jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nadjib Kihal | Ali Nazemi | Steve Bourgault
Source: Nanomaterials (review, 2022-04-05)
Field: Materials Science — Biomaterials

Supramolecular Nanostructures Based on Perylene Diimide Bioconjugates: From Self-Assembly to Applications Self-assembling π-conjugated systems constitute efficient building blocks for the construction of supramolecular structures with tailored functional properties. In this context, perylene diimide (PDI) has attracted attention owing to its chemical robustness, thermal and photo-stability, and outstanding optical and electronic properties. Recently, the conjugation of PDI derivatives to biological molecules, including oligonucleotides and peptides, has opened new avenues for the design of nanoassemblies with unique structures and functionalities. In the present review, we offer a comprehensive summary of supramolecular bio-assemblies based on PDI. After briefly presenting the physicochemical, structural, and optical properties of PDI derivatives, we discuss the synthesis, self-assembly, and applications of PDI bioconjugates. Diimide ; Perylene ; Supramolecular chemistry ; Nanotechnology ; Materials science ; Self-assembly ; Nanostructure ; Context (archaeology) ; Conjugated system ; Molecule ; Combinatorial chemistry ; Chemistry ; Polymer ; Organic chemistry Supramolecular Self-Assembly in Materials ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Margarida M. A. Sacramento | João Borges | Fernando J. S. Correia | Ricardo Calado | João M. M. Rodrigues | Sónia G. Patrício | João F. Mano
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-12-08)
Field: Materials Science — Biomaterials

Green approaches for extraction, chemical modification and processing of marine polysaccharides for biomedical applications Over the past few decades, natural-origin polysaccharides have received increasing attention across different fields of application, including biomedicine and biotechnology, because of their specific physicochemical and biological properties that have afforded the fabrication of a plethora of multifunctional devices for healthcare applications. More recently, marine raw materials from fisheries and aquaculture have emerged as a highly sustainable approach to convert marine biomass into added-value polysaccharides for human benefit. Nowadays, significant efforts have been made to combine such circular bio-based approach with cost-effective and environmentally-friendly technologies that enable the isolation of marine-origin polysaccharides up to the final construction of a biomedical device, thus developing an entirely sustainable pipeline. In this regard, the present review intends to provide an up-to-date outlook on the current green extraction methodologies of marine-origin polysaccharides and their molecular engineering toolbox for designing a multitude of biomaterial platforms for healthcare. Furthermore, we discuss how to foster circular bio-based approaches to pursue the further development of added-value biomedical devices, while preserving the marine ecosystem. Biochemical engineering ; Toolbox ; Nanotechnology ; Biomedicine ; Computer science ; Biotechnology ; Engineering ; Biology ; Bioinformatics ; Materials science biodegradable polymer synthesis and properties ; Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications
Authors: Anna Doersam | Olga Tsigkou | Celina Jones
Source: Advanced Materials Technologies (article, 2022-06-22)
Field: Materials Science — Biomaterials

A Review: Textile Technologies for Single and Multi‐Layer Tubular Soft Tissue Engineering Abstract In cell‐free scaffold tissue engineering (TE), an essential prerequisite is the scaffold design to promote cellular activities and tissue formation. The success is greatly dependent upon the nature of the scaffold including the composition, topography, and mechanical performance. Recent TE approaches use textile technologies to create biomimetic and functional scaffolds similar to the extracellular matrix (ECM). The hierarchical architecture of fiber to yarn to fabric allows precise size‐to‐scale design. Moreover, textile technologies offer a high degree of manufacturing precision, reproducibility, and potential industrial scalability. Although research in industry and academia has increased significantly over the past years, current fibrous scaffold designs don't fully exploit the potential and diversity of yarn and fabric structures. This review emphasizes hollow tubular textile‐based scaffolds that aim to replace vasculature and respiratory tissues. The different techniques, that is, electrospinning, weaving, knitting, and braiding are discussed and the effect of the fiber, yarn, and fabric assembly is highlighted. The design of multi‐layer hybrid structures, combining conventional textile techniques and electrospinning can address current challenges. Prospects of structural design in TE scaffold fabrication can anticipate profiled fibers, and complex pattern designs to fully exploit the potential of textile technologies for tissue‐engineered scaffolds. Scaffold ; Textile ; Weaving ; Electrospinning ; Yarn ; Tissue engineering ; Nanotechnology ; Materials science ; Fiber ; 3D printing ; Modular design ; Computer science ; Biomedical engineering ; Engineering ; Composite material Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Advanced Sensor and Energy Harvesting Materials
Authors: Ryan‐Rhys Griffiths | Leo Klarner | Henry B. Moss | Aditya Ravuri | Sang Truong | Stanton, Samuel | Tom, Gary | Bojana Rankovic | Yuanqi Du | Arian R. Jamasb | Deshwal, Aryan | Julius Schwartz | Austin Tripp | Gregory Kell | Frieder, Simon | Anthony Bourached | Anthony K.C. Chan | Jacob Moss | Chengzhi Guo | Durholt, Johannes | Chaurasia, Saudamini | Strieth-Kalthoff, Felix | Alpha A. Lee | Cheng, Bingqing | Aspuru-Guzik, Alán | Philippe Schwaller | Jian Tang
Source: arXiv (Cornell University) (preprint, 2022-12-06)
Field: Chemistry — Spectroscopy

GAUCHE: A Library for Gaussian Processes in Chemistry We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche Gaussian ; Codebase ; Computer science ; Gaussian process ; Probabilistic logic ; Theoretical computer science ; Algorithm ; Chemistry ; Artificial intelligence ; Programming language ; Computational chemistry Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Patrick Reiser | Marlen Neubert | André Eberhard | Luca Torresi | Chen Zhou | Chen Shao | Houssam Metni | Clint van Hoesel | Henrik Schopmans | Timo Sommer | Pascal Friederich
Source: Repository KITopen (Karlsruhe Institute of Technology) (preprint, 2022-01-01)
Field: Materials Science — Materials Chemistry

Graph neural networks for materials science and chemistry Computer science ; Graph ; Relevance (law) ; Cheminformatics ; Data science ; Representation (politics) ; Artificial intelligence ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Duanyang Liu | Liming Xu | Xu-Min Lin | Xing Wei | Wenjie Yu | Yan Wang | Zhongming Wei
Source: Chip (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Machine learning for semiconductors Thanks to the increasingly high standard of electronics, the semiconductor material science and semiconductor manufacturing have been booming in the last few decades, with massive data accumulated in both fields. If analyzed effectively, the data will be conducive to the discovery of new semiconductor materials and the development of semicondulctor manufacturing. Fortunately, machine learning, as a fast-growing tool from computer science, is expected to significantly speed up the data analysis. In recently years, many researches on machine learning study of semiconductor materials and semiconductor manufacturing have been reported. This article is aimed to introduce these progress and present some prospects in this field. Semiconductor device fabrication ; Semiconductor ; Field (mathematics) ; Semiconductor materials ; Electronics ; Semiconductor device ; Computer science ; Manufacturing engineering ; Engineering physics ; Engineering ; Nanotechnology ; Electrical engineering ; Materials science ; Mathematics Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: David Kuntz | Angela K. Wilson
Source: Pure and Applied Chemistry (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory Abstract Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory. Artificial intelligence ; Workflow ; Chemistry ; Machine learning ; Automation ; Computer science ; Algorithm ; Engineering ; Database ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Xiaohua Tian | Dingding Shi | Kun Zhang | Hong-Xing Li | Zhou Li-wen | Tianyou Ma | Cheng Wang | Qinlong Wen | Changlong Tan
Source: Computational Materials Science (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys Support vector machine ; Shape-memory alloy ; Generalization ; Artificial intelligence ; Ferromagnetism ; Diffusionless transformation ; Cross-validation ; Transformation (genetics) ; Machine learning ; Computer science ; Feature (linguistics) ; Random forest ; Algorithm ; Materials science ; Martensite ; Mathematics ; Condensed matter physics ; Mathematical analysis ; Physics ; Chemistry ; Metallurgy Shape Memory Alloy Transformations
Authors: Sukriti Singh | Raghavan B. Sunoj
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing A transfer learning protocol for yield and enantioselectivity predictions of transition metal- and organo-catalytic reactions, suitable for small (&lt;400) to large (&gt;4000) data regimes. Protocol (science) ; Transfer of learning ; Catalysis ; Yield (engineering) ; Computer science ; Artificial neural network ; Knowledge transfer ; Transfer (computing) ; Artificial intelligence ; Chemistry ; Natural language processing ; Organic chemistry ; Knowledge management ; Medicine ; Physics ; Parallel computing ; Thermodynamics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Songlin Yu | Haiyang Chai | Yuqi Xiong | Ming Kang | Chengzhen Geng | Yu Liu | Yanqiu Chen | Yaling Zhang | Qian Zhang | Changlin Li | Hao Wei | Yuhang Zhao | Fengmei Yu | A. Lu
Source: Advanced Materials (article, 2022-04-28)
Field: Materials Science — Materials Chemistry

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions. Computer science ; Field (mathematics) ; Artificial intelligence ; Scale (ratio) ; Artificial neural network ; Silicone rubber ; Experimental data ; Deep learning ; Materials science ; Machine learning Machine Learning in Materials Science ; Polymer composites and self-healing ; Block Copolymer Self-Assembly
Authors: Nihang Fu | Lai Wei | Yuqi Song | Qinyang Li | Rui Xin | Sadman Sadeed Omee | Rongzhi Dong | Edirisuriya M. Dilanga Siriwardane | Jianjun Hu
Source: Machine Learning Science and Technology (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Material transformers: deep learning language models for generative materials design Abstract Pre-trained transformer language models (LMs) on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation. However, no such models have been applied to learn the composition patterns for the generative design of material compositions. Here we train a series of seven modern transformer models (GPT, GPT-2, GPT-Neo, GPT-J, BLMM, BART, and RoBERTa) for materials design using the expanded formulas of the ICSD, OQMD, and Materials Projects databases. Six different datasets with/out non-charge-neutral or EB samples are used to benchmark the generative design performances and uncover the biases of modern transformer models for the generative design of materials compositions. Our experiments show that the materials transformers based on causal LMs can generate chemically valid material compositions with as high as 97.61% to be charge neutral and 91.22% to be electronegativity balanced, which has more than six times higher enrichment compared to the baseline pseudo-random sampling algorithm. Our LMs also demonstrate high generation novelty and their potential in new materials discovery is proved by their capability to recover the leave-out materials. We also find that the properties of the generated compositions can be tailored by training the models with selected training sets such as high-bandgap samples. Our experiments also show that different models each have their own preference in terms of the properties of the generated samples and their running time complexity varies a lot. We have applied our materials transformers to discover a set of new materials as validated using density functional theory calculations. All our trained materials transformer models and code can be accessed freely at http://www.github.com/usccolumbia/MTransformer . Transformer ; Generative grammar ; Novelty ; Computer science ; Generative model ; Superposition principle ; Artificial intelligence ; Mathematics ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Software Engineering Research ; Ferroelectric and Negative Capacitance Devices
Authors: Felix Döppel | Martin Votsmeier
Source: Chemical Engineering Science (article, 2022-08-02)
Field: Materials Science — Materials Chemistry

Efficient machine learning based surrogate models for surface kinetics by approximating the rates of the rate-determining steps Kinetics ; Surrogate model ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Heat Transfer and Optimization
Authors: Prashant Singh | Tyler Del Rose | Guillermo Vazquez | Raymundo Arróyave | Yaroslav Mudryk
Source: Acta Materialia (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Machine-learning enabled thermodynamic model for the design of new rare-earth compounds Density functional theory ; Intermetallic ; Materials science ; Machine learning ; Enthalpy ; Stability (learning theory) ; Artificial intelligence ; Metastability ; Thermodynamics ; Computer science ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Rare-earth and actinide compounds ; X-ray Diffraction in Crystallography
Authors: Lung-Yi Chen | Ting-Wei Hsu | Tsai-Chen Hsiung | Yi‐Pei Li
Source: The Journal of Physical Chemistry A (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Increment Theory for Formation Enthalpy Predictions Machine learning predictions of molecular thermochemistry, such as formation enthalpy, have been limited for large and complicated species because of the lack of available training data. Such predictions would be important in the prediction of reaction thermodynamics and the construction of kinetic models. Herein, we introduce a graph-based deep learning approach that can separately learn the enthalpy contribution of each atom in its local environment with the effect of the overall molecular structure taken into account. Because this approach follows the additivity scheme of increment theory, it can be generalized to larger and more complicated species not present in the training data. By training the model on molecules with up to 11 heavy atoms, it can predict the formation enthalpy of testing molecules with up to 42 heavy atoms with a mean absolute error of 2 kcal/mol, which is less than half of the error of the conventional increment theory. We expect that this approach will also enable rapid prediction of other extensive properties of large molecules that are difficult to derive from experiments or <i>ab initio</i> calculation. Thermochemistry ; Chemistry ; Enthalpy ; Molecule ; Additive function ; Standard enthalpy change of formation ; Ab initio ; Standard enthalpy of formation ; Atom (system on chip) ; Thermodynamics ; Statistical physics ; Computational chemistry ; Physical chemistry ; Computer science ; Physics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Adeesh Kolluru | Nima Shoghi | Muhammed Shuaibi | Siddharth Goyal | Abhishek Das | C. Lawrence Zitnick | Zachary W. Ulissi
Source: The Journal of Chemical Physics (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

Transfer learning using attentions across atomic systems with graph neural networks (TAAG) Recent advances in Graph Neural Networks (GNNs) have transformed the space of molecular and catalyst discovery. Despite the fact that the underlying physics across these domains remain the same, most prior work has focused on building domain-specific models either in small molecules or in materials. However, building large datasets across all domains is computationally expensive; therefore, the use of transfer learning (TL) to generalize to different domains is a promising but under-explored approach to this problem. To evaluate this hypothesis, we use a model that is pretrained on the Open Catalyst Dataset (OC20), and we study the model's behavior when fine-tuned for a set of different datasets and tasks. This includes MD17, the *CO adsorbate dataset, and OC20 across different tasks. Through extensive TL experiments, we demonstrate that the initial layers of GNNs learn a more basic representation that is consistent across domains, whereas the final layers learn more task-specific features. Moreover, these well-known strategies show significant improvement over the non-pretrained models for in-domain tasks with improvements of 53% and 17% for the *CO dataset and across the Open Catalyst Project (OCP) task, respectively. TL approaches result in up to 4× speedup in model training depending on the target data and task. However, these do not perform well for the MD17 dataset, resulting in worse performance than the non-pretrained model for few molecules. Based on these observations, we propose transfer learning using attentions across atomic systems with graph Neural Networks (TAAG), an attention-based approach that adapts to prioritize and transfer important features from the interaction layers of GNNs. The proposed method outperforms the best TL approach for out-of-domain datasets, such as MD17, and gives a mean improvement of 6% over a model trained from scratch. Computer science ; Transfer of learning ; Speedup ; Chemical space ; Task (project management) ; Machine learning ; Graph ; Artificial intelligence ; Artificial neural network ; Set (abstract data type) ; Representation (politics) ; Multi-task learning ; Theoretical computer science ; Drug discovery ; Chemistry ; Parallel computing Machine Learning in Materials Science ; Topic Modeling ; Fuel Cells and Related Materials
Authors: Yingzong Liang | Mingwei Chen | Yanan Wang | Huaxian Jia | Tenglong Lu | Fankai Xie | Guanghui Cai | Zongguo Wang | Sheng Meng | Miao Liu
Source: Science China Materials (article, 2022-07-27)
Field: Materials Science — Materials Chemistry

A universal model for accurately predicting the formation energy of inorganic compounds Electronegativity ; Atom (system on chip) ; Computer science ; Throughput ; Energy (signal processing) ; Phase space ; Algorithm ; Statistical physics ; Biological system ; Machine learning ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions