jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liuhua Mu | Jie Jiang | Shiyu Gao | Xiaoyan Li | Shiqi Sheng
Source: International Journal of Molecular Sciences (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

A DFT Study of Band-Gap Tuning in 2D Black Phosphorus via Li+, Na+, Mg2+, and Ca2+ Ions Black phosphorus (BP) and its two-dimensional derivative (2D-BP) have garnered significant attention as promising anode materials for electrochemical energy storage devices, including next-generation fast-charging batteries. However, the interactions between BP and light metal ions, as well as how these interactions influence BP's electronic properties, remain poorly understood. Here, we employed density functional theory (DFT) to investigate the effects of monovalent (Li<sup>+</sup> and Na<sup>+</sup>) and divalent (Mg<sup>2+</sup> and Ca<sup>2+</sup>) ions on the valence electronic structure of 2D-BP. Molecular orbital analysis revealed that the adsorption of divalent cations can significantly reduce the band gap, suggesting an enhancement in charge transfer rates. In contrast, the adsorption of monovalent cations had minimal impact on the band gap, suggesting the preservation of 2D-BP's intrinsic electrical properties. Energetic and charge analyses indicated that the extent of charge transfer primarily governs the ability of ions to modulate 2D-BP's electronic structure, especially under high-pressure conditions where ions are in close proximity to the 2D-BP surface. Moreover, charge polarization calculations revealed that, compared with monovalent cations, divalent cations induced greater polarization, disrupting the symmetry of the pristine 2D-BP and further influencing its electronic characteristics. These findings provide a molecular-level understanding of how ion interactions influence 2D-BP's electronic properties during ion-intercalation processes, where ions are in close proximity to the 2D-BP surface. Moreover, the calculated diffusion barrier results revealed the potential of 2D-BP as an effective anode material for lithium-ion, sodium-ion, and magnesium-ion batteries, though its performance may be limited for calcium-ion batteries. By extending our understanding of interactions between ions and 2D-BP, this work contributes to the design of efficient and reliable energy storage technologies, particularly for the next-generation fast-charging batteries. Ion ; Density functional theory ; Chemistry ; Divalent ; Band gap ; Chemical physics ; Electronic structure ; Anode ; Intercalation (chemistry) ; HOMO/LUMO ; Inorganic chemistry ; Materials science ; Computational chemistry ; Molecule ; Physical chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Masaaki Mitsui | Yuki Miyoshi | Daichi Arima
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Tailoring sensitization properties and improving near-infrared photon upconversion performance through alloying in superatomic molecular Au<sub>25</sub> nanoclusters Noble-metal nanoclusters (NCs) protected by organic ligands have recently come to the forefront as potent triplet sensitizers for photon upconversion (UC) <i>via</i> triplet-triplet annihilation (TTA), owing to their capacity for atomic-level photophysical property customization. Among these, the rod-shaped bi-icosahedral [Au<sub>25</sub>(PPh<sub>3</sub>)<sub>10</sub>(S-C<sub>2</sub>H<sub>4</sub>Ph)<sub>5</sub>Cl<sub>2</sub>]<sup>2+</sup> (Au-rod) NC is a particularly iconic superatomic molecular NC, recently identified as a near-infrared (NIR)-absorbing sensitizer for TTA-UC. In this study, we synthesized Cu-doped NCs, [Au<sub>25-<i>x</i></sub>Cu<sub><i>x</i></sub>(PPh<sub>3</sub>)<sub>10</sub>(S-C<sub>2</sub>H<sub>4</sub>Ph)<sub>5</sub>Cl<sub>2</sub>]<sup>2+</sup> (AuCu-rod), and paired them with 9,10-bis(phenylethynyl)anthracene (BPEA) annihilator/emitter to explore the impact of Cu-doping on the triplet sensitization and NIR-UC performance. The triplet state of AuCu-rod, with lifetime of 3 μs, exhibited a modest blue shift compared to the Au-rod, resulting in the increment in the driving force for triplet energy transfer (TET) to the BPEA acceptor. The TET rate constant was determined to be 5.0 × 10<sup>7</sup> M<sup>-1</sup> s<sup>-1</sup>, which is an order of magnitude higher than the rate constant for the Au-rod/BPEA pair. This improvement has led to a remarkable increase in the TET efficiency. Notably, the AuCu-rod/BPEA pair facilitated the efficient UC of 805 nm NIR light into 510 nm visible light, realizing a large anti-Stokes shift close to 0.9 eV. The UC internal quantum yield of this combination was determined to be 2.33 ± 0.05%, marking a fivefold enhancement over the Au-rod sensitizer (0.49%). Thus, alloying NC sensitizers offers a promising route to enhance UC performance by tuning the triplet state energy and optimizing the compatibility between the sensitizer and annihilator. Additionally, in this series of experiments, the formation of small amounts of BPEA microaggregates was observed. These aggregates did not undergo singlet fission and could retain multiple long-lived triplet excitons. This characteristic facilitated TTA among triplet excitons, resulting in efficient NIR-to-visible UC emission. Nanoclusters ; Photon upconversion ; Materials science ; Infrared ; Sensitization ; Nanotechnology ; Optoelectronics ; Photon ; Optics ; Physics ; Doping Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Francis Berthias | Nurgül Bilgin | Jasmin Mecinović | Ole N. Jensen
Source: PROTEOMICS (article, 2024-01-28)
Field: Chemistry — Spectroscopy

Top‐down ion mobility/mass spectrometry reveals enzyme specificity: Separation and sequencing of isomeric proteoforms Enzymatic catalysis is one of the fundamental processes that drives the dynamic landscape of post-translational modifications (PTMs), expanding the structural and functional diversity of proteins. Here, we assessed enzyme specificity using a top-down ion mobility spectrometry (IMS) and tandem mass spectrometry (MS/MS) workflow. We successfully applied trapped IMS (TIMS) to investigate site-specific N-ε-acetylation of lysine residues of full-length histone H4 catalyzed by histone lysine acetyltransferase KAT8. We demonstrate that KAT8 exhibits a preference for N-ε-acetylation of residue K16, while also adding acetyl groups on residues K5 and K8 as the first degree of acetylation. Achieving TIMS resolving power values of up to 300, we fully separated mono-acetylated regioisomers (H4K5ac, H4K8ac, and H4K16ac). Each of these separated regioisomers produce unique MS/MS fragment ions, enabling estimation of their individual mobility distributions and the exact localization of the N-ε-acetylation sites. This study highlights the potential of top-down TIMS-MS/MS for conducting enzymatic assays at the intact protein level and, more generally, for separation and identification of intact isomeric proteoforms and precise PTM localization. Mass spectrometry ; Ion-mobility spectrometry ; Chemistry ; Chromatography ; Top-down proteomics ; Enzyme ; Ion ; Tandem mass spectrometry ; Selected reaction monitoring ; Biochemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Shuyi Jia | Chao Zhang | Victor S.C. Fung
Source: arXiv (Cornell University) (preprint, 2024-06-19)
Field: Materials Science — Materials Chemistry

LLMatDesign: Autonomous Materials Discovery with Large Language Models Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials, but these methods still depend heavily on very large quantities of training data and often lack the flexibility and chemical understanding often desired in materials discovery. We introduce LLMatDesign, a novel language-based framework for interpretable materials design powered by large language models (LLMs). LLMatDesign utilizes LLM agents to translate human instructions, apply modifications to materials, and evaluate outcomes using provided tools. By incorporating self-reflection on its previous decisions, LLMatDesign adapts rapidly to new tasks and conditions in a zero-shot manner. A systematic evaluation of LLMatDesign on several materials design tasks, in silico, validates LLMatDesign's effectiveness in developing new materials with user-defined target properties in the small data regime. Our framework demonstrates the remarkable potential of autonomous LLM-guided materials discovery in the computational setting and towards self-driving laboratories in the future. Computer science ; Natural language processing Machine Learning in Materials Science
Authors: Boris N. Slautin | Yongtao Liu | Hiroshi Funakubo | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: ACS Nano (preprint, 2024-08-26)
Field: Materials Science — Materials Chemistry

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Here, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems. Context (archaeology) ; Computer science ; Approximate Bayesian computation ; Bayesian probability ; Bayesian inference ; Object (grammar) ; Data science ; Theoretical computer science ; Artificial intelligence ; Inference Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Kévin Alhada–Lahbabi | Damien Deleruyelle | Brice Gautier
Source: npj Computational Materials (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching Phase-field modeling offers a powerful tool for investigating the electrical control of the domain structure in ferroelectrics. However, its broad application is constrained by demanding computational requirements, limiting its utility in inverse design scenarios. Here, we introduce a machine-learning surrogate to accelerate 3D phase-field modeling of tip-induced electrical switching. By dynamically handling the boundary conditions, the surrogate achieves accurate reproduction of switching trajectories under various tip locations and applied voltages. With stable predictions throughout entire morphological evolution pathways and a relative error inferior to 10% compared to direct solvers, the model efficiently emulates intricate switching sequences. By successfully replicating the boundary conditions, the presented framework strides towards a holistic surrogate for the ferroelectric phase field. With up to 2500-fold speed-ups over classical methods, our approach opens the path for the tractable design of the domain structure and the resolution of realistic inverse problems. Ferroelectricity ; Materials science ; Field (mathematics) ; Phase (matter) ; Computer science ; Optoelectronics ; Physics ; Mathematics ; Dielectric Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Solidification and crystal growth phenomena
Authors: D.B. Miracle | Dan J. Thoma
Source: Current Opinion in Solid State and Materials Science (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Autonomous research and development of structural materials – An introduction and vision Vision science ; Cognitive science ; Development (topology) ; Engineering ethics ; Computer science ; Artificial intelligence ; Psychology ; Engineering ; Mathematics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Advanced ceramic materials synthesis
Authors: F. Adams | Austin McDannald | Ichiro Takeuchi | A. Gilad Kusne
Source: Matter (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

Human-in-the-loop for Bayesian autonomous materials phase mapping Loop (graph theory) ; Human-in-the-loop ; Bayesian probability ; Phase (matter) ; Computer science ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Medical Imaging Techniques and Applications
Authors: Yasemen Kuddusi | Maarten R. Dobbelaere | Kevin M. Van Geem | Andreas Züttel
Source: Catalysis Science & Technology (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accelerated design of nickel-cobalt based catalysts for CO<sub>2</sub> hydrogenation with human-in-the-loop active machine learning Thermo-catalytic conversion of CO<sub>2</sub> into more valuable compounds, such as methane, is an attractive strategy for energy storage in chemical bonds and creating a carbon-based circular economy. However, designing heterogeneous catalysts remains a challenging, time- and resource-consuming task. Herein, we present an interpretable, human-in-the-loop active machine learning framework to efficiently plan catalytic experiments, execute them in an automated set-up, and estimate the effect of experimental variables on the catalytic activity. A dataset with 48 catalytic activity tests was compiled from a design space of Ni-Co/Al<sub>2</sub>O<sub>3</sub> catalysts with over 50 million potential combinations in only eight iterations. This small dataset was found sufficient to predict CO<sub>2</sub> conversion, methane selectivity, and methane space-time yield with remarkable accuracy (<i>R</i> <sup>2</sup> > 0.9) for untested catalysts and reaction conditions. New experiments and catalysts were selected with this methodology, leading to experimental conditions that improved the methane space-time yield by nearly 50% in comparison to the previously obtained maximum in the dataset. Interpretation of the model predictions unveiled the effect of each catalyst descriptor and reaction condition on the outcome. Particularly, the strong predicted inverse trend between the calcination temperature and the catalytic activity was validated experimentally, and characterization implied an underlying structure-performance relationship. Finally, it is demonstrated that the deployed active learning model is excellently suited to predict and fit kinetic trends with a minimal amount of data. This data-driven framework is a first step to faster, model-based, and interpretable design of catalysts and holds promise for broader applications across catalytic processes. Cobalt ; Catalysis ; Nickel ; Loop (graph theory) ; Chemistry ; Materials science ; Inorganic chemistry ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysts for Methane Reforming
Authors: Fuhao Ji | Auralee Edelen | Ryan Roussel | Xiaozhe Shen | Sara Miskovich | Stephen Weathersby | Duan Luo | Mianzhen Mo | Patrick Krämer | Christopher Mayes | Mohamed A. K. Othman | Emilio A. Nanni | Xijie Wang | Alexander H. Reid | Michael P. Minitti | R. J. England
Source: Nature Communications (preprint, 2024-06-03)
Field: Materials Science — Surfaces, Coatings and Films

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems. Bayesian probability ; Ultrashort pulse ; Electron ; Diffraction ; Ultrafast electron diffraction ; Electron diffraction ; Computer science ; Physics ; Artificial intelligence ; Nuclear physics ; Optics ; Laser Electron and X-Ray Spectroscopy Techniques
Authors: Li Chuan-Nan | Liang Han-Pu | Zhao Bai-Qing | Wei Su-Huai | Xie Zhang
Source: Journal of Materials Informatics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted crystal structure prediction made simple Crystal structure prediction (CSP) plays a crucial role in condensed matter physics and materials science, with its importance evident not only in theoretical research but also in the discovery of new materials and the advancement of novel technologies. However, due to the diversity and complexity of crystal structures, trial-and-error experimental synthesis is time-consuming, labor-intensive, and insufficient to meet the increasing demand for new materials. In recent years, machine learning (ML) methods have significantly boosted CSP. In this review, we present a comprehensive review of the ML models applied in CSP. We first introduce the general steps for CSP and highlight the bottlenecks in conventional CSP methods. We further discuss the representation of crystal structures and illustrate how ML-assisted CSP works. In particular, we review the applications of graph neural networks (GNNs) and ML force fields in CSP, which have been demonstrated to significantly speed up structure search and optimization. In addition, we provide an overview of advanced generative models in CSP, including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models. Finally, we discuss the remaining challenges in ML-assisted CSP. Simple (philosophy) ; Artificial intelligence ; Crystal structure prediction ; Computer science ; Machine learning ; Materials science ; Crystal structure ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yuxuan Wang | Ross D. King
Source: Machine Learning (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Extrapolation is not the same as interpolation Abstract We propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): f (drug) $$\rightarrow$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> activity. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based on learning a bivariate function that predicts the difference in activities of two drugs F (drug1, drug2) $$\rightarrow$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> difference in activity, followed by the use of ranking algorithms. This formulation is general and agnostic, suitable for finding samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines , random forests , and Gradient Boosting Machines . We compared the formulation with standard regression on thousands of drug design datasets, gene expression datasets and material property datasets. The test set extrapolation metric was the identification of examples with greater values than the training set, and top-performing examples (within the top 10% of the whole dataset). On this metric our pairwise formulation vastly outperformed standard regression. Its proposed variations also showed a consistent outperformance. Its application in the stock selection problem further confirmed the advantage of this pairwise formulation. Extrapolation ; Interpolation (computer graphics) ; Mathematics ; Computer science ; Applied mathematics ; Artificial intelligence ; Algorithm ; Statistics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Model Reduction and Neural Networks
Authors: Venkat Venkatasubramanian
Source: AIChE Journal (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Do large language models “understand” their knowledge? Abstract Large language models (LLMs) are often criticized for lacking true “understanding” and the ability to “reason” with their knowledge, being seen merely as autocomplete engines. I suggest that this assessment might be missing a nuanced insight. LLMs do develop a kind of empirical “understanding” that is “geometry”‐like, which is adequate for many applications. However, this “geometric” understanding, built from incomplete and noisy data, makes them unreliable, difficult to generalize, and lacking in inference capabilities and explanations. To overcome these limitations, LLMs should be integrated with an “algebraic” representation of knowledge that includes symbolic AI elements used in expert systems. This integration aims to create large knowledge models (LKMs) grounded in first principles that can reason and explain, mimicking human expert capabilities. Furthermore, we need a conceptual breakthrough, such as the transformation from Newtonian mechanics to statistical mechanics, to create a new science of LLMs. Inference ; Computer science ; Representation (politics) ; Knowledge representation and reasoning ; Epistemology ; Data science ; Cognitive science ; Management science ; Artificial intelligence ; Psychology ; Engineering ; Politics ; Political science Machine Learning in Materials Science ; Topic Modeling ; Machine Learning in Bioinformatics