jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: The Journal of Physical Chemistry Letters (article, 2021-05-11)
Field: Materials Science — Materials Chemistry

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doomed to fail. Many compelling functional materials and catalytic processes involve strained chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to open-shell transition-metal centers. Although promising targets, these materials present unique challenges for electronic structure methods and combinatorial challenges for their discovery. In this Perspective, we describe the advances needed in accuracy, efficiency, and approach beyond what is typical in conventional DFT-based ML workflows. These challenges have begun to be addressed through ML models trained to predict the results of multiple methods or the differences between them, enabling quantitative sensitivity analysis. For DFT to be trusted for a given data point in a high-throughput screen, it must pass a series of tests. ML models that predict the likelihood of calculation success and detect the presence of strong correlation will enable rapid diagnoses and adaptation strategies. These "decision engines" represent the first steps toward autonomous workflows that avoid the need for expert determination of the robustness of DFT-based materials discoveries. Computer science ; Robustness (evolution) ; Workflow ; Density functional theory ; Machine learning ; Artificial intelligence ; Key (lock) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: David Rosenberger | Justin S. Smith | Angel E. Garcı́a
Source: The Journal of Physical Chemistry B (article, 2021-04-02)
Field: Materials Science — Materials Chemistry

Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison The replacement of classical force fields (FFs) with novel neural-network-based frameworks is an emergent topic in molecular dynamics (MD) simulations. In contrast to classical FFs, which have proven their capability to provide insights into complex soft matter systems at an atomistic resolution, the machine learning (ML) potentials have yet to demonstrate their applicability for soft materials. However, the underlying philosophy, which is learning the energy of an atom in its surrounding chemical environment, makes this approach a promising tool. In particular for the exploration of novel chemical compounds, which have not been considered in the original parametrization of classical FFs. In this article, we study the performance of the ANI-2x ML model and compare the results with those of two classical FFs, namely, CHARMM27 and the GROMOS96 43a1 FF. We explore the performance of these FFs for bulk water and two model peptides, trialanine and a 9-mer of the α-aminoisobutyric acid, in vacuum and water. The results for water describe a highly ordered water structure, with a structure similar to those using <i>ab initio</i> molecular dynamics simulations. The energy landscape of the peptides described by Ramachandran maps show secondary structure basins similar to those of the classical FFs but differ in the position and relative stability of the basins. Details of the sampled structures show a divergent performance of the different models, which can be related either to the short-ranged nature of the ML potentials or to shortcomings of the underlying data set used for training. These findings highlight the current state of the applicability of ANI-2x ML potential for MD simulations of soft matter systems. Simultaneously, they provide insights for future improvements of current ML potentials. Ramachandran plot ; Stability (learning theory) ; Artificial neural network ; Parametrization (atmospheric modeling) ; Energy landscape ; Molecular dynamics ; Computer science ; Force field (fiction) ; Biological system ; Position (finance) ; Statistical physics ; Ab initio ; Artificial intelligence ; Set (abstract data type) ; Chemistry ; Physics ; Machine learning ; Computational chemistry ; Protein structure ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Kun Xue | Fuke Wang | Ady Suwardi | Ming‐Yong Han | Peili Teo | Pei Wang | Shijie Wang | Enyi Ye | Zibiao Li | Xian Jun Loh
Source: Materials Today Bio (review, 2021-09-01)
Field: Materials Science — Materials Chemistry

Biomaterials by design: Harnessing data for future development Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality. Commercialization ; Pipeline (software) ; Computer science ; Nanotechnology ; New product development ; Data science ; Field (mathematics) ; Engineering ; Materials science ; Business Machine Learning in Materials Science ; 3D Printing in Biomedical Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Cheng Qian | Ben McLean | Daniel Hedman | Feng Ding
Source: APL Materials (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

A comprehensive assessment of empirical potentials for carbon materials Carbon materials and their unique properties have been extensively studied by molecular dynamics, thanks to the wide range of available carbon bond order potentials (CBOPs). Recently, with the increase in popularity of machine learning (ML), potentials such as Gaussian approximation potential (GAP), trained using ML, can accurately predict results for carbon. However, selecting the right potential is crucial as each performs differently for different carbon allotropes, and these differences can lead to inaccurate results. This work compares the widely used CBOPs and the GAP-20 ML potential with density functional theory results, including lattice constants, cohesive energies, defect formation energies, van der Waals interactions, thermal stabilities, and mechanical properties for different carbon allotropes. We find that GAP-20 can more accurately predict the structure, defect properties, and formation energies for a variety of crystalline phase carbon compared to CBOPs. Importantly, GAP-20 can simulate the thermal stability of C60 and the fracture of carbon nanotubes and graphene accurately, where CBOPs struggle. However, similar to CBOPs, GAP-20 is unable to accurately account for van der Waals interactions. Despite this, we find that GAP-20 outperforms all CBOPs assessed here and is at present the most suitable potential for studying thermal and mechanical properties for pristine and defective carbon. van der Waals force ; Materials science ; Graphene ; Carbon fibers ; Density functional theory ; Carbon nanotube ; Chemical physics ; Band gap ; Nanotechnology ; Computational chemistry ; Molecule ; Composite material ; Chemistry ; Composite number Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: S. Quondam Antonio | Francesco Riganti Fulginei | Antonino Laudani | Antonio Faba | E. Cardelli
Source: Journal of Magnetism and Magnetic Materials (article, 2021-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms Preisach model of hysteresis ; Artificial neural network ; Hysteresis ; Waveform ; Magnetic hysteresis ; Electrical steel ; Computer science ; Pulse-width modulation ; Set (abstract data type) ; Algorithm ; Control theory (sociology) ; Topology (electrical circuits) ; Physics ; Magnetization ; Voltage ; Materials science ; Artificial intelligence ; Magnetic field ; Mathematics ; Condensed matter physics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Alioscka A. Sousa | Peter Schuck | Sergio A. Hassan
Source: Nanoscale Advances (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Biomolecular interactions of ultrasmall metallic nanoparticles and nanoclusters The use of nanoparticles (NPs) in biomedicine has made a gradual transition from proof-of-concept to clinical applications, with several NP types meeting regulatory approval or undergoing clinical trials. A new type of metallic nanostructures called ultrasmall nanoparticles (usNPs) and nanoclusters (NCs), while retaining essential properties of the larger (classical) NPs, have features common to bioactive proteins. This combination expands the potential use of usNPs and NCs to areas of diagnosis and therapy traditionally reserved for small-molecule medicine. Their distinctive physicochemical properties can lead to unique <i>in vivo</i> behaviors, including improved renal clearance and tumor distribution. Both the beneficial and potentially deleterious outcomes (cytotoxicity, inflammation) can, in principle, be controlled through a judicious choice of the nanocore shape and size, as well as the chemical ligands attached to the surface. At present, the ability to control the behavior of usNPs is limited, partly because advances are still needed in nanoengineering and chemical synthesis to manufacture and characterize ultrasmall nanostructures and partly because our understanding of their interactions in biological environments is incomplete. This review addresses the second limitation. We review experimental and computational methods currently available to understand molecular mechanisms, with particular attention to usNP-protein complexation, and highlight areas where further progress is needed. We discuss approaches that we find most promising to provide relevant molecular-level insight for designing usNPs with specific behaviors and pave the way to translational applications. Nanoclusters ; Nanoparticle ; Nanotechnology ; Materials science ; Metal ; Metallurgy Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery
Authors: Andreas Eschenbacher | Farnoosh Goodarzi | Robin John Varghese | Kasper Enemark‐Rasmussen | Søren Kegnæs | Mehrdad Seifali Abbas‐Abadi | Kevin M. Van Geem
Source: ACS Sustainable Chemistry & Engineering (article, 2021-10-18)
Field: Chemistry — Inorganic Chemistry

Boron-Modified Mesoporous ZSM-5 for the Conversion of Pyrolysis Vapors from LDPE and Mixed Polyolefins: Maximizing the C<sub>2</sub>–C<sub>4</sub> Olefin Yield with Minimal Carbon Footprint Recovering monomers from polyolefins is challenging due to their chemical inertness. Chemical recycling via pyrolysis produces a complex liquid requiring subsequent upgrading and further processing. In the presented work, low-density polyethylene and postconsumer mixed polyolefin (MPO) waste were pyrolyzed at 550 °C and the vapors were passed over a catalyst to maximize the production of monomers, that is, light olefins and aromatics, which could produce new polymers in a circular approach. Hierarchical HZSM-5 zeolites containing both micro- and mesopores and their boron-modified versions with medium and high Si/Al ratios were studied and thoroughly characterized. The effect of catalyst temperature (550–700 °C), catalyst/feed ratio, steam treatment, and catalyst deactivation by coking was investigated using a tandem micropyrolyzer coupled to comprehensive two-dimensional gas chromatography and flame ionization detection/time-of-flight mass spectrometry detectors, allowing a detailed identification and quantification of the products. All catalysts effectively narrowed the product distribution with high selectivity toward C2–C4 olefins, monoaromatics, and C5–C11 aliphatics. HZSM-5 zeolite catalysts with a medium Si/Al ratio showed a higher monomer recovery (∼90%) than high Si/Al (∼80%), which has a higher selectivity to C5–C11 aliphatics. Importantly, the product yield distribution from MPO closely resembled low-density polyethylene. The addition of boron to the zeolite reduced the aromatization activity. Similarly, at comparable conversion levels, mild steam treatment of the catalyst lowered the selectivity to aromatics and increased the recovery of C2–C4 olefins. Hierarchical HZSM-5 zeolite showed improved tolerance against deactivation by coking compared to conventional HZSM-5, demonstrating the potential of boron-modified hierarchical HZSM-5 zeolite to recover monomers from polyolefin waste at high selectivity. Low-density polyethylene ; Catalysis ; Zeolite ; Pyrolysis ; Chemistry ; Chemical engineering ; Polyolefin ; Selectivity ; Product distribution ; Mesoporous material ; Polyethylene ; Yield (engineering) ; Organic chemistry ; Molecular sieve ; Materials science ; Composite material Zeolite Catalysis and Synthesis ; Advanced Battery Materials and Technologies ; Thermochemical Biomass Conversion Processes
Authors: Elzbieta Rembeza | Martin K. M. Engqvist
Source: PLoS Computational Biology (article, 2021-09-23)
Field: Chemistry — Spectroscopy

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation. Class (philosophy) ; Enzyme ; Similarity (geometry) ; Computational biology ; Domain (mathematical analysis) ; Computer science ; Function (biology) ; Database ; Biology ; Genetics ; Artificial intelligence ; Mathematics ; Biochemistry Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Enzyme Structure and Function
Authors: Matthew R. Aronson | Scott H. Medina | Michael J. Mitchell
Source: APL Bioengineering (review, 2021-01-25)
Field: Materials Science — Biomaterials

Peptide functionalized liposomes for receptor targeted cancer therapy Most clinically approved cancer therapies are potent and toxic small molecules that are limited by severe off-target toxicities and poor tumor-specific localization. Over the past few decades, attempts have been made to load chemotherapies into liposomes, which act to deliver the therapeutic agent directly to the tumor. Although liposomal encapsulation has been shown to decrease toxicity in human patients, reliance on passive targeting via the enhanced permeability and retention (EPR) effect has left some of these issues unresolved. Recently, investigations into modifying the surface of liposomes via covalent and/or electrostatic functionalization have offered mechanisms for tumor homing and subsequently controlled chemotherapeutic delivery. A wide variety of biomolecules can be utilized to functionalize liposomes such as proteins, carbohydrates, and nucleic acids, which enable multiple directions for cancer cell localization. Importantly, when nanoparticles are modified with such molecules, care must be taken as not to inactivate or denature the ligand. Peptides, which are small proteins with <30 amino acids, have demonstrated the exceptional ability to act as ligands for transmembrane protein receptors overexpressed in many tumor phenotypes. Exploring this strategy offers a method in tumor targeting for cancers such as glioblastoma multiforme, pancreatic, lung, and breast based on the manifold of receptors overexpressed on various tumor cell populations. In this review, we offer a comprehensive summary of peptide-functionalized liposomes for receptor-targeted cancer therapy. Liposome ; Receptor ; Small molecule ; Cancer research ; Drug delivery ; Chemistry ; Peptide ; Cancer cell ; Cancer ; Biochemistry ; Biology ; Medicine ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Charles D. Ross | Gabriele Gradoni | Qi Jian Lim | Zhen Peng
Source: IEEE Transactions on Antennas and Propagation (article, 2021-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering Reflective Metasurfaces With Ising Hamiltonian and Quantum Annealing We present a novel and flexible method to optimize the phase response of reflective metasurfaces (MSs) toward the desired scattering profile. The scattering power is expressed as a spin-chain Hamiltonian using the radar cross section (RCS) formalism. For MSs reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing (QA) algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective MSs. Even though the work is focused on the phase modulation of MSs, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces (RISs) that are modulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks. Ising model ; Hamiltonian (control theory) ; Adiabatic process ; Physics ; Adiabatic quantum computation ; Scattering ; Quantum ; Geometric phase ; Qubit ; Quantum mechanics ; Quantum computer ; Mathematics ; Mathematical optimization Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Memory and Neural Computing
Authors: Tinnabhop Santadkha | Wanwisa Skolpap | Veerachai Thitapakorn
Source: Journal of Pharmaceutical Sciences (article, 2021-11-25)
Field: Materials Science — Biomaterials

Diffusion Modeling and In Vitro Release Kinetics Studies of Curcumin−Loaded Superparamagnetic Nanomicelles in Cancer Drug Delivery System Curcumin ; Diffusion ; Chemistry ; Kinetics ; Drug delivery ; Controlled release ; Crystallinity ; Nanoparticle ; Chemical engineering ; Chromatography ; Nuclear chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Biochemistry ; Thermodynamics ; Crystallography Nanoparticle-Based Drug Delivery ; Curcumin's Biomedical Applications ; Advanced Drug Delivery Systems
Authors: Pengkai Qin | Linbo Wu | Bo‐Geng Li | Naixiang Li | Xiaohu Pan | Junming Dai
Source: Polymers (article, 2021-10-08)
Field: Materials Science — Biomaterials

Superior Gas Barrier Properties of Biodegradable PBST vs. PBAT Copolyesters: A Comparative Study As a bio-based counterpart of poly(butylene adipate-co-terephthalate) (PBAT), the well-known commercially available biodegradable aliphatic-aromatic copolyester, poly(butylene succinate-co-terephthalate) (PBST) has comparable physical and mechanical properties, but its gas barrier properties, which are very important for packaging material and mulch film applications, have not yet been reported in literature. In this paper, the O<sub>2</sub>, CO<sub>2</sub> and water vapor barrier properties of PBST vs. PBAT were comparatively studied and reported for the first time. Theoretical calculation of O<sub>2</sub> and CO<sub>2</sub> permeation coefficients via group contribution method was also conducted. The barrier properties of PBST show clear copolymer composition dependence due to different contribution of BS and BT repeat units and composition-dependent crystallinity. Comparing with PBAT, PBST with close copolymer and three-phase (crystalline, amorphous, rigid amorphous) compositions shows 3.5 times O<sub>2</sub> and CO<sub>2</sub> and 1.5 times water vapor barrier properties. The slower segment movement and less free volume of PBST, and therefore slower gas diffusion in PBST, accounts for its superior O<sub>2</sub> and CO<sub>2</sub> barrier, while the better hydrophilicity of PBST counteracts partial contribution of slower segment movement so that the improvement in water vapor barrier is not as high as in O<sub>2</sub> and CO<sub>2</sub> barrier. Adipate ; Materials science ; Crystallinity ; Permeation ; Amorphous solid ; Chemical engineering ; Copolymer ; Copolyester ; Polymer chemistry ; Polymer ; Composite material ; Polyester ; Chemistry ; Organic chemistry ; Membrane biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Natural Fiber Reinforced Composites
Authors: Andreas Nenning | Manuel Holzmann | Jürgen Fleig | Alexander Karl Opitz
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Excellent kinetics of single-phase Gd-doped ceria fuel electrodes in solid oxide cells Solid oxide fuel electrodes with pure GDC as the functional layer exhibit excellent kinetics, in line with mechanistic impedance modelling. Materials science ; Kinetics ; Oxide ; Doping ; Phase (matter) ; Fuel cells ; Chemical engineering ; Electrode ; Chemistry ; Optoelectronics ; Physical chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science