jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joung Sik Suh | Byeong‐Chan Suh | Jun Ho Bae | Young Min Kim
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Biomaterials

Machine Learning-Based Design of Biodegradable Mg Alloys for Load-Bearing Implants Bearing (navigation) ; Load bearing ; Materials science ; Engineering ; Computer science ; Structural engineering ; Artificial intelligence Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Titanium Alloys Microstructure and Properties
Authors: Kaihua Zhang | Ying Jiang | Liang-Shun Zhang
Source: Chinese Journal of Polymer Science (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method Polymer ; Artificial neural network ; Computer science ; Artificial intelligence ; Field (mathematics) ; Set (abstract data type) ; Variety (cybernetics) ; Materials science ; Statistical physics ; Biological system ; Physics ; Mathematics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Junfei Zhang | Yueqi Li | Xinbo Zhou
Source: ChemRxiv (preprint, 2022-05-18)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction of the Computed Band Gaps of Double Perovskite Materials Conventional electronic structure methods based on density functional theory (DFT) suffer from not only high computational cost that scales cubically with the number of electrons, but also limited accuracy arising from the approximations of the exchange-correlation functional. In particular, the latter issue is particularly severe for insulators and semiconductors, whose band gaps are systematically underestimated by DFT. Surrogate methods based on machine learning has garnered much attention as a viable alternative to bypass these limitations, especially in prediction of solid-state band gaps. Here, we construct a random forest regression model for band gaps of double perovskite materials, using a dataset of 1306 band gaps computed with the GLLBSC (Gritsenko, van Leeuwen, van Lenthe, and Baerends solid correlation) functional. Among the 20 physical features employed, we find that the bulk modulus, superconductivity temperature, and cation electronegativity exhibit the highest importance scores, consistent with the physics of the underlying electronic structure. Using the top 10 features, a model accuracy of 85.6% with a root mean square error of 0.64 eV is obtained, comparable to a previous study employing kernel ridge regression. Our results attest to the potential of machine learning regressions for rapid screening of promising candidate functional materials. Electronegativity ; Density functional theory ; Machine learning ; Band gap ; Artificial intelligence ; Random forest ; Mean squared error ; Condensed matter physics ; Statistical physics ; Hybrid functional ; Materials science ; Computer science ; Mathematics ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Thermoelectric Materials and Devices
Authors: Sheng Gong | Tian Xie | Yang Shao‐Horn | Rafael Gómez‐Bombarelli | Jeffrey C. Grossman
Source: arXiv (Cornell University) (preprint, 2022-08-09)
Field: Materials Science — Materials Chemistry

Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity Historically, materials informatics has relied on human-designed descriptors of materials structures. In recent years, graph neural networks (GNNs) have been proposed for learning representations of crystal structures from data end-to-end producing vectorial embeddings that are optimized for downstream prediction tasks. However, a systematic scheme is lacking to analyze and understand the limits of GNNs for capturing crystal structures. In this work, we propose to use human-designed descriptors as a bank of human knowledge to test whether black-box GNNs can capture the knowledge of crystal structures. We find that current state-of-the-art GNNs cannot capture the periodicity of crystal structures well, and we analyze the limitations of the GNN models that result in this failure from three aspects: local expressive power, long-range information, and readout function. We propose an initial solution, hybridizing descriptors with GNNs, to improve the prediction of GNNs for materials properties, especially phonon internal energy and heat capacity with 90% lower errors, and we analyze the mechanisms for the improved prediction. All the analysis can be extended easily to other deep representation learning models, human-designed descriptors, and systems such as molecules and amorphous materials. Computer science ; Representation (politics) ; Graph ; Artificial intelligence ; Artificial neural network ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Raimondas Galvelis | Alejandro Varela‐Rial | Stefan H. Doerr | Roberto Fino | Peter Eastman | Thomas E. Markland | John D. Chodera | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2022-01-20)
Field: Materials Science — Materials Chemistry

NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines neural network potentials (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by 5 times and achieve a combined sampling of one microsecond for each complex, marking the longest simulations ever reported for this class of simulation. Metadynamics ; Molecular dynamics ; Microsecond ; Computer science ; Molecular mechanics ; Computational science ; Complex system ; Statistical physics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Hongyu Yu | Yang Zhong | Liangliang Hong | Changsong Xu | Wei Ren | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2022-03-06)
Field: Materials Science — Materials Chemistry

Spin-Dependent Graph Neural Network Potential for Magnetic Materials The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic moments and structural degrees of freedom remains a challenge. This work introduces SpinGNN, a spin-dependent interatomic potential approach that employs the graph neural network (GNN) to describe magnetic systems. SpinGNN consists of two types of edge GNNs: Heisenberg edge GNN (HEGNN) and spin-distance edge GNN (SEGNN). HEGNN is tailored to capture Heisenberg-type spin-lattice interactions, while SEGNN accurately models multi-body and high-order spin-lattice coupling. The effectiveness of SpinGNN is demonstrated by its exceptional precision in fitting a high-order spin Hamiltonian and two complex spin-lattice Hamiltonians with great precision. Furthermore, it successfully models the subtle spin-lattice coupling in BiFeO3 and performs large-scale spin-lattice dynamics simulations, predicting its antiferromagnetic ground state, magnetic phase transition, and domain wall energy landscape with high accuracy. Our study broadens the scope of graph neural network potentials to magnetic systems, serving as a foundation for carrying out large-scale spin-lattice dynamic simulations of such systems. Heisenberg model ; Condensed matter physics ; Physics ; Lattice (music) ; Interatomic potential ; Antiferromagnetism ; Ground state ; Hamiltonian (control theory) ; Statistical physics ; Molecular dynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Quantum many-body systems
Authors: Hiroyuki Teramae | Meiyan Xuan | Jun Takayama | Mari Okazaki | Takeshi Sakamoto
Source: Journal of Computer Chemistry Japan (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Machine Learning Study of Antioxidant Effects with Molecular Orbital Energies as Explanatory Variables The values of the DPPH free radical scavenging ability values (IC50) of ferulic acid and its derivatives have been estimated by machine learning with only molecular orbital (MO) energies as the explanatory variables. We use four machine learning regression methods, Partial Least Square (pls), Random Forest (rf), Multi-Layer Perceptron Neural Network with Optional Monotonicity Constraints (monmlp) and eXtreme Gradient Boosting with Linear model (xgbLinear), using R-package caret. We use 22 molecules for the training set and 6 molecules for the test set. The root mean square (RMS) errors of predicted values for the test set are used for estimating the precision of the training. The best result is obtained by xgbLinear just using two MO energies (HOMO and LUMO). It has been proved that the IC50 values can be predicted by the molecular orbital energies only. Gradient boosting ; Test set ; HOMO/LUMO ; Linear regression ; Artificial neural network ; Ferulic acid ; Molecule ; Random forest ; Regression ; Mathematics ; Chemistry ; Artificial intelligence ; Statistics ; Computer science ; Chromatography ; Organic chemistry Free Radicals and Antioxidants
Authors: Xiaoxin Lu | Nan Cheng | Jibao Lu | Rong Sun
Source: 2022 23rd International Conference on Electronic Packaging Technology (ICEPT) (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

Predicting effective thermal conductivity of thermal interface materials using machine learning Particle-laden composites have been reported to be widely used thermal interfacial materials (TIM), which is located between integrated heat spreader and chip and can promote the heat dissipation in the electronic packaging area. In this paper, we propose a data-driven algorithm to efficiently predict the thermal performance of the Al2O3/silicone and Al2O3-AlN/silicone composites combining the numerical high-throughput computation and machine learning algorithms, which can further benefit the design of the formula. Specifically, the geometric models of the particulate composites with various compositions are firstly constructed by GeoDict software and the corresponding thermal properties of the models are simulated. Based on the obtained database, various machine learning models are established and evaluated, including Linear Regression, Decision Tree, Random Forest, Support Vector Machine and Artificial Neural Network. Comparing the performance measures of these models such as MSE, MAE and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , we conclude that the random forest and artificial neural network have best performance in predicting the thermal properties of the particulate TIMs, showing satisfactory accuracy. Artificial neural network ; Thermal conductivity ; Thermal grease ; Support vector machine ; Computer science ; Thermal ; Machine learning ; Computation ; Artificial intelligence ; Algorithm ; Silicone ; Materials science ; Mechanical engineering ; Composite material ; Engineering ; Thermodynamics ; Physics Thermal properties of materials ; Composite Material Mechanics ; Heat Transfer and Optimization
Authors: Jack Ceroni | Torin F. Stetina | Mária Kieferová | Carlos Ortiz Marrero | Juan Miguel Arrazola | Nathan Wiebe
Source: arXiv (Cornell University) (preprint, 2022-10-11)
Field: Materials Science — Materials Chemistry

Generating Approximate Ground States of Molecules Using Quantum Machine Learning The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method's ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover's lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. Wave function ; Quantum ; Quantum algorithm ; Ground state ; Computer science ; Function (biology) ; Artificial neural network ; Statistical physics ; Quantum state ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Chen Jiang | Tao Liu | Timing Yang | Zhenyu Jiang | Honglin Liu
Source: 2022 41st Chinese Control Conference (CCC) (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Mask R-CNN based deep learning analysis on in-situ measured crystal images with automatic dataset labelling For real-time measurement of crystal size distribution (CSD) by in-situ captured crystal images, a deep-learning based image analysis method is proposed to improve measurement accuracy and efficiency, based on the well recognized mask-regional convolutional neural network (Mask R-CNN). An automatic dataset labelling algorithm is established to facilitate preparing the training dataset that is required to include a large number of crystal image samples for effective deep learning. Moreover, an image thresholding segmentation algorithm is introduced to extract the region of interest (ROI) in each crystal image sample for training the Mask R-CNN, such that improved segmentation accuracy and efficiency could be obtained for online image analysis to measure CSD during a crystallization process. Experimental results on measuring the crystallization process of ß form L-glutamic acid (ß-LGA) are shown to verify the effectiveness and advantage of the proposed method. Artificial intelligence ; Thresholding ; Computer science ; Convolutional neural network ; Pattern recognition (psychology) ; Deep learning ; Segmentation ; Image segmentation ; Sample (material) ; Image (mathematics) ; Computer vision ; Chemistry Crystallization and Solubility Studies ; Enzyme Structure and Function ; Machine Learning in Materials Science
Authors: Yanfei Guan | Taegyo Lee | Ke Wang | Shu Yu | J. Christopher McWilliams
Source: ChemRxiv (preprint, 2022-12-16)
Field: Materials Science — Materials Chemistry

SNAr Regioselectivity Predictions: Machine Learning Trigger-ing DFT Reaction Modeling through Statistical Threshold Fast and accurate prospective predictions of the regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods have been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data dependent. Herein, we introduce a prototype seamlessly bridging machine learning and TST modeling by triggering the resource-intensive but much less domain sensitive DFT calculation only on less confident ML predictions. The proposed workflow was trained and tested on both Pfizer internal dataset and USPTO public dataset to predict regioselectivity for SNAr reactions. Our method is accurate and fast which achieves 96.3% and 94.7% accuracy predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time. Regioselectivity ; Workflow ; Computer science ; Nucleophilic aromatic substitution ; Density functional theory ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Database ; Catalysis ; Organic chemistry ; Nucleophilic substitution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: P. M. Siva Raja | R. Sumithra | S. Vidhya | Kavita Ramanan
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Deep learning neural classification for structure-property modelling with engineering alloys Integrated Computational Materials Engineering (ICME) is the method used for performing material discovery and design. Computational techniques presented a new deep learning classification method to screen the candidate material designs. The materials are adapted. In ICME process–structure–property workflows, ambiguity was ubiquitous. A Piecewise Regressive Tversky Similarity based Deep Learning Neural Classification (PRTS-DLNC) Method is introduced for minimizing as well as transmit uncertainties for robust uncertainties. PRTS-DLNC Method has number of compound data are considered as input. An input compound information were given to hidden layer 1. In that layer, piecewise regression is employed for performing the compound data analysis with structure–property linkages. After that, the regression coefficient value is sent to the hidden layer 2. In that layer, tversky similarity function is used to identify the similarity between the regression coefficient value of training compound data and threshold value. Ttversky similarity value varies from 0 to 1 and the results are transmitted to the output layer. By this way, PRTS-DLNC Method improves the performance of structure–property linkages. The computational cost of proposed PRTS-DLNC Method is higher than conventional uncertainty quantification. Property (philosophy) ; Similarity (geometry) ; Piecewise ; Artificial intelligence ; Computer science ; Artificial neural network ; Data mining ; Deep learning ; Pattern recognition (psychology) ; Algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Manufacturing Process and Optimization
Authors: Mohammad J. Eslamibidgoli | Kourosh Malek | Michael Eikerling
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Autonomous Image Analysis to Accelerate the Discovery and Integration of Energy Materials Accelerating the development of electrochemical energy devices (e.g., batteries, fuel cells, electrolysis cells) is pivotal for the transition towards a green and sustainable energy economy. A significant proportion of development efforts in this realm is relying on new functional materials as electrocatalytically active media, ionic media, porous transport media, or multi-functional electrodes. Experimental materials research harnesses a broad spectrum of imaging and visualization methods, based on modern spectroscopy, microscopy, and scattering techniques. These techniques are utilized to quantify structural characteristics of materials and to detect and monitor structural changes during fabrication and normal operating cycles of the device. The extreme diversity and the complexity of materials leave the applications of conventional methods for image processing often empirical and indiscriminate. Recently, deep learning (DL) models based on deep convolutional neural networks (ConvNets) have been transforming the field of computer vision by outperforming the classical algorithms in various problems such as image recognition, reconstruction, synthesis, style transfer, and other tasks. To leverage the advancements in DL-based approaches in materials research, ConvNet models are employed for various analysis tasks like segmentation, object detection, denoising, or super-resolution microscopy. Here, we will present DL-based methodical pipelines to automate the particle size distribution analysis of Pt nanoparticles in catalyst layers [1] and for high-throughput screening of catalyst layer inks [2] in polymer electrolyte fuel cells. References : [1] Colliard-Granero, André, Mariah Batool, Jasna Jankovic, Jenia Jitsev, Michael Eikerling, Kourosh Malek, and Mohammad Javad Eslamibidgoli. "Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells." Nanoscale (2021) DOI: 10.1039/d1nr06435e [2] Eslamibidgoli, Mohammad J., Fabian P. Tipp, Jenia Jitsev, Jasna Jankovic, Michael H. Eikerling, and Kourosh Malek. "Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells." RSC Advances 11, no. 51 (2021): 32126-32134. Computer science ; Deep learning ; Artificial intelligence ; Convolutional neural network ; Nanotechnology ; Materials science Machine Learning in Materials Science