jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vahe Gharakhanyan | Luke J. Wirth | José Antonio Garrido Torres | Ethan Eisenberg | Ting Wang | Dallas R. Trinkle | Snigdhansu Chatterjee | Alexander Urban
Source: The Journal of Chemical Physics (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. Interpretability ; Machine learning ; Artificial intelligence ; Binary number ; Computer science ; Stability (learning theory) ; Melting point ; Feature (linguistics) ; Unsupervised learning ; Materials science ; Mathematics Machine Learning in Materials Science ; Ionic liquids properties and applications ; Thermal and Kinetic Analysis
Authors: Yuliang Guo | Xiaobo Sun | Handong Jiao | Liwen Zhang | Wenxuan Qin | Xiaoli Xi | Zuoren Nie
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Effect of electric fields on tungsten distribution in Na<sub>2</sub>WO<sub>4</sub>–WO<sub>3</sub> molten salt Tungsten coatings have unique properties such as high melting points and hardness and are widely used in the nuclear fusion and aviation fields. In experiments, compared to pure Na<sub>2</sub>WO<sub>4</sub> molten salt, electrolysis with Na<sub>2</sub>WO<sub>4</sub>-WO<sub>3</sub> molten salt results in a lower deposition voltage. Herein, an investigation combining experimental and computational approaches was conducted, involving molecular dynamics simulations with deep learning, high-temperature <i>in situ</i> Raman spectroscopy and activation strain model analysis. The results indicated that the molten salt system's behaviour, influenced by migration and polarization effects, led to increased formation of Na<sub>2</sub>W<sub>2</sub>O<sub>7</sub> in the Na<sub>2</sub>WO<sub>4</sub>-WO<sub>3</sub> molten salt, which has a lower decomposition voltage and subsequently accelerated the cathodic deposition of tungsten. We analyzed the mechanism of the effect of the electric field on the Na<sub>2</sub>W<sub>2</sub>O<sub>7</sub> structure based on the bond strength and electron density. This research provides crucial theoretical support for the effect of electric field on tungsten in molten salt and demonstrates the feasibility of using machine learning-based DPMD methods in simulating tungsten-containing molten salt systems. Molten salt ; Tungsten ; Electrolysis ; Electric field ; Materials science ; Raman spectroscopy ; Electrochemistry ; Inorganic chemistry ; Analytical Chemistry (journal) ; Chemistry ; Metallurgy ; Physical chemistry ; Electrode Nuclear Materials and Properties ; Fusion materials and technologies ; Molten salt chemistry and electrochemical processes
Authors: Zachary M. Sherman | Jiho Kang | Delia J. Milliron | Thomas M. Truskett
Source: The Journal of Physical Chemistry Letters (article, 2024-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Illuminating Disorder: Optical Properties of Complex Plasmonic Assemblies The optical properties of disordered plasmonic nanoparticle assemblies can be continuously tuned through the structural organization and composition of their colloidal building blocks. However, progress in the design and experimental realization of these materials has been limited by challenges associated with controlling and characterizing disordered assemblies and predicting their optical properties. This Perspective discusses integrated studies of experimental assembly of disordered optical materials, such as doped metal oxide nanocrystal gels and metasurfaces, with electromagnetic computations on large-scale simulated structures. The simulations prove vital for connecting experimental parameters to disordered structural motifs and optical properties, revealing structure-property relations that inform design choices. Opportunities are identified for optimizing optical property designs for disordered materials using computational inverse methods and tools from machine learning. Plasmon ; Materials science ; Nanotechnology ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: Tabitha Jones | Deyue Zhou | Jia Liu | Ivan P. Parkin | Tung‐Chun Lee
Source: Journal of Materials Chemistry B (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantitative multiplexing of uric acid and creatinine using polydisperse plasmonic nanoparticles enabled by electrochemical-SERS and machine learning Surface-enhanced Raman spectroscopy (SERS) is a promising technique for the detection of biomarkers, but it can struggle to quantify multiple analytes in complex fluids. This study combines electrochemical SERS (E-SERS) and machine learning for the quantitative multiplexed detection of uric acid (UA) and creatinine (CRN). Using classical polydisperse Ag nanoparticles (NPs) made by scalable synthesis, we achieved quantitative multiplexing with low limits of detection (LoDs) and high prediction accuracy, comparable to those made by sophisticated approaches. The E-SERS LoDs at the optimal applied potentials were 0.127 μM and 0.354 μM for UA and CRN respectively, compared to 0.504 μM and 1.02 μM for conventional SERS (recorded at 0 V). By collecting a multi-dimensional E-SERS dataset and applying a two-step partial least squares regression - multilayer perceptron (PLSR-MLP) machine learning algorithm, we were able to identify the analyte concentrations in unseen spectra with a prediction accuracy of 0.94. This research demonstrates the potential of E-SERS and machine learning for multiplexed detection in clinical settings. Materials science ; Analyte ; Uric acid ; Plasmon ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Electrochemistry ; Nanoparticle ; Nanotechnology ; Multiplexing ; Plasmonic nanoparticles ; Raman scattering ; Chromatography ; Chemistry ; Computer science ; Optoelectronics ; Optics ; Biochemistry ; Physical chemistry ; Electrode ; Telecommunications ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Jeehyun Jeong | Suyeon Park | Juhyung Park | Jeong Han Song | Jeonghun Kwak
Source: Advanced Energy Materials (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Assisted Process Optimization for High‐Performance Organic Thermoelectrics Abstract Optimizing the processing conditions of conjugated polymers is a key step in improving the performance of various organic electronic devices, including organic thermoelectric (TE) devices that are capable of harvesting energy from waste heat. However, the inherent structural and energetic disorders in these materials and their unpredictable property changes after processing complicate the optimization principles, requiring numerous trial‐and‐error experiments to maximize the TE performance. Here, a machine‐learning (ML)‐based design of experiments approach is introduced to address these challenges, and its effectiveness is demonstrated using a representative thiophene‐based doped polymer system for organic TE. This approach not only helps to quantitatively understand how each processing parameter affects the TE properties of the polymer but also facilitates the prediction of optimal processing conditions, leading to achieve maximum TE performance with minimal experiments within a large parameter space. Furthermore, the ML‐based approach is validated by revealing the origins of the power factor maximization at the ML‐predicted processing conditions through analysis of the morphology and electronic states of the doped polymer films. The proposed methodology is applicable to the majority of polymer systems for organic thermoelectric energy conversion and will provide guidelines for determining optimal process conditions and improving TE performance with minimal effort. Materials science ; Thermoelectric materials ; Process (computing) ; Process engineering ; Nanotechnology ; Engineering physics ; Computer science ; Composite material ; Engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: A. K. Nair | Carlos Da Silva | Cristina H. Amon
Source: APL Machine Learning (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures Predicting the thermal conductivity of two-dimensional (2D) heterostructures is challenging and cannot be adequately resolved using conventional computational approaches. To address this challenge, we propose a new and efficient approach that combines first-principles density functional theory (DFT) calculations with a machine-learning interatomic potential (MLIP) methodology to determine the thermal conductivity of a novel 2D van der Waals TiS2/MoS2 heterostructure. We leverage the proposed approach to estimate the thermal conductivities of TiS2/MoS2 heterostructures as well as bilayer-TiS2 and bilayer-MoS2. A unique aspect of this approach is the combined implementation of the moment tensor potential for short-range (intralayer) interactions and the D3-dispersion correction scheme for long-range (interlayer) van der Waals interactions. This approach employs relatively inexpensive computational DFT-based datasets generated from ab initio molecular dynamics simulations to accurately describe the interatomic interactions in the bilayers. The thermal conductivities of the bilayers exhibit the following trend: bilayer-TiS2 &amp;gt; bilayer-MoS2 &amp;gt; the TiS2/MoS2 heterostructure. In addition, this work makes the case that the 2D bilayers exhibit considerably higher thermal conductivities than bulk graphite, a common battery anode material, indicating the potential to utilize 2D heterostructures in thermal management applications and energy storage devices. Furthermore, the MLIP-based methodology provides a reliable approach for estimating the thermal conductivity of bilayers and heterostructures. Thermal conductivity ; van der Waals force ; Condensed matter physics ; Heterojunction ; Materials science ; Conductivity ; Thermal ; Thermodynamics ; Physics ; Quantum mechanics ; Composite material ; Molecule Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Haiyan Gong | Zhongheng Fu | Lingwei Ma | Dawei Zhang
Source: npj Materials Degradation (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Inhibitor_Mol_VAE: a variational autoencoder approach for generating corrosion inhibitor molecules Deep learning-based generative modeling demonstrates proven advantages as an effective approach in molecular discovery. This study introduces a generative-network based method called Inhibitor_Mol_VAE, which uses a variational autoencoder model to generate corrosion inhibitor molecules with targeted inhibition efficiency. We first evaluate the model’s ability to reconstruct molecules. Then, we assess the model’s ability to generate new inhibitor molecules using physiochemical properties (including MolWt, LogP, Vdw_volume, and Electronegativity). New molecules with high inhibition efficiencies at low concentrations, such as [ethoxy(methoxy)phosphoryl]-phenylmethanol and (alpha-methylamino-benzyl)-phosphonsaeure-monoaethylester are successfully discovered. Autoencoder ; Corrosion inhibitor ; Molecule ; Chemistry ; Computational chemistry ; Materials science ; Corrosion ; Computer science ; Artificial intelligence ; Metallurgy ; Organic chemistry ; Artificial neural network Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Benjamin B. Hoar | Weitong Zhang | Yuanzhou Chen | Jingwen Sun | Hongyuan Sheng | Yucheng Zhang | Yisi Chen | Jenny Y. Yang | Cyrille Costentin | Quanquan Gu | Chong Liu
Source: ACS electrochemistry. (article, 2024-10-03)
Field: Chemistry — Electrochemistry

Redox-Detecting Deep Learning for Mechanism Discernment in Cyclic Voltammograms of Multiple Redox Events In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within cyclic voltammograms of multiple redox events. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal <i>a priori</i> knowledge. The DL model will augment human researchers' productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory. Redox ; Discernment ; Cyclic voltammetry ; Mechanism (biology) ; Chemistry ; Combinatorial chemistry ; Electrochemistry ; Inorganic chemistry ; Electrode ; Physics ; Physical chemistry ; Philosophy Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Yu Song | Xiaonan Lu | Kaixin Wang | Joseph V. Ryan | Morten M. Smedskjær | John D. Vienna | Mathieu Bauchy
Source: npj Materials Degradation (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Unveiling the effect of composition on nuclear waste immobilization glasses’ durability by nonparametric machine learning Abstract Ensuring the long-term chemical durability of glasses is critical for nuclear waste immobilization operations. Durable glasses usually undergo qualification for disposal based on their response to standardized tests such as the product consistency test or the vapor hydration test (VHT). The VHT uses elevated temperature and water vapor to accelerate glass alteration and the formation of secondary phases. Understanding the relationship between glass composition and VHT response is of fundamental and practical interest. However, this relationship is complex, non-linear, and sometimes fairly variable, posing challenges in identifying the distinct effect of individual oxides on VHT response. Here, we leverage a dataset comprising 654 Hanford low-activity waste (LAW) glasses across a wide compositional envelope and employ various machine learning techniques to explore this relationship. We find that Gaussian process regression (GPR), a nonparametric regression method, yields the highest predictive accuracy. By utilizing the trained model, we discern the influence of each oxide on the glasses’ VHT response. Moreover, we discuss the trade-off between underfitting and overfitting for extrapolating the material performance in the context of sparse and heterogeneous datasets. Overfitting ; Leverage (statistics) ; Machine learning ; Context (archaeology) ; Durability ; Linear regression ; Regression ; Computer science ; Artificial intelligence ; Materials science ; Mathematics ; Statistics ; Composite material ; Artificial neural network Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Geochemistry and Geologic Mapping
Authors: Ming Hu
Source: Journal of Applied Physics (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects The discovery of advanced thermal materials with exceptional phonon properties drives technological advancements, impacting innovations from electronics to superconductors. Understanding the intricate relationship between composition, structure, and phonon thermal transport properties is crucial for speeding up such discovery. Exploring innovative materials involves navigating vast design spaces and considering chemical and structural factors on multiple scales and modalities. Artificial intelligence (AI) is transforming science and engineering and poised to transform discovery and innovation. This era offers a unique opportunity to establish a new paradigm for the discovery of advanced materials by leveraging databases, simulations, and accumulated knowledge, venturing into experimental frontiers, and incorporating cutting-edge AI technologies. In this perspective, first, the general approach of density functional theory (DFT) coupled with phonon Boltzmann transport equation (BTE) for predicting comprehensive phonon properties will be reviewed. Then, to circumvent the extremely computationally demanding DFT + BTE approach, some early studies and progress of deploying AI/machine learning (ML) models to phonon thermal transport in the context of structure–phonon property relationship prediction will be presented, and their limitations will also be discussed. Finally, a summary of current challenges and an outlook of future trends will be given. Further development of incorporating AI/ML algorithms for phonon thermal transport could range from phonon database construction to universal machine learning potential training, to inverse design of materials with target phonon properties and to extend ML models beyond traditional phonons. Current (fluid) ; Phonon ; Engineering physics ; Thermal ; Power (physics) ; Thermal conductivity ; Materials science ; Condensed matter physics ; Nanotechnology ; Physics ; Electrical engineering ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties
Authors: Xin Qi | Jim Pfaendtner
Source: Journal of Chemical Theory and Computation (article, 2024-03-19)
Field: Materials Science — Biomaterials

High-Throughput Computational Screening of Solid-Binding Peptides Inspired by biomineralization, a naturally occurring, protein-facilitated process, solid-binding peptides (SBPs) have gained much attention for their potential to fabricate various shaped nanocrystals and hierarchical nanostructures. The advantage of SBPs over other traditionally used synthetic polymers or short ligands is their tunable interaction with the solid material surface via carefully programmed sequence and being solution-dependent simultaneously. However, designing a sequence with targeted binding affinity or selectivity often involves intensive processes such as phage display, and only a limited number of sequences can be identified. Other computational efforts have also been introduced, but the validation process remains prohibitively expensive once a suitable sequence has been identified. In this paper, we present a new model to rapidly estimate the binding free energy of any given sequence to a solid surface. We show how the overall binding of a polypeptide can be estimated from the free energy contribution of each residue based on the statistics of the thermodynamically stable structure ensemble. We validated our model using five silica-binding peptides of different binding affinities and lengths and showed that the model is accurate and robust across a wider range of chemistries and binding strengths. The computational cost of this method can be as low as 3% of the commonly used enhanced sampling scheme for similar studies and has a great potential to be used in high-throughput algorithms to obtain larger training data sets for machine learning SBP screening. Binding affinities ; Sequence (biology) ; Computer science ; Throughput ; Process (computing) ; Binding site ; Affinities ; Nanotechnology ; Biological system ; Computational biology ; Chemistry ; Materials science ; Biology Diatoms and Algae Research ; Advanced Proteomics Techniques and Applications ; Machine Learning in Materials Science
Authors: Wilton J. M. Kort-Kamp | Carmelo Lo Vecchio | David Sebastián | Xiang Lyu | Plamen Atanassov | Piotr Zelenay | Vincenzo Baglio | Alexey Serov
Source: Journal of Power Sources (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode Platinum group ; Platinum ; Cathode ; Methanol ; Methanol fuel ; Group (periodic table) ; Metal ; Direct methanol fuel cell ; Materials science ; Chemistry ; Chemical engineering ; Engineering ; Metallurgy ; Catalysis ; Organic chemistry ; Physical chemistry ; Electrode ; Anode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrochemical Analysis and Applications