jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anderson G. M. da Silva | Thenner S. Rodrigues | Jiale Wang | Pedro H. C. Camargo
Source: Chemical Communications (review, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic catalysis with designer nanoparticles Catalysis is central to a more sustainable future and a circular economy. If the energy required to drive catalytic processes could be harvested directly from sunlight, the possibility of replacing contemporary processes based on terrestrial fuels by the conversion of light into chemical energy could become a step closer to reality. Plasmonic catalysis is currently at the forefront of photocatalysis, enabling one to overcome the limitations of "classical" wide bandgap semiconductors for solar-driven chemistry. Plasmonic catalysis enables the acceleration and control of a variety of molecular transformations due to the localized surface plasmon resonance (LSPR) excitation. Studies in this area have often focused on the fundamental understanding of plasmonic catalysis and the demonstration of plasmonic catalytic activities towards different reactions. In this feature article, we discuss recent contributions from our group in this field by employing plasmonic nanoparticles (NPs) with controllable features as model systems to gain insights into structure-performance relationships in plasmonic catalysis. We start by discussing the effect of size, shape, and composition in plasmonic NPs over their activities towards LSPR-mediated molecular transformations. Then, we focus on the effect of metal support interactions over activities, reaction selectivity, and reaction pathways. Next, we shift to the control over the structure in hollow NPs and nanorattles. Inspired by the findings from these model systems, we demonstrate a design-driven strategy for the development of plasmonic catalysts based on plasmonic-catalytic multicomponent NPs for two types of molecular transformations: the selective hydrogenation of phenylacetylene and the oxygen evolution reaction. Finally, future directions, challenges, and perspectives in the field of plasmonic catalysis with designer NPs are discussed. We believe that the examples and concepts presented herein may inspire work and progress in plasmonic catalysis encompassing the design of plasmonic multicomponent materials, new strategies to control reaction selectivity, and the unraveling of stability and reaction mechanisms. Plasmon ; Plasmonic nanoparticles ; Catalysis ; Nanoparticle ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Suliman Khan | Majid Sharifi | Jason P. Gleghorn | Mohammad Mahdi Nejadi Babadaei | Samir Haj Bloukh | Zehra Edis | Mohammadreza Amin | Qian Bai | Timo L.M. ten Hagen | Mojtaba Falahati | William C. Cho
Source: Journal of Controlled Release (review, 2022-06-07)
Field: Materials Science — Biomaterials

Artificial engineering of the protein corona at bio-nano interfaces for improved cancer-targeted nanotherapy PEGylation ; Biocompatibility ; Drug delivery ; Nanotechnology ; Context (archaeology) ; Nanoparticle ; Chemistry ; Nanomedicine ; Cancer cell ; Biophysics ; Surface engineering ; Materials science ; Cancer ; Biochemistry ; Polyethylene glycol Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Characterization and Applications of Magnetic Nanoparticles
Authors: Yaxi Hu | Wenlin Zhao | Liquan Wang | Jiaping Lin | Lei Du
Source: ACS Applied Materials & Interfaces (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers Despite advances in machine learning for accurately predicting material properties, forecasting the performance of thermosetting polymers remains a challenge due to the sparsity of historical experimental data and their complicated crosslinked structures. We proposed a machine-learning-assisted materials genome approach (MGA) for rapidly designing novel epoxy thermosets with excellent mechanical properties (high tensile moduli, high tensile strength, and high toughness) through high-throughput screening in a vast chemical space. Machine-learning models were established by combining attention- and gate-augmented graph convolutional networks, multilayer perceptrons, classical gel theory, and transfer learning from small molecules to polymers. Proof-of-concept experiments were carried out, and the structures designed by the MGA were verified. Gene substructures affecting the modulus, strength, and toughness were also extracted, revealing the mechanisms of polymers with high mechanical properties. The developed strategy can be employed to design other thermosetting polymers efficiently. Thermosetting polymer ; Materials science ; Polymer ; Toughness ; Ultimate tensile strength ; Epoxy ; Composite material ; Computer science Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Carbon dioxide utilization in catalysis
Authors: Meng Shi | Chang‐Ping Feng | Jiang Li | Shaoyun Guo
Source: Composites Science and Technology (article, 2022-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning to optimize nanocomposite materials for electromagnetic interference shielding Materials science ; Electromagnetic shielding ; Nanocomposite ; Electromagnetic interference ; Composite material ; Interference (communication) ; Electronic engineering ; Computer science ; Telecommunications ; Engineering Electromagnetic wave absorption materials ; Material Properties and Applications ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Nikolaj Rørbæk Knøsgaard | Kristian S. Thygesen
Source: Nature Communications (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Representing individual electronic states for machine learning GW band structures of 2D materials Choosing optimal representation methods of atomic and electronic structures is essential when machine learning properties of materials. We address the problem of representing quantum states of electrons in a solid for the purpose of machine leaning state-specific electronic properties. Specifically, we construct a fingerprint based on energy decomposed operator matrix elements (ENDOME) and radially decomposed projected density of states (RAD-PDOS), which are both obtainable from a standard density functional theory (DFT) calculation. Using such fingerprints we train a gradient boosting model on a set of 46k G<sub>0</sub>W<sub>0</sub> quasiparticle energies. The resulting model predicts the self-energy correction of states in materials not seen by the model with a mean absolute error of 0.14 eV. By including the material's calculated dielectric constant in the fingerprint the error can be further reduced by 30%, which we find is due to an enhanced ability to learn the correlation/screening part of the self-energy. Our work paves the way for accurate estimates of quasiparticle band structures at the cost of a standard DFT calculation. Computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Minki Kim | Min Young Ha | Woo‐Bin Jung | Jeesoo Yoon | Euichul Shin | Il‐doo Kim | Won Bo Lee | YongJoo Kim | Hee‐Tae Jung
Source: Advanced Materials (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Searching for an Optimal Multi‐Metallic Alloy Catalyst by Active Learning Combined with Experiments Searching for an optimal component and composition of multi-metallic alloy catalysts, comprising two or more elements, is one of the key issues in catalysis research. Due to the exhaustive data requirement of conventional machine-learning (ML) models and the high cost of experimental trials, current approaches rely mainly on the combination of density functional theory and ML techniques. In this study, a significant step is taken toward overcoming limitations by the interplay of experiment and active learning to effectively search for an optimal component and composition of multi-metallic alloy catalysts. The active-learning model is iteratively updated using by examining electrocatalytic performance of fabricated solid-solution nanoparticles for the hydrogen evolution reaction (HER). An optimal metal precursor composition of Pt<sub>0.65</sub> Ru<sub>0.30</sub> Ni<sub>0.05</sub> exhibits an HER overpotential of 54.2 mV, which is superior to that of the pure Pt catalyst. This result indicates the successful construction of the model by only utilizing the precursor mixture composition as input data, thereby improving the overpotential by searching for an optimal catalyst. This method appears to be widely applicable since it is able to determine an optimal component and composition of electrocatalyst without obvious restriction to the types of catalysts to which it can be applied. Overpotential ; Materials science ; Catalysis ; Alloy ; Component (thermodynamics) ; Electrocatalyst ; Metal ; Composition (language) ; Chemical engineering ; Electrochemistry ; Metallurgy ; Thermodynamics ; Organic chemistry ; Physical chemistry ; Electrode ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Rami Al-Jarrah | Faris M. AL‐Oqla
Source: Composite Structures (article, 2022-03-16)
Field: Materials Science — Polymers and Plastics

A novel integrated BPNN/SNN artificial neural network for predicting the mechanical performance of green fibers for better composite manufacturing Artificial neural network ; Ultimate tensile strength ; Natural fiber ; Fiber ; Materials science ; Composite number ; Composite material ; Biological system ; Cellulose fiber ; Modulus ; Computer science ; Artificial intelligence Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Advanced Cellulose Research Studies
Authors: Joel M. Bowman | Chen Qu | Riccardo Conte | Apurba Nandi | Paul L. Houston | Qi Yu
Source: Journal of Chemical Theory and Computation (review, 2022-12-17)
Field: Materials Science — Materials Chemistry

Δ-Machine Learned Potential Energy Surfaces and Force Fields There has been great progress in developing machine-learned potential energy surfaces (PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of atoms generally limits the level of electronic structure theory to less than the "gold standard" CCSD(T) level. Indeed, for the well-known MD17 dataset for molecules with 9-20 atoms, all of the energies and forces were obtained with DFT calculations (PBE). This Perspective is focused on a Δ-machine learning method that we recently proposed and applied to bring DFT-based PESs to close to CCSD(T) accuracy. This is demonstrated for hydronium, <i>N</i>-methylacetamide, acetyl acetone, and ethanol. For 15-atom tropolone, it appears that special approaches (e.g., molecular tailoring, local CCSD(T)) are needed to obtain the CCSD(T) energies. A new aspect of this approach is the extension of Δ-machine learning to force fields. The approach is based on many-body corrections to polarizable force field potentials. This is examined in detail using the TTM2.1 water potential. The corrections make use of our recent CCSD(T) datasets for 2-b, 3-b, and 4-b interactions for water. These datasets were used to develop a new fully ab initio potential for water, termed q-AQUA. Computer science ; Potential energy ; Force field (fiction) ; Potential field ; Energy (signal processing) ; Nanotechnology ; Physics ; Artificial intelligence ; Materials science ; Classical mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear Physics and Applications
Authors: Talmage Porter | Michael M. Vaka | Parker Steenblik | Dennis Della Corte
Source: Communications Chemistry (review, 2022-06-02)
Field: Materials Science — Materials Chemistry

Computational methods to simulate molten salt thermophysical properties Molten salts are important thermal conductors used in molten salt reactors and solar applications. To use molten salts safely, accurate knowledge of their thermophysical properties is necessary. However, it is experimentally challenging to measure these properties and a comprehensive evaluation of the full chemical space is unfeasible. Computational methods provide an alternative route to access these properties. Here, we summarize the developments in methods over the last 70 years and cluster them into three relevant eras. We review the main advances and limitations of each era and conclude with an optimistic perspective for the next decade, which will likely be dominated by emerging machine learning techniques. This article is aimed to help researchers in peripheral scientific domains understand the current challenges of molten salt simulation and identify opportunities to contribute. Molten salt ; Computer science ; Measure (data warehouse) ; Perspective (graphical) ; Salt (chemistry) ; Molten metal ; Biochemical engineering ; Nanotechnology ; Data science ; Process engineering ; Materials science ; Chemistry ; Engineering ; Artificial intelligence ; Data mining ; Metallurgy Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Metallurgical Processes and Thermodynamics
Authors: Rebeca T. Stiepel | Erik S. Pena | Stephen A. Ehrenzeller | Matthew D. Gallovic | Liubov M. Lifshits | Christopher J. Genito | Eric M. Bachelder | Kristy M. Ainslie
Source: Journal of Controlled Release (article, 2022-10-12)
Field: Materials Science — Biomaterials

A predictive mechanistic model of drug release from surface eroding polymeric nanoparticles Drug delivery ; Drug ; Chemistry ; Nanoparticle ; Diffusion ; Polymer ; Drug carrier ; Pharmacology ; Nanotechnology ; Materials science ; Biophysics ; Organic chemistry ; Thermodynamics ; Medicine Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Polymer Surface Interaction Studies
Authors: Dongliang Ding | Minhao Zou | Xu Wang | Guangzhao Qin | Shiyu Zhang | Siew Yin Chan | Qingyong Meng | Zhen‐Guo Liu | Qiuyu Zhang | Yanhui Chen
Source: Chemical Engineering Journal (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Thermal conductivity of polydisperse hexagonal BN/polyimide composites: Iterative EMT model and machine learning based on first principles investigation Materials science ; Thermal conductivity ; Composite material ; Boron nitride ; Thermal conduction ; Exfoliation joint ; Polyimide ; Hot pressing ; Graphene ; Nanotechnology Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Heat Transfer and Optimization
Authors: Zihao Wang | Teng Zhou | Kai Sundmacher
Source: Chemical Engineering Journal (article, 2022-04-27)
Field: Chemistry — Inorganic Chemistry

Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation PubChem ; Fingerprint (computing) ; Ethylene ; Metal-organic framework ; Computer science ; Cheminformatics ; Molecular descriptor ; Artificial intelligence ; Matching (statistics) ; Substructure ; Biological system ; Machine learning ; Biochemical engineering ; Chemistry ; Quantitative structure–activity relationship ; Engineering ; Mathematics ; Computational chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Çiğdem Altıntaş | İlknur Eruçar | Seda Keskın
Source: CrystEngComm (review, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

MOF/COF hybrids as next generation materials for energy and biomedical applications The rapid increase in the number and variety of metal organic frameworks (MOFs) and covalent organic frameworks (COFs) has led to groundbreaking applications in the field of materials science and engineering. New MOF/COF hybrids combine the outstanding features of MOF and COF structures, such as high crystallinities, large surface areas, high porosities, the ability to decorate the structures with functional groups, and improved chemical and mechanical stabilities. These new hybrid materials offer promising performances for a wide range of applications including catalysis, energy storage, gas separation, and nanomedicine. In this highlight, we discuss the recent advancements of MOF/COF hybrids as next generation materials for energy and biomedical applications with a special focus on the use of computational tools to address the opportunities and challenges of using MOF/COF hybrids for various applications. Materials science ; Nanotechnology ; High heat ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Muhammad Iqbal Maulana | Muhammad Adly Rahandi Lubis | Fauzi Febrianto | Seng Hua Lee | Apri Heri Iswanto | Petar Antov | Lubos Kristak | Efri Mardawati | Rita Kartika Sari | Lukmanul Hakim Zaini | Wahyu Hidayat | Valentina Lo Giudice | Luigi Todaro
Source: Forests (review, 2022-10-02)
Field: Materials Science — Biomaterials

Environmentally Friendly Starch-Based Adhesives for Bonding High-Performance Wood Composites: A Review In recent years, bio-based wood adhesives have gained an increased industrial and research interest as an environmentally friendly and renewable alternative to the commercial petroleum-based synthetic adhesives used in the wood-based industry. Due to its renewability, abundance, relatively low price, and good adhesion properties, starch is a promising natural feedstock for synthesizing bio-based adhesives for wood-based composites. This review aims to summarize the recent advances in developing sustainable starch-based wood adhesives for manufacturing non-toxic, low-emission wood composites with enhanced properties and lower environmental impact. Recent developments in starch modification, physical, and enzymatic treatments applied to improve the performance of starch-based wood adhesives, mainly in terms of improving their water resistance and bonding strength, are also outlined and discussed. Environmentally friendly ; Adhesive ; Raw material ; Materials science ; Engineered wood ; Starch ; Renewable energy ; Composite material ; Pulp and paper industry ; Renewable resource ; Chemistry ; Engineering biodegradable polymer synthesis and properties ; Lignin and Wood Chemistry ; Advanced Cellulose Research Studies
Authors: Alap Ali Zahid | Aishik Chakraborty | Yasmeen Shamiya | Shruthi Polla Ravi | Arghya Paul
Source: Materials Horizons (review, 2022-01-01)
Field: Materials Science — Biomaterials

Leveraging the advancements in functional biomaterials and scaffold fabrication technologies for chronic wound healing applications Exploring new avenues for clinical management of chronic wounds holds the key to eliminating socioeconomic burdens and health-related concerns associated with this silent killer. Engineered biomaterials offer great promise for repair and regeneration of chronic wounds because of their ability to deliver therapeutics, protect the wound environment, and support the skin matrices to facilitate tissue growth. This mini review presents recent advances in biomaterial functionalities for enhancing wound healing and demonstrates a move from sub-optimal methods to multi-functionalized treatment approaches. In this context, we discuss the recently reported biomaterial characteristics such as bioadhesiveness, antimicrobial properties, proangiogenic attributes, and anti-inflammatory properties that promote chronic wound healing. In addition, we highlight the necessary mechanical and mass transport properties of such biomaterials. Then, we discuss the characteristic properties of various biomaterial templates, including hydrogels, cryogels, nanomaterials, and biomolecule-functionalized materials. These biomaterials can be microfabricated into various structures, including smart patches, microneedles, electrospun scaffolds, and 3D-bioprinted structures, to advance the field of biomaterial scaffolds for effective wound healing. Finally, we provide an outlook on the future while emphasizing the need for their detailed functional behaviour and inflammatory response studies in a complex <i>in vivo</i> environment for superior clinical outcomes and reduced regulatory hurdles. Scaffold ; Wound healing ; Nanotechnology ; Materials science ; Fabrication ; Biomedical engineering ; Medicine ; Surgery ; Pathology Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Surgical Sutures and Adhesives
Authors: Jana Zecha | Wassim Gabriel | Ria Spallek | Yun-Chien Chang | Julia Mergner | Mathias Wilhelm | Florian Bassermann | Bernhard Küster
Source: Nature Communications (article, 2022-01-10)
Field: Chemistry — Spectroscopy

Linking post-translational modifications and protein turnover by site-resolved protein turnover profiling Proteome-wide measurements of protein turnover have largely ignored the impact of post-translational modifications (PTMs). To address this gap, we employ stable isotope labeling and mass spectrometry to measure the turnover of >120,000 peptidoforms including >33,000 phosphorylated, acetylated, and ubiquitinated peptides for >9,000 native proteins. This site-resolved protein turnover (SPOT) profiling discloses global and site-specific differences in turnover associated with the presence or absence of PTMs. While causal relationships may not always be immediately apparent, we speculate that PTMs with diverging turnover may distinguish states of differential protein stability, structure, localization, enzymatic activity, or protein-protein interactions. We show examples of how the turnover data may give insights into unknown functions of PTMs and provide a freely accessible online tool that allows interrogation and visualisation of all turnover data. The SPOT methodology is applicable to many cell types and modifications, offering the potential to prioritize PTMs for future functional investigations. Protein turnover ; Proteome ; Ubiquitin ; Posttranslational modification ; Acetylation ; Computational biology ; Chemistry ; Biochemistry ; Biology ; Protein biosynthesis ; Enzyme Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Peptidase Inhibition and Analysis