jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Linda Hung | Joyce A. Yager | Danielle R. Monteverde | Dave Baiocchi | Ha-Kyung Kwon | Shijing Sun | Santosh K. Suram
Source: ChemRxiv (preprint, 2024-03-01)
Field: Materials Science — Materials Chemistry

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights What are researchers’ motivations and challenges related to automation and autonomy in materials science laboratories? Our survey on this topic received 102 responses from researchers across a variety of institutions and in a variety of roles. Accelerated discovery was a clear theme in the responses, and another theme was concern about the role of human researchers. Survey respondents shared a variety of use cases targeting accelerated materials discovery, including examples where partial automation is preferred over full self-driving laboratories. Building on the observed patterns of researcher priorities and needs, we propose a framework for levels of laboratory autonomy from non-automated (L0) to fully autonomous (L5). Data science ; Computer science ; Management science ; Engineering ; Engineering ethics Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Yuqiang Li | Kaipeng Zheng | Weiran Huang | Wanli Ouyang | Han-Sen Zhong
Source: Research Square (preprint, 2024-10-07)
Field: Materials Science — Materials Chemistry

CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning Artificial intelligence ; Deep learning ; Computer science ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Andrew Larsen | Matthew Memmott | Ross Lee | Caden Wilson | John D. Hedengren | John R. Benson
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Multi-Objective Optimization of Molten Salt Microreactor Shielding Perturbations Employing Machine Learning Microreactor ; Electromagnetic shielding ; Molten salt ; Salt (chemistry) ; Computer science ; Nuclear engineering ; Mechanical engineering ; Process engineering ; Engineering ; Materials science ; Chemistry ; Metallurgy ; Composite material ; Catalysis ; Organic chemistry Catalytic Processes in Materials Science ; Nuclear Materials and Properties ; Metallurgical Processes and Thermodynamics
Authors: Shubha R. Kharel | Fanchen Meng | Xiaohui Qu | Matthew R. Carbone | Deyu Lu
Source: arXiv (Cornell University) (preprint, 2024-09-29)
Field: Materials Science — Materials Chemistry

OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with OmniXAS, a framework that contains a suite of transfer learning approaches for XAS prediction, each contributing to improved accuracy and efficiency, as demonstrated on K-edge spectra database covering eight 3d transition metals (Ti-Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving up to order-of-magnitude improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multi-task model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after element-wise fine-tuning outperform element-specific models by up to 69%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach boosts the throughput of XAS modeling by orders of magnitude versus first-principles simulations and is extendable to XAS prediction for a broader range of elements. This transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research. X-ray ; Spectral line ; Absorption (acoustics) ; Physics ; Materials science ; Computer science ; Optics ; Astronomy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Haotong Liang | C Wang | Heshan Yu | Dylan J. Kirsch | Rohit Pant | Austin McDannald | A. Gilad Kusne | Jimin Zhao | Ichiro Takeuchi
Source: arXiv (Cornell University) (preprint, 2024-10-22)
Field: Materials Science — Materials Chemistry

Real-time experiment-theory closed-loop interaction for autonomous materials science Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experiment-theory cycles in practice are usually ad hoc, often inherently difficult, or impractical to repeat on a systematic basis, beset by the scale or the time constraint of computation or the phenomena under study. Here, we demonstrate Autonomous MAterials Search Engine (AMASE), where we enlist robot science to perform self-driving continuous cyclical interaction of experiments and computational predictions for materials exploration. In particular, we have applied the AMASE formalism to the rapid mapping of a temperature-composition phase diagram, a fundamental task for the search and discovery of new materials. Thermal processing and experimental determination of compositional phase boundaries in thin films are autonomously interspersed with real-time updating of the phase diagram prediction through the minimization of Gibbs free energies. AMASE was able to accurately determine the eutectic phase diagram of the Sn-Bi binary thin-film system on the fly from a self-guided campaign covering just a small fraction of the entire composition - temperature phase space, translating to a 6-fold reduction in the number of necessary experiments. This study demonstrates for the first time the possibility of real-time, autonomous, and iterative interactions of experiments and theory carried out without any human intervention. Closed loop ; Loop (graph theory) ; Control theory (sociology) ; Computer science ; Mathematics ; Control engineering ; Engineering ; Control (management) ; Artificial intelligence Machine Learning in Materials Science
Authors: Connor Davel | Nazanin Bassiri‐Gharb | Juan‐Pablo Correa‐Baena
Source: ChemRxiv (preprint, 2024-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning in X-ray Scattering for Materials Discovery and Characterization X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of online crystal structure databases, increased use of in-situ and operando methodologies, and user-accessible beamlines. The new wealth of data has also spawned an increasing use of machine learning (ML) to either construct high-throughput surrogates of established analysis or extract patterns from large datasets. However, XRD spectroscopy has been for many years solved via Rietveld refinement, while most ML techniques are simply complex statistical evaluation methods that are physics-agnostic. The discrepancy between data analysis and the underlying physics can lead to incorrect conclusions and/or limit the wide-spread adoption of ML techniques. In this review, we bridge the gap between ML and XRD spectroscopy with an introduction designed both for new data scientists and experimentalists interested in problems related to ML-guided spectroscopy analysis. We cover how supervised ML methods are used to predict likely symmetries and phases in pure and mixed samples, including challenges related to experimental artifacts and model interpretation. We also review recent uses of unsupervised methods in the extraction of patterns hidden in high-dimensional data, such as in in-situ and microscopic studies. Finally, we discuss the importance of problem formulation, data transferability, and reporting with recent case studies and give various resources throughout to expedite the learning curve for readers new to XRD or ML. We advocate for greater scrutiny of ML methods, how they are presented in the literature, and how to conduct data-driven research responsibly. Computer science ; Characterization (materials science) ; Rietveld refinement ; Machine learning ; Artificial intelligence ; Data mining ; Crystal structure ; Materials science ; Nanotechnology ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Chao Shen | Li, Yuan | Wenkang Zhan | Pan, Shujie | Lin, Fuxin | Kaiyao Xin | Cong, Hui | Xu, Chi | Cheng, Xiaotian | Liu, Ruixiang | Ni, Zhibo | Jin, Chaoyuan | Bo Xu | Chen, Siming | Wei, Zhongming | Xue, Chunlai | Zhanguo Wang | Chao Zhao
Source: arXiv (Cornell University) (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters. Heterojunction ; Semiconductor ; Psychology ; Computer science ; Nanotechnology ; Materials science ; Artificial intelligence ; Engineering physics ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials
Authors: H. M. Johnson | Filipp Gusev | Jordan T. Dull | Yejoon Seo | Rodney D. Priestley | Olexandr Isayev | Barry P. Rand
Source: ChemRxiv (preprint, 2024-04-17)
Field: Materials Science — Materials Chemistry

Discovery of Crystallizable Organic Semiconductors with Machine Learning Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that govern the crystallization of organic thin films. Organic semiconductor ; Semiconductor ; Materials science ; Engineering physics ; Computer science ; Optoelectronics ; Physics Machine Learning in Materials Science
Authors: Vinay I. Hegde | Miroslava Peterson | Sarah I. Allec | Xiaonan Lu | Thiruvillamalai Mahadevan | T.S. Nguyen | Jayani Kalahe | Jared M. Oshiro | Robert J. Seffens | Ethan K. Nickerson | Jincheng Du | Brian J. Riley | John D. Vienna | James E. Saal
Source: arXiv (Cornell University) (preprint, 2024-05-16)
Field: Materials Science — Materials Chemistry

Towards Informatics-Driven Design of Nuclear Waste Forms Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design. In this perspective, we present a few guiding principles for applying informatics-based methods towards the design of novel nuclear waste forms. We advocate for adopting a system design approach, and describe the effective usage of data-driven methods in every stage of such a design process. We demonstrate how this approach can optimally leverage physics-based simulations, machine learning surrogates, and experimental synthesis and characterization, within a feedback-driven closed-loop sequential learning framework. We discuss the importance of incorporating domain knowledge into the representation of materials, the construction and curation of datasets, the development of predictive property models, and the design and execution of experiments. We illustrate the application of this approach by successfully designing and validating Na- and Nd-containing phosphate-based ceramic waste forms. Finally, we discuss open challenges in such informatics-driven workflows and present an outlook for their widespread application for the cleanup of nuclear wastes. Informatics ; Radioactive waste ; Waste management ; Engineering Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Kai Gu | Yingping Liang | Jiaming Su | Peihan Sun | Jia Hui Peng | Naihua Miao | Zhimei Sun | Ying Fu | Haizheng Zhong | Jun Zhang
Source: arXiv (Cornell University) (preprint, 2024-12-14)
Field: Materials Science — Materials Chemistry

Deep Learning Models for Colloidal Nanocrystal Synthesis Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a dataset of 3500 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with inputs of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the importance order of nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals. Nanocrystal ; Colloid ; Nanotechnology ; Deep learning ; Materials science ; Computer science ; Artificial intelligence ; Chemical engineering ; Engineering Machine Learning in Materials Science
Authors: Sarah A. Berger | Christopher Grimm | Marco Cespugli | A. Krassnigg | Tobias Schopper | Irene Marzuoli | Isabel Oroz‐Guinea | Stephan Vrabl | Lukas Roemer | M. WEBER | Fabian M. Kulier | Yuliya Orel | Bettina M. Nestl | Christian Gruber | Georg Steinkellner | Serena Bisagni | Francis Gosselin | Hans Iding | Kurt Puentener | Dennis Wetzl | Wolfgang Kroutil | Joerg H. Schrittwieser
Source: ChemRxiv (preprint, 2024-12-18)
Field: Chemistry — Inorganic Chemistry

Data-Driven Construction of an Imine Reductase Library Capable of Broad-Scope Reductive Amination at Equimolar Substrate Concentrations Reductive amination catalysed by imine reductase (IRED) and reductive aminase (RedAm) enzymes has recently been established as a powerful method for the asymmetric synthesis of chiral amines. While this biocatalytic technology has rapidly progressed from proof of concept to initial industrial applications, its scope and limitations remain to be fully explored. In this work, we report a broad and systematic profiling of reductive amination performance in the sequence space of IREDs and RedAms. This investigation employed an iterative strategy for activity and stereoselectivity screening, guided by chemo- and bioinformatic modelling as well as machine learning. By evaluating the catalytic performance of 175 IREDs against structurally diverse panels of 36 carbonyl compounds and 24 amines, we show that the majority of these enzymes is capable of asymmetric reductive amination at equimolar concentrations of the two substrates (50 mM each). The most effective enzymes identified in this study display sequence characteristics of RedAms, are active on 29–42% of the analysed substrate combinations, and combine high specific activities for the most favourable substrate pair (1.7−27.7 U/mgIRED) with excellent stereoselectivity. Beyond assembling this high-performance enzyme panel, we demonstrate extrapolation from our collected screening data to new substrate combinations by deep learning and the scale-up of selected reactions to a preparative batch size (10 mmol substrate, 200 mL reaction volume), delivering gram amounts of reductive amination products in high yield (63–89%) and optical purity (98% to >99% ee). Reductive amination ; Amination ; Substrate (aquarium) ; Chemistry ; Combinatorial chemistry ; Imine ; Stereoselectivity ; Catalysis ; Organic chemistry ; Biology Asymmetric Hydrogenation and Catalysis ; Enzyme Catalysis and Immobilization ; Antibiotic Resistance in Bacteria
Authors: Christopher C. Price | Yansong Li | Guanyu Zhou | Rehan Younas | Siyang Zeng | Tony Scanlon | Jason M. Munro | Christopher L. Hinkle
Source: arXiv (Cornell University) (preprint, 2024-09-12)
Field: Materials Science — Materials Chemistry

Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis. Nanomaterials ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science Machine Learning in Materials Science
Authors: Ahmad, Jin
Source: Digitala vetenskapliga arkivet (Diva) (Karlstad University) (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Konstruktion av Koordinationsburar med Generativ AI Deep learning methods applied to chemistry can speed the discovery of novel compounds and facilitate the design of highly complex structures that are both valid and have important societal applications. Here, we present a pioneering exploration into the use of Generative Artificial Intelligence (GenAI) to design coordination cages within the field of supramolecular chemistry. Specifically, the study leverages GraphINVENT, a graph-based deep generative model, to facilitate the automated generation of tetrahedral coordination cages. Through a combination of computational tools and cheminformatics, the research aims to extend the capabilities of GenAI, traditionally applied in simpler chemical contexts, to the complex and nuanced arena of coordination cages. The approach involves a variety of training strategies, including initial pre-training on a large dataset (GDB-13) followed by transfer learning targeted at generating specific coordination cage structures. Data augmentation techniques were also applied to enrich training but did not yield successful outcomes. Several other strategies were employed, including focusing on single metal ion structures to enhance model familiarity with Fe-based cages and extending training datasets with diverse molecular examples from the ChEMBL database. Despite these strategies, the models struggled to capture the complex interactions required for successful cage generation, indicating potential limitations with both the diversity of the training datasets and the model’s architectural capacity to handle the intricate chemistry of coordination cages. However, training on the organic ligands (linkers) yielded successful results, emphasizing the benefits of focusing on smaller building blocks. The lessons learned from this project are substantial. Firstly, the knowledge acquired about generative models and the complex world of supramolecular chemistry has provided a unique opportunity to understand the challenges and possibilities of applying GenAI to such a complicated field. The results obtained in this project have highlighted the need for further refinement of data handling and model training techniques, paving the way for more advanced applications in the future. Finally, this project has not only raised our understanding of the capabilities and limitations of GenAI in coordination cages, but also set a foundation for future research that could eventually lead to breakthroughs in designing novel cage structures. Further study could concentrate on learning from the linkers in future data-driven cage design projects. Generative grammar ; Variety (cybernetics) ; Field (mathematics) ; chEMBL ; Deep learning ; Generative model Supramolecular Chemistry and Complexes ; Machine Learning in Materials Science ; Computational Drug Discovery Methods