jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Belkacem Kada | Khalid A. Juhany | Ibraheem AlQadi | Mostefa Bourchak
Source: International Journal of Advanced Computer Science and Applications (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Design of an Advanced Distributed Adaptive Control for Multi-SMA Actuators Aerospace applications place high demands on designing Shape Memory Alloy (SMA) actuators, including accuracy, dependability, high-performance criteria, and cooperative activation. Because of their portability, durability, and performance under extreme conditions, SMAs have found a home in the aerospace industry as single and array actuators. This paper presents the development of a control scheme for thermally activating rotary SMA actuators as single and cooperative actuators. The control scheme is a hybrid adaptive robust control abbreviated as HARC. The immersion and invariance adaptive (I&I adaptive) and L2-gain control frameworks are utilized in developing the HARC approach. To create stable transient responses despite parametric and non-parametric errors, recursive backstepping is utilized for asymptotic stability. At the same time, L2-gain control is applied to ensure the global stability of the transient closed-loop system. Both techniques are used in conjunction with one another. In contrast to the conventional I&I, the robust control law can be developed without needing a target system or the solution of PDEs to satisfy the I&I condition. The parametric uncertainty is estimated with the help of an adaptive rule, and the non-parametric uncertainty brought on by the phase change of the SMA material and modeling mistakes is accounted for with the help of asymptotic nonlinear functions. The designed HARC is then extended to cover the actuation of multi-SMA or array actuators to respond to the increasing demand for cooperative controllers using distributed control protocols. It has been demonstrated through simulation testing on a rotational NiTi SMA actuator that the suggested control approach is both practical and resilient. SMA* ; Computer science ; Actuator ; Control theory (sociology) ; Backstepping ; Parametric statistics ; Adaptive control ; Robustness (evolution) ; Transient (computer programming) ; Control engineering ; Control (management) ; Engineering ; Artificial intelligence ; Mathematics ; Algorithm Shape Memory Alloy Transformations
Authors: Noah Crum | Leanto Sunny | Pooya Ronagh | Raymond Laflamme | Radhakrishnan Balu | George Siopsis
Source: arXiv (Cornell University) (preprint, 2023-05-13)
Field: Materials Science — Materials Chemistry

Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI Motivated by applications of quantum computers in Gibbs sampling from continuous real-valued functions, we ask whether such algorithms can provide practical advantages for machine learning models trained on classical data and seek measures for quantifying such impacts. In this study, we focus on deep energy-based models (EBM), as they require continuous-domain Gibbs sampling both during training and inference. In lieu of fault-tolerant quantum computers that can execute quantum Gibbs sampling algorithms, we use the Monte Carlo simulation of diffusion processes as a classical alternative. More specifically, we investigate whether long-run persistent chain Monte Carlo simulation of Langevin dynamics improves the quality of the representations achieved by EBMs. We consider a scheme in which the Monte Carlo simulation of a diffusion, whose drift is given by the gradient of the energy function, is used to improve the adversarial robustness and calibration score of an independent classifier network. Our results show that increasing the computational budget of Gibbs sampling in persistent contrastive divergence improves both the calibration and adversarial robustness of the model, suggesting a prospective avenue of quantum advantage for generative AI using future large-scale quantum computers. Computer science ; Robustness (evolution) ; Monte Carlo method ; Gibbs sampling ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Statistics ; Bayesian probability Machine Learning in Materials Science ; Theoretical and Computational Physics ; Adversarial Robustness in Machine Learning
Authors: Md Emran Ahmmed
Source: International Journal For Multidisciplinary Research (article, 2023-11-24)
Field: Materials Science — Polymers and Plastics

Artificial Intelligence (AI) In Garment Design: Opportunities And Challenges In The Ready-made Garments (RMG) Sector of Bangladesh This study looks at the pros and cons of using artificial intelligence (AI) in planning ready-made garments in Bangladesh's Gazipur district, where the garment industry is booming. Twenty designers from different institutions using AI and traditional software tools to create attires were involved in the study. The results show that applying AI to the field comes with both opportunities and challenges. AI has made garment creation more efficient, less expensive, more productive, more customizable, better at predicting trends, and more sustainable. However, it raises issues such as job loss, quality control, data privacy, dependence on technology, and ethical questions. This study shows how important it is to take a balanced approach to adopting AI, considering its possible benefits while addressing its risks, so that Bangladesh's ready-made garment industry can continue to grow and thrive. Clothing ; Quality (philosophy) ; Field (mathematics) ; Marketing ; Engineering ; Business ; Political science ; Law Textile materials and evaluations
Authors: Natasha Videcrantz Faurschou | Rolf H. Taaning | Christian Pedersen
Source: ChemRxiv (preprint, 2023-02-23)
Field: Chemistry — Organic Chemistry

Substrate Specific Closed-loop Optimization of Carbohydrate Protective Group Chemistry Using Bayesian Optimization and Transfer Learning A new way of performing reaction optimization within carbohydrate chemistry is presented. This is done by performing closed-loop optimization of regioselective benzoylation of unprotected glycosides using Bayesian optimization. Both 6-O- monobenzoylations and 3,6-O-dibenzoylations of three different monosaccharides are optimized. A novel transfer learning approach, where data from previous optimizations of different substrates is used to speed up the optimizations, has also been developed. The optimal conditions found by the Bayesian Optimization algorithm provide new insight into substrate specificity, as the conditions found are significantly different. In most cases, the optimal conditions include Et3N and benzoic anhydride, a new reagent combination for these reactions, discovered by the algorithm, demonstrating the power of this concept to widen the chemical space. Further, the developed procedures include ambient conditions and short reaction times. Bayesian optimization ; Reagent ; Chemistry ; Bayesian probability ; Monosaccharide ; Computer science ; Regioselectivity ; Substrate (aquarium) ; Carbohydrate chemistry ; Transfer of learning ; Bayesian inference ; Chemical space ; Combinatorial chemistry ; Organic chemistry ; Artificial intelligence ; Catalysis ; Drug discovery Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Microbial Metabolic Engineering and Bioproduction
Authors: Craig A. Bridges | Bishnu P. Thapaliya | Xiao‐Guang Sun | Anees Al‐Najjar | Nageswara S. V. Rao | Andrzej Nycz | Alex Walters | Luke Meyer | Sheng Dai
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Chemistry — Electrochemistry

(Invited) Electrochemistry in a Lab of the Future Advanced research laboratories are producing ever more data and introducing more advanced methods of data analysis and feedback to control these laboratories. These "labs of the future" are intended to revolutionize the way that research is done, enabling in some cases the exploration of higher dimensional problems and better research outcomes. At ORNL, the Autonomous Chemistry Laboratory (ACL) is being developed as a self-driving laboratory to accelerate materials discovery and innovation. A combination of robotic tools are being integrated with computational tools to analyze reaction results and predict optimal directions for future investigation. We have developed an electrochemical workstation integrated with materials synthesis and discovery. The capabilities and concept for the ACL will be presented. Electrochemistry ; Computer science ; Chemistry ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Various Chemistry Research Topics
Authors: Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-07-08)
Field: Materials Science — Materials Chemistry

Impact of noise on inverse design: The case of NMR spectra matching Despite its fundamental importance and widespread use for assessing reaction success in organic chemistry, deducing chemical structures from nuclear magnetic resonance (NMR) measurements has remained largely manual and time consuming. To keep up with the accelerated pace of automated synthesis in self driving laboratory settings, robust computational algorithms are needed to rapidly perform structure elucidations. We analyse the effectiveness of solving the NMR spectra matching task encountered in this inverse structure elucidation problem by systematically constraining the chemical search space, and correspondingly reducing the ambiguity of the matching task. Numerical evidence collected for the twenty most common stoichiometries in the QM9-NMR data base indicate systematic trends of more permissible machine learning prediction errors in constrained search spaces. Results suggest that compounds with multiple heteroatoms are harder to characterize than others. Extending QM9 by $\sim$10 times more constitutional isomers with 3D structures generated by Surge, ETKDG and CREST, we used ML models of chemical shifts trained on the QM9-NMR data to test the spectra matching algorithms. Combining both $^{13}\mathrm{C}$ and $^{1}\mathrm{H}$ shifts in the matching process suggests twice as permissible machine learning prediction errors than for matching based on $^{13}\mathrm{C}$ shifts alone. Performance curves demonstrate that reducing ambiguity and search space can decrease machine learning training data needs by orders of magnitude. Matching (statistics) ; Inverse ; Chemical shift ; Algorithm ; Computer science ; Ambiguity ; Artificial intelligence ; Machine learning ; NMR spectra database ; Chemical space ; Task (project management) ; Spectral line ; Mathematics ; Nuclear magnetic resonance ; Chemistry ; Physics ; Geometry ; Quantum mechanics ; Statistics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: F. Adams | Austin McDannald | Ichiro Takeuchi | A. Gilad Kusne
Source: SSRN Electronic Journal (preprint, 2023-01-01)
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 ; Control theory (sociology) ; Mathematics ; Physics ; Control (management) Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Electron and X-Ray Spectroscopy Techniques
Authors: Ryan Jacobs | Philip E. Goins | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Role of Multifidelity Data in Sequential Active Learning Materials Discovery Campaigns: Case Study of Electronic Bandgap Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g., time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together in what we here call multifidelity data the optimal approaches to their utilization are not established. It is therefore important to develop strategies to acquire and use multifidelity data to realize the most efficient iterative materials exploration. In this work, we assess the impact of using multifidelity data through mock demonstration of designing solar cell materials, using the electronic bandgap as the target property. We propose a new approach of using multifidelity data through leveraging machine learning models of both low- and high-fidelity data, where using predicted low-fidelity data as an input feature in the high-fidelity model can improve the impact of a multifidelity data approach. We show how tradeoffs of low- versus high-fidelity measurement cost and acquisition can impact the materials discovery process, and find that the use of multifidelity data has maximal impact on the materials discovery campaign when approximately five low-fidelity measurements per high-fidelity measurement are performed, and when the cost of low-fidelity measurements is approximately 5% or less than that of high-fidelity measurements. This work provides practical guidance and useful qualitative measures for improving materials discovery campaigns that involve multifidelity data. Fidelity ; Computer science ; High fidelity ; Feature (linguistics) ; Process (computing) ; Machine learning ; Data mining ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Software Engineering Research ; Big Data and Business Intelligence
Authors: Susumu Fujii | Yuta Shimizu | Junji Hyodo | Akihide Kuwabara | Yoshihiro Yamazaki
Source: ChemRxiv (preprint, 2023-04-10)
Field: Materials Science — Materials Chemistry

Synthesizable materials discovery via interpretable, physics-informed machine learning models The critical roles of computations and machine learning in accelerating materials discovery have become increasingly recognized, particularly in predicting and interpreting the synthesizability and functionality of new materials. Here, we develop a synthesizable materials discovery scheme using interpretable, physics-informed models. Our approach is based on an integration of high-throughput computations that capture the essence of materials properties, including the impact of point defects, and explainable machine learning models. These models provide quantitative predictions and interpretations of the materials’ synthesizability and functionality based on structural and chemical descriptors in a vast compositional space. Applying this scheme to proton-conducting cubic inorganic electrolytes for fuel cells, two unconventional materials for proton-conducting electrolytes, Pb-doped Bi12SiO20 and Sr-doped Bi4Ge3O12, were discovered in the first two synthesis trials. This scheme effectively bridges the existing gap between computational predictions, human interpretations, and experimental feasibility in machine learning, offering new insights into materials discovery and accelerating the development of new functional materials. Computation ; Computer science ; Chemical space ; Scheme (mathematics) ; Artificial intelligence ; Machine learning ; Throughput ; Drug discovery ; Nanotechnology ; Materials science ; Algorithm ; Chemistry ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Shizhao Lu | Arthi Jayaraman
Source: arXiv (Cornell University) (preprint, 2023-05-25)
Field: Materials Science — Materials Chemistry

Pair-Variational Autoencoders (PairVAE) for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques In material research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of the synthesized material. Depending on the availability of and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other techniques(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, PairVAE, that works with data from Small Angle X-Ray Scattering (SAXS) that presents information about bulk morphology and images from Scanning Electron Microscopy (SEM) that presents two-dimensional local structural information of the sample. Using paired SAXS and SEM data of novel block copolymer assembled morphologies [open access data from Doerk G.S., et al. Science Advances. 2023 Jan 13;9(2): eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern, and vice versa. This method can be extended to other soft materials morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as creating a database for other downstream calculations of structure-property relationships. Characterization (materials science) ; Small-angle X-ray scattering ; Scattering ; Block (permutation group theory) ; Computer science ; Sample (material) ; Materials science ; Artificial intelligence ; Biological system ; Nanotechnology ; Optics ; Chemistry ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Block Copolymer Self-Assembly
Authors: Jie Chen | Hengrui Zhang | Carolin B. Wahl | Wei Liu | Chad A. Mirkin | Vinayak P. Dravid | Daniel W. Apley | Wei Chen
Source: ChemRxiv (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves an unprecedented testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML be used to accelerate experimental high-throughput materials data analytics. Bottleneck ; Computer science ; Throughput ; Convolutional neural network ; Fuse (electrical) ; Artificial intelligence ; Analytics ; Machine learning ; Process (computing) ; Identification (biology) ; Artificial neural network ; Pattern recognition (psychology) ; Data mining ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hitarth Choubisa | Md Azimul Haque | Tong Zhu | Lewei Zeng | Maral Vafaie | Derya Baran | Edward H. Sargent
Source: arXiv (Cornell University) (preprint, 2023-02-26)
Field: Materials Science — Materials Chemistry

Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways. Here we seek to incorporate historical data and update and refine it using experimental feedback by employing error-correction learning (ECL). We thus learn from prior datasets and then adapt the model to differences in synthesis and characterization that are otherwise difficult to parameterize. We then apply this strategy to discovering thermoelectric materials where we prioritize synthesis at temperatures &lt; 300°C. We document a previously unreported chemical family of thermoelectric materials, PbSe:SnSb, finding that the best candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power factor more than 2x that of PbSe. Our investigations show that our closed-loop experimentation strategy reduces the required number of experiments to find an optimized material by as much as 3x compared to high-throughput searches powered by state-of-the-art machine learning models. We also observe that this improvement is dependent on the accuracy of prior in a manner that exhibits diminishing returns, and after a certain accuracy is reached, it is factors associated with experimental pathways that dictate the trends. Thermoelectric effect ; Loop (graph theory) ; Materials science ; Closed loop ; Computer science ; Physics ; Control engineering ; Engineering ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Shang Zhu | Bharath Ramsundar | Emil Annevelink | Hongyi Lin | Adarsh Dave | Pin-Wen Guan | Kevin L. Gering | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces. Electrolyte ; Differentiable function ; Battery (electricity) ; Robustness (evolution) ; Work (physics) ; Computer science ; Electrode ; Materials science ; Mechanical engineering ; Chemistry ; Thermodynamics ; Physics ; Engineering ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research