jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haruka Yasuoka | Kenji Tagashira | Hideo Doi | Kazuaki Z. Takahashi | Jun‐ichi Fukuda | Takeshi Aoyagi
Source: NIPPON GOMU KYOKAISHI (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Informatics Study of Mechanical Properties of Liquid Crystal Elastomers Liquid crystal elastomers (LCEs) are a relatively new class of materials that display soft elasticity, that is, they can be deformed without resistance. Furthermore, LCEs show a rapid and accurate response to external stimuli such as electric, magnetic, and thermal fields. For this reason, it is expected to be applied to actuators or sensors. In order to apply these characteristics to devices, we tried to predict the characteristics of LCE by simulation. First of all, we developed an extended coarse-grained LCE model to enable simulation of systems of various architectures. Our model is a hybrid of Gay-Berne particles and Lennard-Jones particles, based on previously reported LCE modeling techniques. By using molecular dynamics (MD) method, the stress-strain curves as the response to an external force were obtained, and soft elasticity was clearly observed. Then, the regression analysis using machine learning (ML) was conducted on the results of the stress-strain curves of the MD simulations. The results indicated that spacing for a room for mobility of mesogenic units in the design variables of LCE molecules affected elasticity. In addition, the R-squared value of regression curve for stress-strain curves was 0.821, which indicates a strong correlation between the MD data and ML results. Finally, the estimation method of molecular structure from coarse-grained model is discussed. Elastomer ; Materials science ; Informatics ; Liquid crystal ; Composite material ; Polymer science ; Engineering ; Optoelectronics ; Electrical engineering Polymer Nanocomposites and Properties ; Advanced Materials and Mechanics ; Polymer composites and self-healing
Authors: Ruth Misener
Source:  (preprint, 2022-10-19)
Field: Materials Science — Materials Chemistry

Bayesian optimization and design of experiments Bayesian optimization is a popular machine learning-based approach for optimizing black-box functions, with successful applications including hyperparameter tuning of machine learning algorithms, design of engineering systems, and sensor set selection. After introducing Bayesian optimization, we present some joint research with the BASF Data Science for Materials Chemistry teams. With BASF, we’re interested to solve Bayesian optimization challenges which may simultaneously exhibit: multiple objectives, mixed-feature spaces, asynchronous decisions, large batch sizes, input constraints, multi-fidelity observations, hierarchical choices, and costs associated with switching between experimental points. We review the machine learning contributions that we’ve found useful towards achieving these goals and discuss our own methodological and software contributions. We review work from many groups. My own work is a collaboration between Imperial (Jose Pablo Folch, Alexander Thebelt, Shiqiang Zhang, Jan Kronqvist, Calvin Tsay, Ruth Misener) and BASF (Robert Lee, Behrang Shafei, Nathan Sudermann-Merx, David Walz). Bayesian optimization ; Hyperparameter ; Computer science ; Machine learning ; Artificial intelligence ; Bayesian probability ; Set (abstract data type) ; Asynchronous communication ; Industrial engineering ; Engineering Machine Learning in Materials Science ; Machine Learning and Data Classification ; Fault Detection and Control Systems
Authors: Tomoki Hirosawa | Frank Schäfer | Hideaki Maebashi | Hiroyasu Matsuura | Masao Ogata
Source: arXiv (Cornell University) (preprint, 2022-06-02)
Field: Materials Science — Materials Chemistry

Data-driven reconstruction of spectral conductivity and chemical potential from thermoelectric transport data The spectral conductivity, i.e., the electrical conductivity as a function of the Fermi energy, is a cornerstone in determining the thermoelectric transport properties of electrons. However, the spectral conductivity depends on sample-specific properties such as carrier concentrations, vacancies, charge impurities, chemical compositions, and material microstructures, making it difficult to relate the experimental result with the theoretical prediction directly. Here, we propose a data-driven approach based on machine learning to reconstruct the spectral conductivity and chemical potential from the thermoelectric transport data. Using this machine learning method, we first demonstrate that the spectral conductivity and temperature-dependent chemical potentials can be recovered within a simple toy model. In a second step, we apply our method to experimental data in doped one-dimensional telluride Ta$_4$SiTe$_4$~[T. Inohara, \textit{et al.}, Appl. Phys. Lett. \textbf{110}, 183901 (2017)] to reconstruct the spectral conductivity and chemical potential for each sample. Furthermore, the thermal conductivity of electrons and the maximal figure of merit $ZT$ are estimated from the reconstructed spectral conductivity, which provides accurate estimates beyond the Wiedemann-Franz law. Our study clarifies the connection between the thermoelectric transport properties and the low-energy electronic states of real materials, and establishes a promising route to incorporate experimental data into traditional theory-driven workflows. Thermoelectric effect ; Conductivity ; Thermoelectric materials ; Thermal conductivity ; Materials science ; Electron ; Condensed matter physics ; Thermal conduction ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Dibakar Datta
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

(Invited, Digital Presentation) Understanding Interfacial Chemo-Mechanics of Two-Dimensional Materials-Based Heterogeneous Functional Materials for Energy Storage Two-dimensional (2D) materials and their heterostructures (2D + n D, n = 0,1,2,3) attracted enormous interest in wide range of applications including electrochemical systems, e.g. , batteries. Electro-chemo-mechanics, the coupling of mechanics and electrochemistry, plays a crucial role in designing these systems. This talk considers battery as a model system and demonstrates the role of 2D materials in overcoming various interfacial electro-chemo-mechanical challenges. 2D materials are engineered as the van der Waals (vdW) “slippery” interface. For example, silicon (Si) electrodes can be placed over the graphene-coated current collector for Lithium-Ion Batteries (LIBs). This arrangement provides less stress build-up and less stress “cycling” on the vdW slippery substrate instead of a fixed interface. 2D materials such as MXenes can also be used to replace polymer binders, e.g., in Si-based LIBs. Our DFT studies show more stable performance and higher Coulombic efficiency for Si films deposited on graphene-coated nickel ( i.e., slippery interface) than conventional nickel current collectors. The interface strength of monoclinic Se (selenium) is 0.43 J/m 2 , which is similar in magnitude in amorphous Si with graphene (0.41 J/m 2 ). However, the interface strength of c -Se on a 3D aluminum (Al) current collector is higher (0.99 J/m 2 ), suggesting a stronger adhesion for 3D/3D interface than 3D/2D interface. Furthermore, interface strength variation between a -Si and Ti 3 C 2 T x MXenes are determined for various surface functional groups (T x ). The completely hydroxylated Ti 3 C 2 has the highest interface strength of 0.60 J/m 2 with a -Si. The talk also summarizes our recent efforts in developing High Dimensional Deep Learning Potential (HDDLP) to study interfacial electro-chemo-mechanics in 2D materials-based systems. Our computational results are in good agreement with experiments. Besides batteries, our comprehensive interface analyses are beneficial in advancing understanding of other 2D materials-based systems, e.g., sensors, fuel cells, solar cells, nanomedicine, soft-actuators, etc. Materials science ; Graphene ; van der Waals force ; Nanotechnology ; Substrate (aquarium) ; MXenes ; Composite material ; Chemistry ; Molecule MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Satadeep Bhattacharjee | Seung‐Cheol Lee
Source: arXiv (Cornell University) (preprint, 2022-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A general rule for predicting the magnetic moment of Cobalt-based Heusler compounds using compressed sensing and density functional theory We propose a general rule for estimating the magnetic moments of Co$ 2$(cobalt)-based Heusler alloys, especially when doped with late transition metals. We come up with a descriptor that can characterise both pure Co$_2$YZ compounds and the doped ones with the chemical formula Co$_2$Y$_{1-x}$M$_x$Z (M is the dopant) using online data for magnetic moments of Heusler alloys with Co$_2$YZ structure and compressive sensing approach. The newly proposed descriptor not only depends on the number of valence electrons of the compound also it depends on the number of unoccupied d-electrons in the doping site. A comparison of the performance of the proposed descriptor and the Slater-Pauling rule is made. Unlike the Slater-Pauling rule, which is only effective for half-metallic Heusler compounds, our machine-learning approach is more generic since it applies to any Co$_2$YZ Heusler compounds, regardless of whether they are half-metals or not. We use this new rule to estimate the magnetic moments of a few yet-to-be-discovered Heusler compounds and compare the results to density functional theory (DFT) based calculations. Finally, we use DFT and machine learning investigations to prove their stability. Density functional theory ; Magnetic moment ; Valence electron ; Heusler compound ; Materials science ; Valence (chemistry) ; Electron ; Cobalt ; Transition metal ; Electronic structure ; Doping ; Functional theory ; Condensed matter physics ; Computational chemistry ; Physics ; Quantum mechanics ; Chemistry ; Metallurgy Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques
Authors: Md Masud Rana | Duc Duy Nguyen
Source: arXiv (Cornell University) (preprint, 2022-06-01)
Field: Materials Science — Materials Chemistry

EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks. Surface (topology) ; Surface protein ; Element (criminal law) ; Computer science ; Protein ligand ; Ligand (biochemistry) ; Artificial intelligence ; Machine learning ; Biological system ; Data mining ; Mathematics ; Chemistry ; Biology ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Boyi Situ | Zihan Yan | Huo, Rubin | Wang, Kongbo | Liang Zhao | Zhang, Zhe | Zhao, Liang | Tu, Yusong
Source: arXiv (Cornell University) (preprint, 2022-04-06)
Field: Materials Science — Materials Chemistry

Locally Spontaneous Dynamic Oxygen Migration on Biphenylene: A DFT Study The dynamic oxygen migration at the interface of carbon allotropes dominated by the periodic hexagonal rings, including graphene and carbon nanotube, has opened up a new avenue to realize dynamic covalent materials. However, for the carbon materials with hybrid carbon rings, such as biphenylene, whether the dynamic oxygen migration at its interface can still be found remains unknown. Using both density functional theory calculations and machine-learning-based molecular dynamics (MLMD) simulations, we found that the oxygen migration departing away from the four-membered carbon (C4) ring is hindered, and the oxygen atom prefers to spontaneously migrate toward/around the C4 ring. This locally spontaneous dynamic oxygen migration on the biphenylene is attributed to the high barrier of about 1.5 eV for the former process and relatively low barrier of about 0.3 eV for the latter one, originating from the enhanced activity of C-O bond near/around the C4 ring due to the hybrid carbon rings structure. Moreover, the locally spontaneous dynamic oxygen migration is further confirmed by MLMD simulations. This work sheds light on the potential of biphenylene as a catalyst for spatial-controlled energy conversion and provides the guidance for realizing the dynamic covalent interface at other carbon-based or two-dimensional materials. Biphenylene ; Covalent bond ; Graphene ; Carbon fibers ; Oxygen ; Chemical physics ; Molecular dynamics ; Materials science ; Ring (chemistry) ; Density functional theory ; Carbon nanotube ; Nanotechnology ; Chemistry ; Photochemistry ; Computational chemistry ; Composite material ; Composite number ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanopore and Nanochannel Transport Studies
Authors: Xiangrui Yang
Source: arXiv (Cornell University) (preprint, 2022-10-29)
Field: Materials Science — Materials Chemistry

Leveraging Orbital Information and Atomic Feature in Deep Learning Model Predicting material properties base on micro structure of materials has long been a challenging problem. Recently many deep learning methods have been developed for material property prediction. In this study, we propose a crystal representation learning framework, Orbital CrystalNet, OCrystalNet, which consists of two parts: atomic descriptor generation and graph representation learning. In OCrystalNet, we first incorporate orbital field matrix (OFM) and atomic features to construct OFM-feature atomic descriptor, and then the atomic descriptor is used as atom embedding in the atom-bond message passing module which takes advantage of the topological structure of crystal graphs to learn crystal representation. To demonstrate the capabilities of OCrystalNet we performed a number of prediction tasks on Material Project dataset and JARVIS dataset and compared our model with other baselines and state of art methods. To further present the effectiveness of OCrystalNet, we conducted ablation study and case study of our model. The results show that our model have various advantages over other state of art models. Embedding ; Representation (politics) ; Computer science ; Atom (system on chip) ; Feature (linguistics) ; Artificial intelligence ; Molecular graph ; Feature learning ; Graph ; Deep learning ; Crystal (programming language) ; Construct (python library) ; Property (philosophy) ; Theoretical computer science ; Machine learning ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Arunveer Sharma | Gurpreet Singh Josan | N.P. Raju
Source:  (article, 2022-11-18)
Field: Materials Science — Biomaterials

Predicting Sorption Behavior in Edible Bionanocomposite Films with Machine Learning Algorithms Two machine learning (ML) algorithms, viz., neural network (NN) and support vector regression (SVR) machine have been experimentally investigated to intelligently predict sorption isotherms in a biodegradable nanocomposite food packaging material. These materials were developed using dairy residuals with byproducts, which were, otherwise, wasted by the industry. The predication performances of the ML models were compared with that of conventional mathematical sorption methods, viz., Brunauer–Emmitt–Teller (BET) and Guggenhiem–Anderson–de Boer (GAB) models. The prediction efficiency of the ML models, expressed in terms of coefficient of determination (R<sup>2</sup>), mean absolute error (MAE), percent root mean squared error (%RMSE) and Accuracy (%), were experimentally found to be in the range of 0.99-1.0, 0.11-0.76, 0.32-9.78, and 82.41-100, against that of the conventional sorption models being in the range of 0.96-0.99, 0.03-1.98, 0.72-10.28, and 43.34-100, respectively. Evidently, the ML models performed fairly better than the conventional sorption methods. Sorption ; Mean squared error ; Support vector machine ; Algorithm ; Machine learning ; Artificial neural network ; Mean absolute error ; Nanocomposite ; Mathematics ; Coefficient of determination ; Artificial intelligence ; Chemistry ; Computer science ; Materials science ; Statistics ; Organic chemistry ; Adsorption ; Nanotechnology Nanocomposite Films for Food Packaging ; Advanced Chemical Sensor Technologies ; Material Properties and Processing
Authors: B. Moses Abraham | Priyanka Sinha | Prosun Halder | Jayant K. Singh
Source: arXiv (Cornell University) (preprint, 2022-12-10)
Field: Materials Science — Materials Chemistry

Fusing machine learning strategy with density functional theory to hasten the discovery of MXenes for hydrogen generation The complexity of the topological and combinatorial configuration space of MXenes can give rise to gigantic design challenges that cannot be addressed through traditional experimental or routine theoretical approaches. To this end, we establish a robust and more broadly applicable multistep workflow from the toolbox of supervised machine learning (ML) algorithms for predicting the hydrogen evolution reaction (HER) activity over 4,500 MM$^{\prime}$XT$_2$-type MXenes, where 25\% of the material space (1125 systems) is randomly selected to evaluate the HER performance using density functional theory (DFT) calculations. As the most desirable ML model, the random forest regression method with recursive feature elimination and hyperparameter optimization accurately and rapidly predicts the Gibbs free energy of hydrogen adsorption ($Δ$G$_{H}$) with a low predictive mean absolute error of 0.374 eV. Based on these observations, the H-atom adsorbed directly on top of the outermost metal atomic layer of the MM$^{\prime}$XT$_2$-type MXenes (site-2) with Nb, V, Mo, Cr and Ti metals composed of carbon based O-functionalization are discovered to be highly stable and active catalysts, surpassing that of commercially available platinum based counterparts. Overall, the physically meaningful predictions and insights of the developed ML/DFT-based multistep workflow will open new avenues for accelerated screening, rational design and discovery of potential HER catalysts. MXenes ; Density functional theory ; Computer science ; Toolbox ; Hyperparameter ; Machine learning ; Artificial intelligence ; Materials science ; Computational chemistry ; Chemistry ; Nanotechnology MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Hengrui Zhang | Wei Wayne Chen | James M. Rondinelli | Wei Chen
Source: arXiv (Cornell University) (preprint, 2022-11-15)
Field: Materials Science — Materials Chemistry

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains. Trimming ; Computer science ; Data science ; Informatics ; Entropy (arrow of time) ; Data mining ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Kristin A. Persson
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Chemistry — Electrochemistry

(Invited) Towards First-Principles Prediction of Early SEI Formation Despite decades of work, there is still considerable uncertainty regarding the major components of the solid-electrolyte interface (SEI) and its dynamic formation mechanism as a function of electrolyte and anode composition. Here we present a new data-driven first-principles framework using a combination of high-throughput calculations, reaction networks, machine learning and microkinetic modeling. Our automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. We explore this landscape using stochastic methods and shortest pathfinding algorithms, which yield the most likely reaction pathways which are then refined with transition state calculations and kinetic information. The results of the framework show promise in being able to automatically recover previous insights on single reaction pathways, as well as successfully predicting the early dynamics and competitive nature of the SEI formation. As examples, we present formation mechanisms of LEMC as compared to LEDC and recover the Peled-like separation of the SEI into inorganic and organic domains resulting from rich reactive competition. By conducting accelerated simulations at elevated temperature, we track SEI evolution, confirming the postulated reduction of lithium ethylene monocarbonate to dilithium ethylene monocarbonate and hydrogen gas. These findings furnish fundamental insights into the dynamics of SEI formation and demonstrate a path forward toward a predictive understanding of electrochemical passivation. Electrolyte ; Density functional theory ; Computer science ; Dilithium ; Reaction dynamics ; Electrochemistry ; Chemistry ; Statistical physics ; Computational chemistry ; Physics ; Ion ; Electrode ; Physical chemistry Electrochemical Analysis and Applications