jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arda Genç | Libor Kovařík | Hamish L. Fraser
Source: Scientific Reports (article, 2022-09-28)
Field: Materials Science — Surfaces, Coatings and Films

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net's fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions. Segmentation ; Artificial intelligence ; Computer science ; Deep learning ; Ground truth ; Hausdorff distance ; Pattern recognition (psychology) ; Sørensen–Dice coefficient ; Convolutional neural network ; Image segmentation Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Yasaman J. Soofi | Md Asad Rahman | Yijia Gu | Jinling Liu
Source: Computational Materials Science (article, 2022-09-14)
Field: Materials Science — Materials Chemistry

A feasibility study of machine learning-assisted alloy design using wrought aluminum alloys as an example Soundness ; Alloy ; Computer science ; Reliability (semiconductor) ; Set (abstract data type) ; Variance (accounting) ; Machine learning ; Feature (linguistics) ; Artificial intelligence ; Feature engineering ; Materials science ; Deep learning ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Additive Manufacturing Materials and Processes
Authors: Eleonora Gianti | Simona Percec
Source: Biomacromolecules (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go? This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of <i>biomacromolecules</i>. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, <i>learn</i>, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules. Computer science ; Set (abstract data type) ; Interface (matter) ; Chemical space ; Theoretical computer science ; Data science ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Nicolai Ree | Andreas H. Göller | Jan H. Jensen
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution reactions. Regioselectivity ; Electrophilic aromatic substitution ; Electrophilic substitution ; Electrophile ; Substitution (logic) ; Substitution reaction ; Chemistry ; Computer science ; Artificial intelligence ; Combinatorial chemistry ; Organic chemistry ; Programming language ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Huseyin Ucar | Durga Paudyal | Kamal Choudhary
Source: Computational Materials Science (article, 2022-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning predicted magnetic entropy change using chemical descriptors across a large compositional landscape Magnetocaloric refrigeration has drawn considerable attention in the last few decades as it can positively disrupt the current cooling technology. Most research efforts focus on developing new magnetic materials in the laboratory by trial and error. Here we report a materials dataset developed using past experimental work comprising several important magnetocaloric material classes such as La(Fe,Si/Al)13, heusler alloys, manganites, Gd5(Si,Ge)4 family, rare-earth and metallic glasses as well as Laves phase compounds with their reported magnetic entropy changes, -ΔSM(T,H). Notable linear and non-linear machine learning models are implemented to predict the -ΔSM(T,H) of materials. Our analyses indicate that the Random Forest model outperforms the others with R2 of 0.82. We then use this model to screen a large magnetic materials database with nearly 40,000 compounds to identify potential new magnetocaloric materials operating near room temperature. MnGa2Sb2, CrGa2Sb2, SbSCl0.1I0.9, Sm3Te4, LaRhSn, SbSI, Tl0.58Rb0.42Fe1.72Se2, Cs0.86Fe1.66Se2, La2.1MnGe2.2 are some of the newly predicted compounds that could yield large magnetocaloric cooling performance. Magnetic refrigeration ; Materials science ; Laves phase ; Thermodynamics ; Rare earth ; Machine learning ; Condensed matter physics ; Alloy ; Magnetic field ; Metallurgy ; Magnetization ; Computer science ; Physics ; Intermetallic Magnetic and transport properties of perovskites and related materials ; Thermal Expansion and Ionic Conductivity ; Rare-earth and actinide compounds
Authors: Jiachi Xie | Lei Zhang
Source: Applied Surface Science (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

Machine learning and symbolic regression for adsorption of atmospheric molecules on low-dimensional TiO2 Adsorption ; Molecule ; van der Waals force ; Chemistry ; Symbolic regression ; Molecular descriptor ; Chemical physics ; Computational chemistry ; Machine learning ; Biological system ; Computer science ; Physical chemistry ; Quantitative structure–activity relationship ; Organic chemistry ; Genetic programming Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Xinming Wang | Nguyen-Dung Tran | Shuming Zeng | Cong Hou | Ying Chen | Jun Ni
Source: npj Computational Materials (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Element-wise representations with ECNet for material property prediction and applications in high-entropy alloys Abstract When developing deep learning models for accurate property prediction, it is sometimes overlooked that some material physical properties are insensitive to the local atomic environment. Here, we propose the elemental convolution neural networks (ECNet) to obtain more general and global element-wise representations to accurately model material properties. It shows better prediction in properties like band gaps, refractive index, and elastic moduli of crystals. To explore its application on high-entropy alloys (HEAs), we focus on the FeNiCoCrMn/Pd systems based on the data of DFT calculation. The knowledge from less-principal element alloys can enhance performance in HEAs by transfer learning technique. Besides, the element-wise features from the parent model as universal descriptors retain good accuracy at small data limits. Using this framework, we obtain the concentration-dependent formation energy, magnetic moment and local displacement in some sub-ternary and quinary systems. The results enriched the physics of those high-entropy alloys. Quinary ; Ternary operation ; Materials science ; Entropy (arrow of time) ; High entropy alloys ; Material properties ; Statistical physics ; Computer science ; Algorithm ; Thermodynamics ; Alloy ; Physics ; Metallurgy ; Composite material Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Yilin Yang | Mingjie Liu | John R. Kitchin
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Neural network embeddings based similarity search method for atomistic systems With the popularity of machine learning growing in the field of catalysis there are increasing numbers of catalyst databases becoming available. Popularity ; Similarity (geometry) ; Artificial neural network ; Field (mathematics) ; Catalysis ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Psychology ; Mathematics ; Social psychology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Nathan C. Frey | Ryan Soklaski | Simon Axelrod | Siddharth Samsi | Rafael Gómez‐Bombarelli | Connor W. Coley | Vijay Gadepally
Source: ChemRxiv (preprint, 2022-05-16)
Field: Materials Science — Materials Chemistry

Neural Scaling of Deep Chemical Models Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of physical priors in scientific domains makes the strategies and benefits of scaling uncertain. Here, we investigate neural scaling behavior in large chemical models by varying model and dataset sizes over many orders of magnitude, studying models with over one billion parameters, pre-trained on datasets of up to ten million datapoints. We consider large language models for generative chemistry and graph neural networks for machine-learned interatomic potentials. To enable large-scale scientific deep learning studies under resource constraints, we develop the Training Performance Estimation (TPE) framework to reduce the costs of scalable hyperparameter optimization by up to 90%. Using this framework, we discover empirical neural scaling relations for deep chemical models and investigate the interplay between physical priors and scale. Potential applications of large, pre-trained models for "prompt engineering" and unsupervised representation learning of molecules are shown. Artificial intelligence ; Computer science ; Machine learning ; Deep learning ; Scaling ; Scalability ; Artificial neural network ; Hyperparameter ; Key (lock) ; Generative grammar ; Scale (ratio) ; Prior probability ; Data science ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Soroush Mahjoubi | Fan Ye | Yi Bao | Weina Meng | Xian Zhang
Source: Engineering Applications of Artificial Intelligence (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural network Artificial intelligence ; Convolutional neural network ; Computer science ; Deep learning ; Nanomanufacturing ; Graphene ; Robustness (evolution) ; Pixel ; Pattern recognition (psychology) ; Layer (electronics) ; Artificial neural network ; Machine learning ; Identification (biology) ; Materials science ; Computer vision ; Nanotechnology Graphene research and applications ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Han Xu | Sainan Ma | Yang Hou | Qinghua Zhang | Rui Wang | Yingwu Luo | Xiang Gao
Source: ACS Applied Materials & Interfaces (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Identification of Copolymer Microstructures Based on Microscopic Images The microstructure of polymer materials is an important bridge between their molecular structure and macroproperties, which is of great significance to be effectively identified. With the increasing refinement of polymer material design, the microstructure of different polymer materials gradually converges, which is difficult to distinguish. In this study, the machine learning method is applied to recognize the microstructure. A highly accurate and interpretable model based on small experimental data sets has been completed by the methods of transfer learning and feature visualization, making the result of the model that can be explained from the perspective of physical chemistry. This work provides an idea for identifying microstructure and will help further promote intelligent polymer research and development. Microstructure ; Materials science ; Polymer ; Visualization ; Identification (biology) ; Perspective (graphical) ; Copolymer ; Computer science ; Nanotechnology ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Yubing Qian | Weizhong Fu | Weiluo Ren | Ji Chen
Source: The Journal of Chemical Physics (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Interatomic force from neural network based variational quantum Monte Carlo Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as neural networks. Most of the recent efforts applying neural networks to ab initio calculation have been focusing on the energy of the system. In this study, we take a step forward and look at the interatomic force obtained with neural network wavefunction methods by implementing and testing several commonly used force estimators in variational quantum Monte Carlo (VMC). Our results show that neural network ansatz can improve the calculation of interatomic force upon traditional VMC. The relationship between the force error and the quality of the neural network, the contribution of different force terms, and the computational cost of each term is also discussed to provide guidelines for future applications. Our work demonstrates that it is promising to apply neural network wavefunction methods in simulating structures/dynamics of molecules/materials and provide training data for developing accurate force fields. Artificial neural network ; Ansatz ; Wave function ; Computer science ; Estimator ; Statistical physics ; Monte Carlo method ; Quantum Monte Carlo ; Quantum ; Ab initio ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Quantum, superfluid, helium dynamics
Authors: Kuzma Khrabrov | Ilya Shenbin | Alexander Ryabov | Artem Tsypin | Alexander Telepov | Anton Alekseev | Alexander Grishin | П. В. Страшнов | Petr Zhilyaev | Sergey Nikolenko | Artur Kadurin
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset Electronic wave function calculation is a fundamental task of computational quantum chemistry. Knowledge of the wave function parameters allows one to compute physical and chemical properties of molecules and materials. Unfortunately, it is infeasible to compute the wave functions analytically even for simple molecules. Classical quantum chemistry approaches such as the Hartree-Fock method or density functional theory (DFT) allow to compute an approximation of the wave function but are very computationally expensive. One way to lower the computational complexity is to use machine learning models that can provide sufficiently good approximations at a much lower computational cost. In this work we: (1) introduce a new curated large-scale dataset of electron structures of drug-like molecules, (2) establish a novel benchmark for the estimation of molecular properties in the multi-molecule setting, and (3) evaluate a wide range of methods with this benchmark. We show that the accuracy of recently developed machine learning models deteriorates significantly when switching from the single-molecule to the multi-molecule setting. We also show that these models lack generalization over different chemistry classes. In addition, we provide experimental evidence that larger datasets lead to better ML models in the field of quantum chemistry. Wave function ; Benchmark (surveying) ; Hamiltonian (control theory) ; Quantum chemistry ; Density functional theory ; Computer science ; Quantum ; Function (biology) ; Statistical physics ; Range (aeronautics) ; Generalization ; Scale (ratio) ; Computational complexity theory ; Molecule ; Computational chemistry ; Algorithm ; Chemistry ; Quantum mechanics ; Physics ; Mathematics ; Mathematical optimization ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Liu Jian-qiu | Minsheng Huang | Zhenhuan Li | Lv Zhao | Yaxin Zhu
Source: Engineering Fracture Mechanics (article, 2022-02-17)
Field: Materials Science — Metals and Alloys

A deep learning method for predicting microvoid growth in heterogeneous polycrystals Materials science ; Deep learning ; Convolutional neural network ; Artificial intelligence ; Microstructure ; Artificial neural network ; Machine learning ; Computer science ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Mengyue Zhu | Jing Chen | Yiyang Lin
Source: Supramolecular Materials (article, 2022-12-31)
Field: Materials Science — Biomaterials

Exploring chemical space and structural diversity of supramolecular peptide materials Searching chemical space and expanding the structural diversity of supramolecular self-assembly based on the development of combinatorial libraries is significant to the guided design of bio-inspired materials. Here we discuss the peptide self-assembly into a diversity of nanostructures, as well as their network organization into macroscopic hydrogel using secondary structures of α-helix, β-sheet, and coiled-coil peptides. In particular, we highlight the recent advances in developing computational and experimental tools to explore the vast combinatorial space, uncover structure-activity relationships and identify the factors that determine peptide self-assembly. We envision that the integration of newly developed techniques such as high throughput screening, automated flow chemistry, and machine learning into the screening of peptide libraries will offer new opportunities to discover peptide-based functional materials. Supramolecular chemistry ; Chemical space ; Peptide ; Nanotechnology ; Computer science ; Space (punctuation) ; Computational biology ; Chemistry ; Materials science ; Biology ; Biochemistry ; Molecule Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Advanced biosensing and bioanalysis techniques
Authors: Buyu Zhang | Mark Asta | Lin‐Wang Wang
Source: Computational Materials Science (article, 2022-08-12)
Field: Materials Science — Metals and Alloys

Machine learning force field for Fe-H system and investigation on role of hydrogen on the crack propagation in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si15.svg"><mml:mrow><mml:mi mathvariant="bold-italic">α</mml:mi></mml:mrow></mml:math>-Fe A machine learning force field of Fe-H is constructed to study the hydrogen embrittlement via molecular dynamics (MD) simulations. The machine learning force field (MLFF) is obtained by learning the results of the density functional theory (DFT) MD calculation for various configurations, using a neural networks (NNs) model, with decomposed atomic energies in DFT calculations. Various tests are carried out to ensure that the MLFF describes well the statistic and dynamic property of iron + hydrogen systems. Using this MLFF, MD simulations of model iron sample with a small pre-crack tip at different grain boundaries are carried out with different loadings. We found that a high concentration of H ahead the crack tip will enhance the propagation of the crack, while the degree of enhancement depends on the type of the grain boundary. We also found the formation of microvoid ahead of crack and it might also contribute to the crack propagation. Embrittlement ; Grain boundary ; Molecular dynamics ; Materials science ; Force field (fiction) ; Hydrogen embrittlement ; Field (mathematics) ; Hydrogen ; Density functional theory ; Artificial neural network ; Fracture mechanics ; Statistical physics ; Computer science ; Machine learning ; Artificial intelligence ; Composite material ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Quantum mechanics Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Davis Unruh | Venkata Surya Chaitanya Kolluru | Arun Baskaran | Yiming Chen | Maria K. Y. Chan
Source: MRS Bulletin (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities Microscopy ; Spectroscopy ; Instrumentation (computer programming) ; Characterization (materials science) ; Interpretation (philosophy) ; Materials science ; Nanotechnology ; Computer science ; Optics ; Physics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Geochemistry and Geologic Mapping