jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takashi Inagaki | Akari Kato | Koichi Takahashi | Haruka Ozaki | Genki N. Kanda
Source: arXiv (Cornell University) (preprint, 2023-04-18)
Field: Materials Science — Materials Chemistry

LLMs can generate robotic scripts from goal-oriented instructions in biological laboratory automation The use of laboratory automation by all researchers may substantially accelerate scientific activities by humans, including those in the life sciences. However, computer programs to operate robots should be written to implement laboratory automation, which requires technical knowledge and skills that may not be part of a researcher's training or expertise. In the last few years, there has been remarkable development in large language models (LLMs) such as GPT-4, which can generate computer codes based on natural language instructions. In this study, we used LLMs, including GPT-4, to generate scripts for robot operations in biological experiments based on ambiguous instructions. GPT-4 successfully generates scripts for OT-2, an automated liquid-handling robot, from simple instructions in natural language without specifying the robotic actions. Conventionally, translating the nuances of biological experiments into low-level robot actions requires researchers to understand both biology and robotics, imagine robot actions, and write robotic scripts. Our results showed that GPT-4 can connect the context of biological experiments with robot operation through simple prompts with expert-level contextual understanding and inherent knowledge. Replacing robot script programming, which is a tedious task for biological researchers, with natural-language LLM instructions that do not consider robot behavior significantly increases the number of researchers who can benefit from automating biological experiments. Scripting language ; Robot ; Automation ; Computer science ; Human–computer interaction ; Context (archaeology) ; Robotics ; Artificial intelligence ; Laboratory automation ; Natural language ; Software engineering ; Multimedia ; Programming language ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Machine Learning in Bioinformatics
Authors: Zhen Li | He Gao | Longfei Zhang | Shouming Wang | Ji Liu | Shengtao Li
Source: High Voltage (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

The mechanisms of enhanced thickness‐dependent DC breakdown strength of EP/MWCNTs nanocomposites Abstract Owing to advanced power electronic and electrical applications developing towards miniaturisation and integration, the thickness‐dependent DC breakdown mechanism of epoxy/multiwall‐carbon‐nanotube nanocomposites is under investigation. The results indicate that the breakdown strength of nanocomposites containing 0.05 wt% multiwall‐carbon‐nanotubes rises by ∼18%, and the breakdown strength falls exponentially with increasing thickness. To clarify the microscopic mechanism, a simulation model of DC breakdown, including carriers transport and segmental dynamics, is developed, and the accordant simulation results with experimental results indicate that the thickness‐dependent DC breakdown of epoxy/multiwall‐carbon‐nanotube nanocomposites is determined both by segment chain dynamics and charge transport. According to the breakdown model analysis, the effects of multiwall‐carbon‐nanotube on enhanced breakdown strength are caused by the increased amount of deep traps in the interfacial region, while the influence of thickness is attributed to the enlarged segment chain displacement and electric field distortion as the voltage raising time increases. Carbon nanotube ; Materials science ; Nanocomposite ; Composite material ; Epoxy ; Electrical breakdown ; Breakdown voltage ; Voltage ; Optoelectronics ; Electrical engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Nikita Fedik | Benjamin Nebgen | Nicholas Lubbers | Kipton Barros | Maksim Kulichenko | Ying Wai Li | R.I. Zubatyuk | Richard A. Messerly | Olexandr Isayev | Sergei Tretiak
Source: The Journal of Chemical Physics (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Synergy of semiempirical models and machine learning in computational chemistry Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency. Leverage (statistics) ; Computer science ; Machine learning ; Artificial intelligence ; Locality ; Ab initio ; Heuristics ; Data science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ruben Staub | Philippe Gantzer | Yu Harabuchi | Satoshi Maeda | Alexandre Varnek
Source: Molecules (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case Ab initio kinetic studies are important to understand and design novel chemical reactions. While the Artificial Force Induced Reaction (AFIR) method provides a convenient and efficient framework for kinetic studies, accurate explorations of reaction path networks incur high computational costs. In this article, we are investigating the applicability of Neural Network Potentials (NNP) to accelerate such studies. For this purpose, we are reporting a novel theoretical study of ethylene hydrogenation with a transition metal complex inspired by Wilkinson's catalyst, using the AFIR method. The resulting reaction path network was analyzed by the Generative Topographic Mapping method. The network's geometries were then used to train a state-of-the-art NNP model, to replace expensive ab initio calculations with fast NNP predictions during the search. This procedure was applied to run the first NNP-powered reaction path network exploration using the AFIR method. We discovered that such explorations are particularly challenging for general purpose NNP models, and we identified the underlying limitations. In addition, we are proposing to overcome these challenges by complementing NNP models with fast semiempirical predictions. The proposed solution offers a generally applicable framework, laying the foundations to further accelerate ab initio kinetic studies with Machine Learning Force Fields, and ultimately explore larger systems that are currently inaccessible. Ab initio ; Computer science ; Path (computing) ; Artificial neural network ; Generative grammar ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kohei Yoshida | Yusuke Kanematsu | David S. Rivera Rocabado | Takayoshi Ishimoto
Source: Computational Materials Science (article, 2023-02-15)
Field: Materials Science — Polymers and Plastics

Modelling the dynamic physical properties of vulcanised polymer models by molecular dynamics simulations and machine learning Polymer ; Natural rubber ; Molecular dynamics ; Materials science ; Biological system ; Mechanism (biology) ; Monomer ; Computer science ; Composite material ; Chemistry ; Computational chemistry ; Physics Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies
Authors: Nicola Corriero | Rosanna Rizzi | Gaetano Settembre | Nicoletta Del Buono | Domenico Diacono
Source: Journal of Applied Crystallography (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

<i>CrystalMELA</i>: a new crystallographic machine learning platform for crystal system determination Determination of the crystal system and space group is the first step of crystal structure analysis. Often this turns out to be a bottleneck in the material characterization workflow for polycrystalline compounds, thus requiring manual interventions. This work proposes a new machine-learning (ML)-based web platform, <i>CrystalMELA</i> (Crystallography MachinE LeArning), for crystal systems classification. Two different ML models, random forest and convolutional neural network, are available through the platform, as well as the extremely randomized trees algorithm, available from the literature. The ML models learned from simulated powder X-ray diffraction patterns of more than 280 000 published crystal structures from organic, inorganic and metal-organic compounds and minerals which were collected from the POW_COD database. A crystal system classification accuracy of 70%, which improved to more than 90% when considering the Top-2 classification accuracy, was obtained in tenfold cross-validation. The validity of the trained models has also been tested against independent experimental data of published compounds. The classification options in the <i>CrystalMELA</i> platform are powerful, easy to use and supported by a user-friendly graphic interface. They can be extended over time with contributions from the community. The tool is freely available at https://www.ba.ic.cnr.it/softwareic/crystalmela/ following registration. Crystallography ; Crystal (programming language) ; Materials science ; Computer science ; Chemistry ; Programming language X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Anif Jamaluddin | Dewanto Harjunowibowo | Sri Budiawanti | Nughthoh Arfawi Kurdhi | SUTARSIS sutarsis | Daphne Teck Ching Lai | S. Ramesh
Source: Energy Reports (article, 2023-10-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Applying machine learning to understand the properties of biomass carbon materials in supercapacitors Carbon is a fundamental material in developing electrochemical double-layer capacitors (EDLCs), also known as supercapacitors. Many studies have proven the impact of various carbon material properties, such as surface area, pore volume, and chemical surface composition, on the specific capacitance of supercapacitors (EDLCs). However, research endeavors to comprehensively evaluate the contribution of these material properties in correlation with experimental parameters, such as electrolyte concentration, voltage window, and current density, are scarce. This study aimed to employ machine learning algorithms to comprehend the interdependence between the properties of biomass-based carbon and the aforementioned experimental parameters with the capacitance of EDLCs. Four models of the machine learning algorithms were utilized in this study, including linear regression (LR), M5-Rules, Random Tree (RT), and Multi-Layer Perceptron (MLP), to determine the most suitable algorithm for analyzing and predicting the capacitance of EDLCs. The results revealed that the MLP model exhibited the highest determination correlation coefficient (R) of 0.871 with a Mean Absolute Error (MAE) of 45.069 F/g. Besides, the study utilized a machine learning correlation attribute model and observed that the supercapacitor’s surface area and pore volume demonstrated significant correlations with the same correlation ratio of 0.4. In conclusion, these findings emphasize the importance of considering surface area and pore volume in developing and optimizing supercapacitors. Finally, this study adds knowledge in supercapacitors and provides valuable insights for designing and developing high-performance energy storage devices. Supercapacitor ; Capacitance ; Materials science ; Carbon fibers ; Correlation coefficient ; Perceptron ; Electrolyte ; Volume (thermodynamics) ; Computer science ; Biological system ; Machine learning ; Artificial neural network ; Composite material ; Thermodynamics ; Electrode ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Fuel Cells and Related Materials
Authors: Abhishek Saxena | Amrinder Mehta | Hitesh Vasudev | Gaurav Prashar | Nisreen Yasir Jasim
Source: Materials Today Proceedings (review, 2023-08-01)
Field: Materials Science — Materials Chemistry

A short review on machine learning for the purpose of optimizing and predicting the properties of polymeric nanocomposites Nanocomposite ; Materials science ; Nanomaterials ; Carbon nanotube ; Toughness ; Composite material ; Polymer ; Aerogel ; Nanotechnology ; Characterization (materials science) Carbon Nanotubes in Composites ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Gui Chen | Zhihao Zhang | Zongyi Li | Chen Luo | Jiang Xia | Xing Wu | Junhao Chu
Source: iScience (review, 2023-09-21)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials Defects are prevalent in two-dimensional (2D) materials due to thermal equilibrium and processing kinetics. The presence of various defect types can affect material properties significantly. With the development of the advanced transmission electron microscopy (TEM), the property-related structures could be investigated in multiple dimensions. It produces TEM datasets containing a large amount of information. Traditional data analysis is influenced by the subjectivity of researchers, and manual analysis is inefficient and imprecise. Recent developments in deep learning provide robust methods for the quantitative identification of defects in 2D materials efficiently and precisely. Taking advantage of big data, it breaks the limitations of TEM as a local characterization tool, making TEM an intelligent macroscopic analysis method. In this review, the recent developments in the TEM data analysis of defects in 2D materials using deep learning technology are summarized. Initially, an in-depth examination of the distinctions between TEM and natural images is presented. Subsequently, a comprehensive exploration of TEM data analysis ensues, encompassing denoising, point defects, line defects, planar defects, quantitative analysis, and applications. Furthermore, an exhaustive assessment of the significant obstacles encountered in the accurate identification of distinct structures is also provided. Characterization (materials science) ; Computer science ; Transmission electron microscopy ; Identification (biology) ; Nanotechnology ; Materials science ; Crystallographic defect ; Artificial intelligence ; Chemistry ; Crystallography Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Balaranjan Selvaratnam | Anton O. Oliynyk | Arthur Mar
Source: Inorganic Chemistry (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning in Solid-State Chemistry, with Applications to Perovskites, Spinels, and Rare-Earth Intermetallics: Finding Descriptors Using Decision Trees Machine-learning methods have exciting potential to aid materials discovery, but their wider adoption can be hindered by the opaqueness of many models. Even if these models are accurate, the inability to understand the basis for the predictions breeds skepticism. Thus, it is imperative to develop machine-learning models that are explainable and interpretable so that researchers can judge for themselves if the predictions are consistent with their own scientific understanding and chemical insight. In this spirit, the sure independence screening and sparsifying operator (SISSO) method was recently proposed as an effective way to identify the simplest combination of chemical descriptors needed to solve classification and regression problems in materials science. This approach uses domain overlap (DO) as the criterion to find the most informative descriptors in classification problems, but sometimes a low score can be assigned to useful descriptors when there are outliers or when samples belonging to a class are clustered in different regions of the feature space. Here, we present a hypothesis that the performance can be improved by implementing decision trees (DT) instead of DO as the scoring function to find the best descriptors. This modified approach was tested on three important structural classification problems in solid-state chemistry: perovskites, spinels, and rare-earth intermetallics. In all cases, the DT scoring gave better features and significantly improved accuracies of ≥0.91 for the training sets and ≥0.86 for the test sets. Artificial intelligence ; Machine learning ; Molecular descriptor ; Outlier ; Chemistry ; Decision tree ; Feature (linguistics) ; Class (philosophy) ; Intermetallic ; Computer science ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Shisheng Zheng | Haowen Ding | Shunning Li | Dong Chen | Feng Pan
Source: Chinese Journal of Structural Chemistry (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Application of topology-based structure features for machine learning in materials science Chemistry ; Topology (electrical circuits) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yosef Knattrup | Jakub Kubečka | Daniel Ayoubi | Jonas Elm
Source: ACS Omega (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications Formation and growth of atmospheric molecular clusters into aerosol particles impact the global climate and contribute to the high uncertainty in modern climate models. Cluster formation is usually studied using quantum chemical methods, which quickly becomes computationally expensive when system sizes grow. In this work, we present a large database of ∼250k atmospheric relevant cluster structures, which can be applied for developing machine learning (ML) models. The database is used to train the ML model kernel ridge regression (KRR) with the FCHL19 representation. We test the ability of the model to extrapolate from smaller clusters to larger clusters, between different molecules, between equilibrium structures and out-of-equilibrium structures, and the transferability onto systems with new interactions. We show that KRR models can extrapolate to larger sizes and transfer acid and base interactions with mean absolute errors below 1 kcal/mol. We suggest introducing an iterative ML step in configurational sampling processes, which can reduce the computational expense. Such an approach would allow us to study significantly more cluster systems at higher accuracy than previously possible and thereby allow us to cover a much larger part of relevant atmospheric compounds. Cluster (spacecraft) ; Computer science ; Kernel (algebra) ; Representation (politics) ; Set (abstract data type) ; Ridge ; Mathematics Machine Learning in Materials Science ; Atmospheric chemistry and aerosols ; Air Quality Monitoring and Forecasting
Authors: Vincenzo Barone | Marco Fusè | Raúl Aguado | Simone Potenti | Íker León | Elena R. Alonso | Santiago Mata | Federico Lazzari | Giordano Mancini | Lorenzo Spada | Andrea Gualandi | Pier Giorgio Cozzi | Cristina Puzzarini | José L. Alonso
Source: Chemistry - A European Journal (article, 2023-02-03)
Field: Chemistry — Spectroscopy

Bringing Machine‐Learning Enhanced Quantum Chemistry and Microwave Spectroscopy to Conformational Landscape Exploration: the Paradigmatic Case of 4‐Fluoro‐Threonine A combined experimental and theoretical study has been carried out on 4-fluoro-threonine, the only naturally occurring fluorinated amino acid. Fluorination of the methyl group significantly increases the conformational complexity with respect to the parent amino acid threonine. The conformational landscape has been characterized in great detail, with special attention given to the inter-conversion pathways between different conformers. This led to the identification of 13 stable low-energy minima. The equilibrium population of so many conformers produces a very complicated and congested rotational spectrum that could be assigned through a strategy that combines several levels of quantum chemical calculations with the principles of machine learning. Twelve conformers out of 13 could be experimentally characterized. The results obtained from the analysis of the intra-molecular interactions can be exploited to accurately model fluorine-substitution effects in biomolecules. Conformational isomerism ; Biomolecule ; Maxima and minima ; Energy landscape ; Chemistry ; Computational chemistry ; Quantum chemistry ; Threonine ; Quantum ; Quantum chemical ; Population ; Rotational spectroscopy ; Chemical physics ; Molecule ; Physics ; Organic chemistry ; Quantum mechanics ; Mathematics ; Biochemistry Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Fluorine in Organic Chemistry
Authors: Weiqun Gao | Weizhong Zheng | Kexin Yan | Weizhen Sun | Ling Zhao
Source: Fuel (article, 2023-06-03)
Field: Chemistry — Inorganic Chemistry

Accelerating the discovery of acid gas-selective MOFs for natural gas purification: A combination of machine learning and molecular fingerprint Adsorption ; Fingerprint (computing) ; Protocol (science) ; Computer science ; Work (physics) ; Gas separation ; Metal-organic framework ; Rational design ; Natural gas ; Molecule ; Metric (unit) ; Chemistry ; Materials science ; Nanotechnology ; Artificial intelligence ; Organic chemistry ; Thermodynamics ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Carbon Dioxide Capture Technologies