jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akhlak Mahmood | Melanie M. Ghelardini | Joseph B. Tracy | Yaroslava G. Yingling
Source: Chemistry of Materials (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods Anisotropic plasmonic nanorods offer a wide range of applications in photovoltaics, energy conversion, sensing, and surface-enhanced Raman spectroscopies. However, achieving control over the size and shape of the surface overcoating on these nanorods remains a challenge due to the complexity arising from the multistep wet chemical processes involved in their experimental synthesis. Here, we show that by employing data imputation and data augmentation methods, we can minimize the limitations of a small experimental data set and successfully train supervised machine learning models that can optimize the experimental synthesis. Using a small data set collected from 30 multistep syntheses of silica-overcoated gold nanorods (GNRs) characterized by optical extinction spectroscopy and transmission electron microscopy, we trained complementary supervised models to predict the overcoating shape of the nanorods using optical spectral features. The effects of experimental parameters and measurements made during different stages of the synthesis were analyzed. Our approach enabled us to design an experimental synthesis recipe to yield a target SiO2 overcoating shape on GNRs employing inverse design optimization. The developed workflow can be extended to other plasmonic nanoparticles and multistage synthesis experiments, where a limited data set is available to understand the effects of synthesis parameters and to establish correlations between measurements and synthetic yields. Nanorod ; Morphology (biology) ; Materials science ; Shell (structure) ; Nanotechnology ; Chemical engineering ; Composite material ; Engineering ; Geology Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Dendrimers and Hyperbranched Polymers
Authors: Ahmed Lamine Ben Kamri | Mohamed Abdelilah Fadla | Ibn Khaldoun Lefkaier | Cheikh lakhdar Ben Messaoud | Mohammed Benali Kanoun | Souraya Goumri‐Said
Source: Computational Condensed Matter (article, 2024-05-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-driven ensemble learning for accurate Seebeck coefficient prediction in half-Heusler compounds based on chemical formulas Seebeck coefficient ; Thermoelectric effect ; Heusler compound ; Materials science ; Condensed matter physics ; Ensemble learning ; Computer science ; Physics ; Artificial intelligence ; Thermodynamics ; Electronic structure Heusler alloys: electronic and magnetic properties ; Microwave-Assisted Synthesis and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Bikash Timalsina | Hoang-Nam Nguyen | Keivan Esfarjani
Source: Journal of Applied Physics (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Neuroevolution machine learning potential to study high temperature deformation of entropy-stabilized oxide MgNiCoCuZnO5 Entropy stabilized oxide of MgNiCoCuZnO5, also known as J14, is a material of active research interest due to a high degree of lattice distortion and tunability. Lattice distortion in J14 plays a crucial role in understanding the elastic constants and lattice thermal conductivity within the single-phase crystal. In this work, a neuroevolution machine learning potential (NEP) is developed for J14, and its accuracy has been compared to density functional theory calculations. The training errors for energy, force, and virial are 5.60 meV/atom, 97.90 meV/Å, and 45.67 meV/atom, respectively. Employing NEP potential, lattice distortion, and elastic constants is studied up to 900 K. In agreement with experimental findings, this study shows that the average lattice distortion of oxygen atoms is relatively higher than that of all transition metals in entropy-stabilized oxide. The observed distortion saturation in the J14 arises from the competing effects of minimum site distortion, which increases with increasing temperature due to enhanced thermal vibrations, and maximum site distortion, which decreases with increasing temperature. Furthermore, a series of molecular dynamics simulations up to 900 K are performed to study the stress–strain behavior. The elastic constants, bulk modulus, and ultimate tensile strength obtained from these simulations indicate a linear decrease in these properties with temperature, as J14 becomes softer owing to thermal effects. Finally, to gain some insight into thermal transport in these materials, with the so-developed NEP potential, and using non-equilibrium molecular dynamics simulations, we study the lattice thermal conductivity (κ) of the ternary compound MgNiO2 as a function of temperature. It is found that κ decreases from 4.25 W m−1 K−1 at room temperature to 3.5 W m−1 K−1 at 900 K. This suppression is attributed to the stronger scattering of low-frequency modes at higher temperatures. Neuroevolution ; Entropy (arrow of time) ; Materials science ; Deformation (meteorology) ; Artificial intelligence ; Oxide ; Thermodynamics ; Computer science ; Statistical physics ; Artificial neural network ; Composite material ; Metallurgy ; Physics Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Transition Metal Oxide Nanomaterials
Authors: Mingshan Yang | Xiangyu Li | Guozheng Kang | Weiqiu Chen
Source: Composites Science and Technology (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Understanding macroscopic thermal conduction in composites reinforced with 2D nanosheets Materials science ; Thermal conductivity ; Composite material ; Boron nitride ; Thermal conduction ; Interfacial thermal resistance ; Thermal ; Thermal contact conductance ; Graphene ; Work (physics) ; Polymer ; Thermal resistance ; Nanotechnology ; Mechanical engineering Thermal properties of materials ; Phase Change Materials Research ; Advanced Thermoelectric Materials and Devices
Authors: Sarah I. Allec | Xiaonan Lu | Daniel R. Cassar | Xuan Tung Nguyen | Vinay I. Hegde | Thiruvillamalai Mahadevan | Miroslava Peterson | Jincheng Du | Brian J. Riley | John D. Vienna | James E. Saal
Source: Journal of the American Ceramic Society (article, 2024-06-11)
Field: Materials Science — Ceramics and Composites

Evaluation of GlassNet for physics‐informed machine learning of glass stability and glass‐forming ability Abstract Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch‐screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass‐forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, we explore the application of an open‐source pre‐trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics‐informed machine learning (PIML)‐predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top‐performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected. Compounding ; Machine learning ; Computer science ; Computation ; Stability (learning theory) ; Artificial intelligence ; Artificial neural network ; Algorithm ; Materials science ; Biological system ; Composite material Glass properties and applications ; Phase-change materials and chalcogenides ; Metallic Glasses and Amorphous Alloys
Authors: Mohd. Aqib | Varad Daoo | Jayant K. Singh
Source: Energy & Fuels (article, 2024-05-13)
Field: Chemistry — Inorganic Chemistry

Efficient Xylene Isomer Separation: Accelerated Screening with Active Learning and Molecular Simulation Separating xylene isomers is vital in the petrochemical industry, yet it poses a considerable challenge due to their proximate boiling points, mandating selective adsorbents. This work utilizes active learning (AL) coupled with molecular simulations to rapidly screen 324,426 hypothetical metal–organic frameworks (hMOFs) to identify optimal materials for preferential para-xylene (pX) adsorption. To begin, a diverse subset, representative of the entire hMOF set, was curated using structural and chemical descriptors and evaluated through multiple screening methodologies. This comparative analysis highlighted the superior efficiency of AL in targeted screening processes, requiring on an average only 500 multicomponent Grand Canonical Monte Carlo simulations to identify the most pX-selective framework, encompassing 50.5% of the top 100 candidates. With an equivalent evaluation budget, both machine learning (ML) and evolutionary algorithms demonstrate an inadequate performance. While the former consistently fails to identify top performers, the latter continuously identifies significantly inferior materials. AL, on the other hand, surpasses rival approaches by effectively balancing exploration and exploitation, guiding simulations toward regions associated with high performance. Furthermore, we report the impact of different surrogate models, acquisition functions, and batch acquisition strategies on the convergence of our AL model. We found that the Gaussian process surrogate model coupled with expected improvement (EI) acquisition function and the Kriging-Believer upper bound (KBUB) acquisition strategy acquires the highest pX-selective MOF in just 86 acquisitions. Examining the top hMOF candidates revealed a complex correlation between the pX selectivity and structural features of hMOFs. In particular, the pcu topology, along with a pore size ranging from 5 to 6 Å, emerged as the dominant characteristic of top hMOFs. Furthermore, pressure-dependent simulations revealed optimal pressure maximizing pX uptake and selectivity. This computational workflow, integrating AL and molecular simulations, shows promise in accelerating data-driven material innovation for separation applications. Separation (statistics) ; Chemistry ; Chromatography ; Computer science ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications
Authors: Zhe Shi | Evgenii Tsymbalov | Wencong Shi | Ariel Barr | Qing‐Jie Li | Jiangxu Li | Xing‐Qiu Chen | Ming Dao | Subra Suresh | Ju Li
Source: Proceedings of the National Academy of Sciences (article, 2024-02-14)
Field: Materials Science — Materials Chemistry

Phonon stability boundary and deep elastic strain engineering of lattice thermal conductivity Recent studies have reported the experimental discovery that nanoscale specimens of even a natural material, such as diamond, can be deformed elastically to as much as 10% tensile elastic strain at room temperature without the onset of permanent damage or fracture. Computational work combining ab initio calculations and machine learning (ML) algorithms has further demonstrated that the bandgap of diamond can be altered significantly purely by reversible elastic straining. These findings open up unprecedented possibilities for designing materials and devices with extreme physical properties and performance characteristics for a variety of technological applications. However, a general scientific framework to guide the design of engineering materials through such elastic strain engineering (ESE) has not yet been developed. By combining first-principles calculations with ML, we present here a general approach to map out the entire phonon stability boundary in six-dimensional strain space, which can guide the ESE of a material without phase transitions. We focus on ESE of vibrational properties, including harmonic phonon dispersions, nonlinear phonon scattering, and thermal conductivity. While the framework presented here can be applied to any material, we show as an example demonstration that the room-temperature lattice thermal conductivity of diamond can be increased by more than 100% or reduced by more than 95% purely by ESE, without triggering phonon instabilities. Such a framework opens the door for tailoring of thermal-barrier, thermoelectric, and electro-optical properties of materials and devices through the purposeful design of homogeneous or inhomogeneous strains. Phonon ; Thermoelectric materials ; Thermal conductivity ; Materials science ; Strain engineering ; Diamond ; Condensed matter physics ; Phonon scattering ; Phase boundary ; Thermoelectric effect ; Ultimate tensile strength ; Nanotechnology ; Composite material ; Phase (matter) ; Phase transition ; Thermodynamics ; Chemistry ; Physics Thermal properties of materials ; High-pressure geophysics and materials ; Diamond and Carbon-based Materials Research
Authors: Zuhong Lin | Jiarong Chen | Ying Fang | Shihai Deng | Haipu Li | Ying Yang | Jingjing Yao
Source: Separation and Purification Technology (article, 2024-08-24)
Field: Chemistry — Inorganic Chemistry

Rapidly tailor metal–organic frameworks for arsenate removal using graph convolutional neural networks Arsenate ; Convolutional neural network ; Metal-organic framework ; Graph ; Computer science ; Metal ; Chemistry ; Artificial intelligence ; Theoretical computer science ; Organic chemistry ; Arsenic ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Arsenic contamination and mitigation ; Covalent Organic Framework Applications
Authors: Fuchun Ge | Ran Wang | Chen Qu | Peikun Zheng | Apurba Nandi | Riccardo Conte | Paul L. Houston | Joel M. Bowman | Pavlo O. Dral
Source: The Journal of Physical Chemistry Letters (preprint, 2024-04-16)
Field: Materials Science — Materials Chemistry

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks, such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by diffusion Monte Carlo simulations. Conformational isomerism ; Monte Carlo method ; Computer science ; Test set ; Training set ; Set (abstract data type) ; Point (geometry) ; Energy (signal processing) ; Artificial intelligence ; Glycine ; Algorithm ; Machine learning ; Mathematics ; Chemistry ; Physics ; Molecule ; Statistics ; Amino acid ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Wouter Vervust | Daniel T. Zhang | An Ghysels | Sander Roet | Titus S. van Erp | Enrico Riccardi
Source: Journal of Computational Chemistry (article, 2024-02-12)
Field: Materials Science — Materials Chemistry

PyRETIS 3: Conquering rare and slow events without boundaries We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user-friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long-lived metastable states and transitions with unbounded reactant and product states. Additionally, the post-analysis software PyVisa is now embedded in the main code, allowing fast use of machine-learning algorithms for clustering and visualising collective variables in the simulation data. Rare events ; Computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Complex Network Analysis Techniques
Authors: James L. Hart | Haining Pan | Saif Siddique | Noah Schnitzer | Krishnanand Mallayya | Shiyu Xu | Lena F. Kourkoutis | Eun-Ah Kim | J. Judy
Source: Proceedings of the National Academy of Sciences (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Real-space visualization of a defect-mediated charge density wave transition We study the coupled charge density wave (CDW) and insulator-to-metal transitions in the 2D quantum material 1T-TaS<sub>2</sub>. By applying in situ cryogenic 4D scanning transmission electron microscopy with in situ electrical resistance measurements, we directly visualize the CDW transition and establish that the transition is mediated by basal dislocations (stacking solitons). We find that dislocations can both nucleate and pin the transition and locally alter the transition temperature <i>T</i><sub>c</sub> by nearly ~75 K. This finding was enabled by the application of unsupervised machine learning to cluster five-dimensional, terabyte scale datasets, which demonstrate a one-to-one correlation between resistance-a global property-and local CDW domain-dislocation dynamics, thereby linking the material microstructure to device properties. This work represents a major step toward defect-engineering of quantum materials, which will become increasingly important as we aim to utilize such materials in real devices. Charge density wave ; Condensed matter physics ; Visualization ; Metal–insulator transition ; Charge density ; Physics ; Space charge ; Materials science ; Quantum mechanics ; Electron ; Computer science ; Electrical resistivity and conductivity Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Quantum and electron transport phenomena
Authors: Chandra Chowdhury
Source: ChemPhysChem (article, 2024-05-19)
Field: Chemistry — Inorganic Chemistry

Bayesian Optimization for Efficient Prediction of Gas Uptake in Nanoporous Materials The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs) are crucial for addressing global challenges like climate change, energy security, and environmental degradation. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the complex design spaces of NPMs for gas storage applications. For a MOF dataset drawn from 19 different sources, we present a quantitative evaluation of BO using a curated set of surrogate model and acquisition function couples. In our study, we employed machine learning (ML) techniques to conduct regression analysis on many models. Following this, we identified the three ML models that exhibited the highest accuracy, which were subsequently chosen as surrogates in our investigation, including the conventional Gaussian Process (GP) model. We found that GP with expected improvement (EI) as the acquisition function but without a gamma prior which is standard in Bayesian Optimisation python library (BO Torch) outperforms other surrogate models. Additionally, it should be noted that while the machine learning model that exhibits superior performance in predicting the target variable may be considered the best choice, it may not necessarily serve as the most suitable surrogate model for BO. This observation has significant importance and warrants further investigation. This comprehensive framework accelerates the pace of materials discovery and addresses urgent needs in energy storage and environmental sustainability. It is to be noted that rather than identifying new MOFs, BO primarily enhances computational efficiency by reducing the reliance on more demanding calculations, such as those involved in Grand Canonical Monte Carlo (GCMC) or Density Functional Theory (DFT). Bayesian optimization ; Computer science ; Nanoporous ; Surrogate model ; Python (programming language) ; Gaussian process ; Machine learning ; Artificial intelligence ; Bayesian probability ; Bayesian inference ; Biochemical engineering ; Gaussian ; Nanotechnology ; Chemistry ; Materials science ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science