jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sumner B. Harris | Christopher M. Rouleau | Kai Xiao | Rama K. Vasudevan
Source: npj Computational Materials (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD. Plume ; Deposition (geology) ; Pulsed laser deposition ; Plasma ; Laser ; Kinetics ; Materials science ; Computer science ; Thin film ; Convolutional neural network ; Artificial intelligence ; Optics ; Nanotechnology ; Geology ; Physics ; Meteorology Machine Learning in Materials Science
Authors: T.B. Jensen | Jakob Rørbæk Saugbjerg | Martin Lahn Henriksen | Jonathan Quinson
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2024-08-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards the automation of nanoparticle syntheses: The case study of gold nanoparticles obtained at room temperature Automation is promising to investigate in detail the effect(s) of parameters that influence the synthesis and properties of nanomaterials. However, it remains challenging to automate even selected key-steps of most wet-chemical syntheses of nanoparticles. Here, a simple, affordable, yet modular, automated station reported for the synthesis of chemicals is adapted to study nanomaterial syntheses, to timely screen larger multi-parameter spaces, to gain a deeper knowledge and awareness on parameters to control, to ultimately achieve more reproducible and sustainable syntheses. As a case study, this automated station is implemented to investigate and develop surfactant-free gold nanoparticles obtained at room temperature in alkaline water-alcohol mixtures. It is shown that the NaOH/Au molar ratio is a to-be-preferred descriptor to control the optical properties and size of the gold colloids for surfactant-free glycerol-mediated syntheses. Size control is simply achieved by carefully tuning the glycerol content and/or amount of base. For ethanol-mediated synthesis, the NaOH/Au molar ratio is also a suitable knob to control the nanoparticle properties. The results pave the way to higher throughput nanoparticle syntheses, ultimately relevant to timely build the missing larger datasets relevant for artificial intelligence, to one day routinely achieve the self-driven discovery of new nanomaterials and their related synthesis. Nanomaterials ; Nanoparticle ; Colloidal gold ; Nanotechnology ; Automation ; Molar ratio ; Modular design ; Pulmonary surfactant ; Materials science ; Computer science ; Chemistry ; Chemical engineering ; Catalysis ; Organic chemistry ; Engineering Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles ; Nanocluster Synthesis and Applications
Authors: Anish Rao | Marek Grzelczak
Source: Chemistry of Materials (article, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods In diverse fields, machine learning (ML) has sparked transformative changes, primarily driven by the wealth of big data. However, an alternative approach seeks to mine insights from “precious data”, offering the possibility to reveal missed knowledge and escape potential knowledge traps. In this context, Bayesian optimization (BO) protocols have emerged as crucial tools for optimizing the synthesis and discovery of a broad spectrum of compounds including nanoparticles. In our work, we aimed to go beyond the commonly explored experimental conditions and showcase a workflow capable of unearthing fresh insights, even in well-studied research domains. The growth of AuNRs is a nonequilibrium process that remains poorly understood despite the presence of well-established seeded growth protocols. Traditional research aimed at understanding the mechanism of AuNR growth has primarily relied on altering one reaction condition at a time. While these studies are undeniably valuable, they often fail to capture the synergies between different reaction conditions, thus constraining the depth of insights they can offer. In the present study, we exploit BO, to identify diverse experimental conditions yielding AuNRs with similar spectroscopic characteristics. Notably, we identify viable and accelerated synthesis conditions involving elevated temperatures (36–40 °C) as well as high ascorbic acid concentrations. More importantly, we note that ascorbic acid and temperature can modulate each other’s undesirable influences on the growth of AuNRs. Finally, by harnessing the power of interpretable ML algorithms, complemented by our deep chemical understanding, we revisited the established hierarchical relationships among reaction conditions that impact the El-Sayed-based growth of AuNRs. Nanorod ; Bayesian optimization ; Nanotechnology ; Materials science ; Bayesian probability ; Computer science ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Matthew R. Carbone | Phillip M. Maffettone | Xiaohui Qu | Shinjae Yoo | Deyu Lu
Source: The Journal of Physical Chemistry A (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multimodal advantage" over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines. X-ray absorption spectroscopy ; Spectroscopy ; Absorption spectroscopy ; Absorption (acoustics) ; Materials science ; X-ray spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Biological system ; Optics ; Physics ; Environmental chemistry ; Biology ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Martí Checa | Bharat Pant | Alexander A. Puretzky | Bogdan Dryzhakov | Rama K. Vasudevan | Yongtao Liu | Pravin Kavle | Arvind Dasgupta | Lane W. Martin | Ye Cao | Liam Collins | Stephen Jesse | Neus Domingo | Kyle P. Kelley
Source: Nature Nanotechnology (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

On-demand nanoengineering of in-plane ferroelectric topologies Hierarchical assemblies of ferroelectric nanodomains, so-called super-domains, can exhibit exotic morphologies that lead to distinct behaviours. Controlling these super-domains reliably is critical for realizing states with desired functional properties. Here we reveal the super-switching mechanism by using a biased atomic force microscopy tip, that is, the switching of the in-plane super-domains, of a model ferroelectric Pb<sub>0.6</sub>Sr<sub>0.4</sub>TiO<sub>3</sub>. We demonstrate that the writing process is dominated by a super-domain nucleation and stabilization process. A complex scanning-probe trajectory enables on-demand formation of intricate centre-divergent, centre-convergent and flux-closure polar structures. Correlative piezoresponse force microscopy and optical spectroscopy confirm the topological nature and tunability of the emergent structures. The precise and versatile nanolithography in a ferroic material and the stability of the generated structures, also validated by phase-field modelling, suggests potential for reliable multi-state nanodevice architectures and, thereby, an alternative route for the creation of tunable topological structures for applications in neuromorphic circuits. Ferroelectricity ; Nanoengineering ; Nanotechnology ; Materials science ; Piezoresponse force microscopy ; Plane (geometry) ; Topology (electrical circuits) ; Optoelectronics ; Engineering ; Geometry ; Electrical engineering Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Katja‐Sophia Csizi | Miguel Steiner | Markus Reiher
Source: Nature Communications (article, 2024-06-22)
Field: Materials Science — Materials Chemistry

Nanoscale chemical reaction exploration with a quantum magnifying glass Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a "quantum magnifying glass" that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability. Quantum ; Nanoscopic scale ; Computer science ; Parametrization (atmospheric modeling) ; Quantum state ; Nanotechnology ; Statistical physics ; Biological system ; Materials science ; Physics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ionic liquids properties and applications
Authors: Hamidreza Yazdani Sarvestani | Aditi Singh | Behnam Ashrafi
Source: Advanced Engineering Materials (article, 2024-07-10)
Field: Materials Science — Biomaterials

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics Nature has long inspired scientific and engineering advancements, particularly in the development of bioinspired ceramics. However, replicating nature's intricate structures through subtractive manufacturing techniques remains a significant challenge due to the limitations of precise and controlled material removal while maintaining structural integrity and complexity. This perspective article explores the transformative potential of machine learning (ML), particularly advancements in generative artificial intelligence (generative adversarial networks, transformer models) and multimodal learning, in accelerating the discovery of high‐performance bioinspired ceramics. ML offers an avenue to optimize material behavior beyond the constraints of traditional experimental methods. Recent advancements have shown ML's effectiveness in predicting mechanical properties and refining material designs, often surpassing conventional approaches. ML excels at identifying complex relationships even with incomplete data during training. The integration of cutting‐edge experimental data, cross‐scale simulations, and ML facilitates high‐fidelity multiscale modeling for predicting intricate phenomena like crack propagation paths in bioinspired ceramic structures. This article emphasizes the significant potential of ML to propel the field of bioinspired ceramics forward, paving the way for the discovery of ceramics with superior and tailored properties. Generative grammar ; Subtractive color ; Transformative learning ; Fidelity ; Bridging (networking) ; Computer science ; Ceramic ; Artificial intelligence ; Nanotechnology ; Data-driven ; Materials science ; Machine learning Calcium Carbonate Crystallization and Inhibition ; Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies
Authors: Denys Biriukov | Robert Vácha
Source: ACS Physical Chemistry Au (review, 2024-04-04)
Field: Materials Science — Materials Chemistry

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050 In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction. Mindset ; Computer science ; Data science ; Transparency (behavior) ; Field (mathematics) ; Preparedness ; Big data ; Perspective (graphical) ; Management science ; Engineering ethics ; Systems engineering ; Engineering ; Artificial intelligence ; Political science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Protein Structure and Dynamics
Authors: James P. Horwath | Xiao‐Min Lin | Hongrui He | Qingteng Zhang | Eric M. Đufresne | Miaoqi Chu | Subramanian K.R.S. Sankaranarayanan | Wei Chen | Suresh Narayanan | Mathew J. Cherukara
Source: Nature Communications (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery. Relaxation (psychology) ; Dynamics (music) ; Spectroscopy ; Computer science ; Statistical physics ; Biological system ; Molecular dynamics ; Artificial intelligence ; Materials science ; Chemical physics ; Physics ; Chemistry ; Biology ; Computational chemistry Machine Learning in Materials Science ; Functional Brain Connectivity Studies ; Material Dynamics and Properties
Authors: Harshini Mohan | Subash Mohandoss | A.P. Gnana Prakash | B. Natarajan | L. Sivachandiran | Aymen Amine Assadi | Ahmed Khacef
Source: Inorganic Chemistry Communications (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Cold plasma assisted synthesis of spinel-CoFe2O4 nanoparticle with narrow bandgap and high magnetic activity Calcination ; Materials science ; Magnetization ; Crystallization ; Coercivity ; Nanoparticle ; Chemical engineering ; Analytical Chemistry (journal) ; Spinel ; Plasma ; Magnetic nanoparticles ; Magnetometer ; Particle size ; Nanotechnology ; Catalysis ; Chemistry ; Metallurgy ; Chromatography ; Magnetic field ; Condensed matter physics ; Organic chemistry Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Multiferroics and related materials
Authors: Grzegorz Sochacki | Xiaoping Zhang | Arsen Abdulali | Fumiya Iida
Source: Journal of Field Robotics (article, 2024-04-01)
Field: Materials Science — Biomaterials

Towards practical robotic chef: Review of relevant work and future challenges Abstract Robotic chefs are a promising technology that can improve the availability of quality food by reducing the time required for cooking, therefore decreasing food's overall cost. This paper clarifies and structures design and benchmarking rules in this new area of research, and provides a comprehensive review of technologies suitable for the construction of cooking robots. The diner is an ultimate judge of the cooking outcome, therefore we put focus on explaining human food preferences and perception of taste and ways to use them for control. Mechanical design of robotic chefs at a practically low cost remains the challenge, but some recently published gripper designs as well as whole robotic systems show the use of cheap materials or off‐the‐shelf components. Moreover, technologies like taste sensing, machine learning, and computer vision are making their way into robotic cooking enabling smart sensing and therefore improving controllability and autonomy. Furthermore, objective assessment of taste and food palatability is a challenge even for trained humans, therefore the paper provides a list of procedures for benchmarking the robot's tasting and cooking abilities. The paper is written from the point of view of a researcher or engineer building a practical robotic system, therefore there is a strong priority for solutions and technologies that are proven, robust and self‐contained enough to be a part of a larger system. Work (physics) ; Computer science ; Engineering ; Systems engineering ; Engineering ethics ; Management science ; Human–computer interaction ; Artificial intelligence ; Mechanical engineering Silk-based biomaterials and applications ; Social Robot Interaction and HRI ; Robot Manipulation and Learning
Authors: Bishwas Pokharel | Deep Shankar Pandey | Anjuli Sapkota | B.V. Ramnaresh Yadav | Vasanta Gurung | Mandira Pradhananga Adhikari | Lok Nath Regmi | Nanda Bikram Adhikari
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon Accurate measurement of the microspores, mesopores, and macropores on the surface of the activated carbon is essential due to its direct influence on the material’s adsorption capacity, surface area, and overall performance in various applications like water purification, air filtration, and gas separation. Traditionally, Scanning Electron Microscopy (SEM) images of activated carbons are collected and manually annotated by a human expert to differentiate and measure different pores in the surface. However, manual analysis of such surfaces is costly, time-consuming, and resource-intensive as requires supervision from experts. In this paper, we propose an automatic Deep-learning-based solution to address this challenge of activated carbon surface segmentation. We introduce a novel SEM Image segmentation dataset for activated carbon. We then evaluate the state-of-the-art deep learning models on the novel semantic segmentation task that shows promising results. Finally, we outline the key research challenges and discuss potential research directions to address these challenges. Computer science ; Artificial intelligence ; Segmentation ; Scanning electron microscope ; Image segmentation ; Computer vision ; Materials science ; Pattern recognition (psychology) ; Composite material Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science