jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Venkateswaran Shekar | Gareth Nicholas | Mansoor Ani Najeeb | Margaret Zeile | Vincent F. Yu | Xiaorong Wang | Dylan Slack | Zhi Li | Philip W. Nega | Emory M. Chan | Alexander J. Norquist | Joshua Schrier | Sorelle A. Friedler
Source: The Journal of Chemical Physics (article, 2022-01-23)
Field: Materials Science — Materials Chemistry

Active meta-learning for predicting and selecting perovskite crystallization experiments Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline. Machine learning ; Artificial intelligence ; Computer science ; Active learning (machine learning) ; Decision tree ; Meta learning (computer science) ; Online machine learning ; Probabilistic logic ; Set (abstract data type) ; Task (project management) ; Engineering Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Machine Learning and ELM
Authors: Kirby Broderick | Eric M. Lopato | Brook Wander | Stefan Bernhard | John R. Kitchin | Zachary W. Ulissi
Source: Applied Catalysis B: Environmental (article, 2022-09-21)
Field: Materials Science — Materials Chemistry

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions Bimetallic strip ; Adsorption ; Workflow ; Catalysis ; Nanoparticle ; Computer science ; Nanotechnology ; Throughput ; Hydrogen ; Materials science ; Biochemical engineering ; Chemistry ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Xiaobo Deng | Guokang Chen | Yifan Liao | Xi Lu | Shuangyan Hu | Tiansheng Gan | Stephan Handschuh‐Wang | Xueli Zhang
Source: Polymers (article, 2022-06-01)
Field: Materials Science — Polymers and Plastics

Self-Healable and Recyclable Dual-Shape Memory Liquid Metal–Elastomer Composites Liquid metal (LM)-polymer composites that combine the thermal and electrical conductivity of LMs with the shape-morphing capability of polymers are attracting a great deal of attention in the fields of reconfigurable electronics and soft robotics. However, investigation of the synergetic effect between the shape-changing properties of LMs and polymer matrices is lacking. Herein, a self-healable and recyclable dual-shape memory composite, comprising an LM (gallium) and a Diels-Alder (DA) crosslinked crystalline polyurethane (PU) elastomer, is reported. The composite exhibits a bilayer structure and achieves excellent shape programming abilities, due to the phase transitions of the LM and the crystalline PU elastomers. To demonstrate these shape-morphing abilities, a heat-triggered soft gripper, which can grasp and release objects according to the environmental temperature, is designed and built. Similarly, combining the electrical conductivity and the dual-shape memory effect of the composite, a light-controlled reconfigurable switch for a circuit is produced. In addition, due to the reversible nature of DA bonds, the composite is self-healable and recyclable. Both the LM and PU elastomer are recyclable, demonstrating the extremely high recycling efficiency (up to 96.7%) of the LM, as well as similar mechanical properties between the reprocessed elastomers and the pristine ones. Materials science ; Composite material ; Elastomer ; Shape-memory alloy ; Self-healing ; Metal ; Dual (grammatical number) ; Metallurgy Polymer composites and self-healing ; Dielectric materials and actuators ; Advanced Materials and Mechanics
Authors: Prithivi Rasaili | Nitin Sharma | Ajaya Bhattarai
Source: Condensed Matter (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

Comparison of Ferromagnetic Materials: Past Work, Recent Trends, and Applications Despite our traditional concept-based understanding of ferromagnetism, an investigation of this phenomenon has revealed several other facts. Ferromagnetism was previously supposed to be exhibited by only a few elements. Subsequently, it was realized that specific elements with d- or f- orbitals demonstrated this phenomenon. When elements without these orbitals exhibited ferromagnetism, intrinsic origin-based and structural defect-based theories were introduced. At present, nonmagnetic oxides, hexaborides of alkaline-earth metals, carbon structures, and nonmetallic non-oxide compounds are gaining significant attention owing to their potential applications in spintronics, electronics, biomedicine, etc. Therefore, herein, previous work, recent trends, and the applications of these materials and studies based on relevant topics, ranging from the traditional understanding of ferromagnetism to the most recent two-element-based systems, are reviewed. Ferromagnetism ; Spintronics ; Materials science ; Nanotechnology ; Atomic orbital ; Oxide ; Condensed matter physics ; Metallurgy ; Physics ; Electron ; Quantum mechanics Boron and Carbon Nanomaterials Research ; Advancements in Battery Materials ; ZnO doping and properties
Authors: Bethany Conroy | Richi Nayak | Andrea Lucia Rocha Hidalgo | Graeme J. Millar
Source: Microporous and Mesoporous Materials (article, 2022-03-10)
Field: Chemistry — Inorganic Chemistry

Evaluation and application of machine learning principles to Zeolite LTA synthesis Machine learning ; Artificial neural network ; Decision tree ; Random forest ; Artificial intelligence ; Linear regression ; Regression ; Regression analysis ; Computer science ; Tree (set theory) ; Ridge ; Algorithm ; Mathematics ; Statistics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Wentao Zhang | Wenguang Huang | Jie Tan | Qingwei Guo | Bingdang Wu
Source: Chemosphere (review, 2022-09-15)
Field: Materials Science — Materials Chemistry

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects Bottleneck ; Biochemical engineering ; Electricity ; Environmental remediation ; Heterogeneous catalysis ; Computer science ; Nanotechnology ; Catalysis ; Environmental pollution ; Chemistry ; Environmental science ; Engineering ; Materials science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Niklas Lenzen | Okyay Altay
Source: Materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Enhanced Dynamic Response Modelling of Superelastic Shape Memory Alloy Wires Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities. Therefore, they are increasingly used for the vibration control of civil engineering structures. The efficient design of SMA-based control devices requires accurate material models. However, the thermodynamically coupled SMA behavior is highly sensitive to strain rate. For an accurate modelling of the material behavior, a wide range of parameters needs to be determined by experiments, where the identification of thermodynamic parameters is particularly challenging due to required technical instruments and expert knowledge. For an efficient identification of thermodynamic parameters, this study proposes a machine-learning-based approach, which was specifically designed considering the dynamic SMA behavior. For this purpose, a feedforward artificial neural network (ANN) architecture was developed. For the generation of training data, a macroscopic constitutive SMA model was adapted considering strain rate effects. After training, the ANN can identify the searched model parameters from cyclic tensile stress-strain tests. The proposed approach is applied on superelastic SMA wires and validated by experiments. SMA* ; Shape-memory alloy ; Materials science ; Artificial neural network ; Dissipation ; Feed forward ; Deformation (meteorology) ; Smart material ; Pseudoelasticity ; Computer science ; Structural engineering ; Mechanical engineering ; Control engineering ; Artificial intelligence ; Engineering ; Composite material Shape Memory Alloy Transformations
Authors: Oluwasegun Chijioke Adekoya | Moses Ebiowei Yibowei | Gbolahan Joseph Adekoya | Emmanuel Rotimi Sadiku | Yskandar Hamam | Suprakas Sinha Ray
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Biomaterials

A mini-review on the application of machine learning in polymer nanogels for drug delivery The advent of nanotechnology has resulted in an exponential improvement in drug delivery systems. Special attention is drawn to the use of nanogels which are nanosized hydrogels as effective drug delivery polymeric materials. Nanogels are 3-dimensional polymeric chains with sizes ranging from 100 to 200 nm. Their non-toxicity, biocompatibility, and biodegradability make them well suited for this purpose. Emerging studies have shown that the use of machine learning (ML) can optimize the drug-carrying and delivery of nanogels. This review would identify the mechanisms of nanogel drug delivery, commonly used machine learning models, areas of possible application of machine learning as it concerns nanogel drug delivery, and limitations in the application of machine learning. Nanogel ; Drug delivery ; Biocompatibility ; Self-healing hydrogels ; Nanotechnology ; Materials science ; Targeted drug delivery ; Computer science ; Polymer chemistry Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; 3D Printing in Biomedical Research
Authors: Zekun Chen | Fernanda C. Bononi | C. Sievers | Wang‐Yeuk Kong | Davide Donadio
Source: Journal of Chemical Theory and Computation (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

UV–Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning Predicting UV-visible absorption spectra is essential to understand photochemical processes and design energy materials. Quantum chemical methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensive, especially for large systems or when one computes line shapes from thermal averages. Here, we present an approach to predict UV-visible absorption spectra of solvated aromatic molecules by quantum chemistry (QC) and machine learning (ML). We show that a ML model, trained on the high-level QC calculation of the excitation energy of a set of aromatic molecules, can accurately predict the line shape of the lowest-energy UV-visible absorption band of several related molecules with less than 0.1 eV deviation with respect to reference experimental spectra. Applying linear decomposition analysis on the excitation energies, we unveil that our ML models probe vertical excitations of these aromatic molecules primarily by learning the atomic environment of their phenyl rings, which align with the physical origin of the π →π* electronic transition. Our study provides an effective workflow that combines ML with quantum chemical methods to accelerate the calculations of UV-visible absorption spectra for various molecular systems. Spectral line ; Molecule ; Absorption spectroscopy ; Absorption (acoustics) ; Chemistry ; Visible spectrum ; Chemical physics ; Atomic physics ; Materials science ; Physics ; Optoelectronics ; Optics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jeffrey Law | Shubham Pandey | Prashun Gorai | Peter C. St. John
Source: JACS Au (article, 2022-12-31)
Field: Materials Science — Materials Chemistry

Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach to finding stable inorganic functional materials. We start by defining an upper bound to the fully relaxed energy obtained via DFT as the energy resulting from a constrained optimization over only cell volume. Because the fractional atomic coordinates for these calculations are known <i>a priori</i>, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural network (GNN). We generate new structures via ionic substitution of known prototypes, and train our GNN on a new database of 128 000 DFT calculations comprising both fully relaxed and volume-only relaxed structures. By minimizing the predicted upper-bound energy, we discover new stable structures with over 99% accuracy (versus DFT). We demonstrate the method by finding promising new candidates for solid-state battery (SSB) electrolytes that not only possess the required stability, but also additional functional properties such as large electrochemical stability windows and high conduction ion fraction. We expect this proposed framework to be directly applicable to a wide range of design challenges in materials science. Density functional theory ; Upper and lower bounds ; Stability (learning theory) ; Computer science ; Energy minimization ; Invariant (physics) ; Minification ; A priori and a posteriori ; Energy functional ; Graph theory ; Chemical stability ; Computational chemistry ; Chemistry ; Mathematics ; Physics ; Thermodynamics ; Machine learning ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Lixue Cheng | Jiace Sun | Thomas F. Miller
Source: Journal of Chemical Theory and Computation (article, 2022-07-20)
Field: Materials Science — Materials Chemistry

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [ <i>J. Chem. Theory Comput.</i> 2019, 15, 6668] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantages of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and exhibiting improved performance with an increasing number of training examples. The resulting clusters from supervised or unsupervised clustering are further combined with scalable Gaussian process regression (GPR) or linear regression (LR) to learn molecular energies accurately by generating a local regression model in each cluster. Among all four combinations of regressors and clustering methods, GMM combined with scalable exact GPR (GMM/GPR) is the most efficient training protocol for MOB-ML. The numerical tests of molecular energy learning on thermalized data sets of drug-like molecules demonstrate the improved accuracy, transferability, and learning efficiency of GMM/GPR over other training protocols for MOB-ML, i.e., supervised regression clustering combined with GPR (RC/GPR) and GPR without clustering. GMM/GPR also provides the best molecular energy predictions compared with ones from the literature on the same benchmark data sets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in wall-clock training time compared with scalable exact GPR with a training size of 6500 QM7b-T molecules. Cluster analysis ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Kriging ; Ground-penetrating radar ; Gaussian process ; Mixture model ; Scalability ; Unsupervised learning ; Machine learning ; Gaussian ; Chemistry ; Computational chemistry ; Radar ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Daeseong Yong | Jaeup U. Kim
Source: Macromolecules (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

Accelerating Langevin Field-Theoretic Simulation of Polymers with Deep Learning Langevin field-theoretic simulation (L-FTS) is a promising tool in polymer field theory that can account for the compositional fluctuation effect, which is neglected in the self-consistent field theory (SCFT). However, L-FTS is a computationally expensive tool, and it may take more than a week to accurately calculate ensemble averages of thermodynamic quantities. In our previous study, we introduced a deep neural network (DNN) that estimates the saddle point of the pressure field to reduce the subsequent Anderson mixing (AM) iterations. Herein, we propose a novel DNN that can be successively applied to determine the saddle point without using conventional field-update algorithms. Major deep learning (DL) models for semantic segmentation in computer vision are adopted to construct the optimal DNN architecture. Our model utilizing atrous convolutions in parallel is accurate and computationally efficient, and it is robust to the simulation parameter changes and can consequently be reused after single training. We demonstrate that our DNN can achieve speedup of factor 6 or more compared to the AM method without affecting accuracy. Open-source code for our deep Langevin FTS (DL-FTS) enables an easy and rapid Python scripting of SCFT and L-FTS incorporated with CPU or GPU parallelization and DL. Computer science ; Python (programming language) ; Langevin dynamics ; Deep learning ; Field (mathematics) ; Speedup ; Saddle point ; Artificial intelligence ; Algorithm ; Floating point ; Artificial neural network ; Computational science ; Statistical physics ; Parallel computing ; Mathematics ; Physics ; Geometry Block Copolymer Self-Assembly ; Protein Structure and Dynamics ; Theoretical and Computational Physics
Authors: Ibrahim Yakub | Ahmad Beng Hong Kueh | Edwin Pineda | Md. Rezaur Rahman | Mohamad Hardyman Barawi | Mohammad Omar Abdullah | Mugahed Amran | Роман Федюк | Nikolai Vatin
Source: Catalysts (article, 2022-07-14)
Field: Materials Science — Materials Chemistry

Employing an Artificial Neural Network in Correlating a Hydrogen-Selective Catalytic Reduction Performance with Crystallite Sizes of a Biomass-Derived Bimetallic Catalyst A predictive model correlating the properties of a catalyst with its performance would be beneficial for the development, from biomass waste, of new, carbon-supported and Earth-abundant metal oxide catalysts. In this work, the effects of copper and iron oxide crystallite size on the performance of the catalysts in reducing nitrogen oxides, in terms of nitrogen oxide conversion and nitrogen selectivity, are investigated. The catalysts are prepared via the incipient wetness method over activated carbon, derived from palm kernel shells. The surface morphology and particle size distribution are examined via field emission scanning electron microscopy, while crystallite size is determined using the wide-angle X-ray scattering and small-angle X-ray scattering methods. It is revealed that the copper-to-iron ratio affects the crystal phases and size distribution over the carbon support. Catalytic performance is then tested using a packed-bed reactor to investigate the nitrogen oxide conversion and nitrogen selectivity. Departing from chemical characterization, two predictive equations are developed via an artificial neural network technique—one for the prediction of NOx conversion and another for N2 selectivity. The model is highly applicable for 250–300 °C operating temperatures, while more data are required for a lower temperature range. Catalysis ; Materials science ; Crystallite ; Bimetallic strip ; Chemical engineering ; Oxide ; Copper oxide ; Inorganic chemistry ; Carbon fibers ; Particle size ; Metal ; Chemistry ; Metallurgy ; Composite material ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Hydrodesulfurization Studies
Authors: Zun Wang | Chong Wang | Sibo Zhao | Yong Xu | Shaogang Hao | Chang Yu Hsieh | Bing-Lin Gu | Wenhui Duan
Source: npj Computational Materials (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

Heterogeneous relational message passing networks for molecular dynamics simulations Abstract With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology. While existing machine learning models have yielded superior performances in many occasions, most of them model and process molecular systems in terms of homogeneous graph, which severely limits the expressive power for representing diverse interactions. In practice, graph data with multiple node and edge types is ubiquitous and more appropriate for molecular systems. Thus, we propose the heterogeneous relational message passing network (HermNet), an end-to-end heterogeneous graph neural networks, to efficiently express multiple interactions in a single model with ab initio accuracy. HermNet performs impressively against many top-performing models on both molecular and extended systems. Specifically, HermNet outperforms other tested models in nearly 75%, 83% and 69% of tasks on revised Molecular Dynamics 17 (rMD17), Quantum Machines 9 (QM9) and extended systems datasets, respectively. In addition, molecular dynamics simulations and material property calculations are performed with HermNet to demonstrate its performance. Finally, we elucidate how the design of HermNet is compatible with quantum mechanics from the perspective of the density functional theory. Besides, HermNet is a universal framework, whose sub-networks could be replaced by other advanced models. Computer science ; Message passing ; Theoretical computer science ; Graph ; Molecular dynamics ; Artificial neural network ; Homogeneous ; Process (computing) ; Artificial intelligence ; Distributed computing ; Statistical physics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Barış Demirbay | Duygu Bayram | Şaziye Uğur
Source: Expert Systems with Applications (article, 2022-06-26)
Field: Chemistry — Electrochemistry

Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films Artificial neural network ; Multivariate statistics ; Computer science ; Artificial intelligence ; Mathematics ; Statistics Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis ; Optical Imaging and Spectroscopy Techniques