and the energy spectra of bound states. A double exponential central potential is used to describe the interaction between two electric dipoles that form through van der Waals interactions. These systems can be important when the electric dipoles are very large and differ from covalent, ionic, and other kinds of bonds, as is the case of the monomers <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>M</mml:mi> <mml:mo>−</mml:mo> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>60</mml:mn> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> </mml:math> where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>M</mml:mi> <mml:mo>=</mml:mo> <mml:mrow> <mml:mfenced close="}" open="{"> <mml:mrow> <mml:mi mathvariant="italic">Cs</mml:mi> <mml:mo>,</mml:mo> <mml:mi mathvariant="italic">Li</mml:mi> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:math> or other alkali metals. Based in statistical physics, it has been shown that a dimer can be formed through a spontaneous and exothermic reaction. The ground state and the first two excited states of this system were calculated, also the solutions and their associated energies were plotted. The result for the ground state is compared to the shooting method and the direct variational method. The numerical value of the ground states obtained suggests that the dimers can be dissociated by electromagnetic waves with wavelengths in the far infrared regime, very close to microwaves. Algorithm ; Physics ; Computer science Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Gaoning Shi | Yaowei Wang | Kun Yang | Yuan Qiu | Hong Zhu | Xiaoqin Zeng
Source: Journal of Magnesium and Alloys (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

A surface emphasized multi-task learning framework for surface property predictions: A case study of magnesium intermetallics • A multi-task learning framework for predicting surface properties from crystal structure graphs is presented. • The model is trained on 3526 surface structures of binary Mg intermetallics labeled with surface energies and work functions. • The ability to leverage different target features are demonstrated when trained from scratch or fine-tuned. • The facet-dependent work functions and corresponding Wulff shapes are predicted for 246 binary Mg intermetallics. Surface properties of crystals are critical in many fields, including electrochemistry and photoelectronics, the efficient prediction of which can expedite the design and optimization of catalysts, batteries, alloys etc. However, we are still far from realizing this vision due to the rarity of surface property-related databases, especially for multicomponent compounds, due to the large sample spaces and limited computing resources. In this work, we present a surface emphasized multi-task crystal graph convolutional neural network (SEM-CGCNN) to predict multiple surface properties simultaneously from crystal structures. The model is evaluated on a dataset of 3526 surface energies and work functions of binary magnesium intermetallics obtained through first-principles calculations, and obvious improvements are observed both in efficiency and accuracy over the original CGCNN model. By transferring the pre-trained model to the datasets of pure metals and other intermetallics, the fine-tuned SEM-CGCNN outperforms learning from scratch and can be further applied to other surface properties and materials systems. This study could be a paradigm for the end-to-end mapping of atomic structures to anisotropic surface properties of crystals, which provides an efficient framework to understand and screen materials with desired surface characteristics. Intermetallic ; Materials science ; Magnesium ; Surface (topology) ; Property (philosophy) ; Task (project management) ; Metallurgy ; Systems engineering ; Geometry ; Engineering ; Alloy MXene and MAX Phase Materials ; Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials
Authors: Thomas A. Teklemariam | Faith Chou | P. Kumaravel | Jeremy Van Buskrik
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Atr-Ftir Spectroscopy and Machine/Deep Learning Models for Detecting Adulteration in Coconut Water with Sugars, Sugar Alcohols, and Artificial Sweeteners Sugar ; Artificial Sweetener ; Fourier transform infrared spectroscopy ; Chemistry ; Food science ; Chemical engineering ; Engineering Coconut Research and Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Rui Wang | Hongyu Yu | Yang Zhong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2024-09-05)
Field: Materials Science — Materials Chemistry

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks The application of machine learning methodologies for predicting properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional Multi-Layer Perceptrons (MLPs). This study evaluates the impact of substituting MLPs with KANs within three established machine learning frameworks: Allegro, Neural Equivariant Interatomic Potentials (NequIP), and the Edge-Based Tensor Prediction Graph Neural Network (ETGNN). Our results demonstrate that the integration of KANs generally yields enhanced prediction accuracies. Specifically, replacing MLPs with KANs in the output blocks leads to notable improvements in accuracy and, in certain scenarios, also results in reduced training times. Furthermore, employing KANs exclusively in the output block facilitates faster inference and improved computational efficiency relative to utilizing KANs throughout the entire model. The selection of an optimal basis function for KANs is found to be contingent upon the particular problem at hand. Our results demonstrate the strong potential of KANs in enhancing machine learning potentials and material property predictions. Energy (signal processing) ; Surface (topology) ; Statistical physics ; Computer science ; Econometrics ; Mathematics ; Physics ; Statistics ; Geometry Machine Learning in Materials Science ; Statistical Mechanics and Entropy ; Neural Networks and Applications
Authors: Dario Baratella | Omar Abou El Kheir | Marco Bernasconi
Source: arXiv (Cornell University) (preprint, 2024-04-23)
Field: Materials Science — Materials Chemistry

Unveiling the crystallization kinetics in Ge-rich Ge$_x$Te alloys by large scale simulations with a machine-learned interatomic potential A machine-learned interatomic potential for Ge-rich Ge$_x$Te alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase change memories which exploits Ge-enrichment of GeSbTe alloys to raise the crystallization temperature. The potential is generated by fitting a large database of energies and forces computed within Density Functional Theory with the neural network scheme implemented in the DeePMD-kit package. The potential is highly accurate and suitable to describe the structural and dynamical properties of the liquid, amorphous and crystalline phases of the wide range of compositions from pure Ge and stoichiometric GeTe to the Ge-rich Ge$_2$Te alloy. Large scale molecular dynamics simulations revealed a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess occurs on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe occurs before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess. Germanium ; Crystallization ; Germanium compounds ; Scale (ratio) ; Kinetics ; Interatomic potential ; Materials science ; Atomic units ; Crystallography ; Metallurgy ; Thermodynamics ; Physics ; Chemistry ; Molecular dynamics ; Silicon ; Computational chemistry ; Classical mechanics Machine Learning in Materials Science ; Phase-change materials and chalcogenides
Authors: Frederik Ørsted Kjeldal | Janus J. Eriksen
Source: Machine Learning Science and Technology (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Transferability of atom-based neural networks Abstract Machine-learning models in chemistry—when based on descriptors of atoms embedded within molecules—face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across chemical compound space. In the present work, we make use of adversarial validation to elucidate certain intrinsic complications related to machine inferences of unseen chemistry. On this basis, we employ invariant and equivariant neural networks—both trained either exclusively on total molecular energies or a combination of these and data from atomic partitioning schemes—to evaluate how such models scale performance-wise between datasets of fundamentally different functionality and composition. We find the inference of local electronic properties to improve significantly when training models on augmented data that appropriately expose local functional features. However, molecular datasets for training purposes must themselves be sufficiently comprehensive and rich in composition to warrant any generalizations to larger systems, and even then, transferability can still only genuinely manifest if the body of atomic energies available for training purposes exposes the uniqueness of different functional moieties within molecules. We demonstrate this point by comparing machine models trained on atomic partitioning schemes based on the spatial locality of either native atomic or molecular orbitals. Transferability ; Atom (system on chip) ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Wenqi Guo | Yiyang Du | Mohamed Shehata
Source:  (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning Physical molecular models are widely used in educational settings for teaching organic and other branches of chemistry, offering an intuitive understanding of molecular structures. Conversely, while less intuitive, virtual models provide additional functionalities, such as retrieving molecular names and other properties. Currently, to the best of our knowledge, there is a gap between 3D molecular models and their digital counterparts. This paper introduces a computer vision model designed to bridge this gap by converting images of physical molecular models into their digital DeepSMILES representations. This conversion facilitates further information retrieval, enhancing educational utility. We developed synthetic and real datasets to train our model and evaluated its performance across various dataset combinations. Additionally, we attempted to improve the model’s accuracy by multi-image input and beam search. We achieved 62.0% top1 accuracy and 80.3% top-3 accuracy with beam search and multi-image input on our validation set. We also explored the model’s characteristics, such as explainability by saliency maps, and examined its calibration. We also discussed the model’s limitations and directions for future research.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>The code for this project and the two datasets are available on https://github.com/weathon/3d_mol_pub. Computer science ; Notation ; Deep learning ; Artificial intelligence ; Natural language processing ; Mathematics ; Arithmetic Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Wenqi Guo | Yiyang Du | Mohamed Shehata
Source: ChemRxiv (preprint, 2024-11-19)
Field: Materials Science — Materials Chemistry

3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning Physical molecular models are widely used in educational settings for teaching organic and other branches of chemistry, offering an intuitive understanding of molecular structures. Conversely, while less intuitive, virtual models provide additional functionalities, such as retrieving molecular names and other properties. Currently, to the best of our knowledge, there is a gap between 3D molecular models and their digital counterparts. This paper introduces a computer vision model designed to bridge this gap by converting images of physical molecular models into their digital DeepSMILES representations. This conversion facilitates further information retrieval, enhancing educational utility. We developed synthetic and real datasets to train our model and evaluated its performance across various dataset combinations. Additionally, we attempted to improve the model’s accuracy by multi-image input and beam search. We achieved 62.0% top1 accuracy and 80.3% top-3 accuracy with beam search and multi-image input on our validation set. We also explored the model’s characteristics, such as explainability by saliency maps, and examined its calibration. We also discussed the model’s limitations and directions for future research. Notation ; Computer science ; Natural language processing ; Artificial intelligence ; Deep learning ; Mathematics ; Arithmetic Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques