jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vivek Oommen | Khemraj Shukla | Somdatta Goswami | Rémi Dingreville | George Em Karniadakis
Source: npj Computational Materials (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Learning two-phase microstructure evolution using neural operators and autoencoder architectures Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks. Autoencoder ; Extrapolation ; Solver ; Interpolation (computer graphics) ; Computer science ; Representation (politics) ; Artificial intelligence ; Deep learning ; Field (mathematics) ; Operator (biology) ; Phase (matter) ; Mesoscale meteorology ; Convolutional neural network ; Phase space ; Algorithm ; Computational science ; Mathematical optimization ; Mathematics ; Physics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Magnetic Properties and Applications
Authors: Seyyed Mojtaba Mousavi | Seyyed Alireza Hashemi | Masoomeh Yari Kalashgrani | Navid Omidifar | Sonia Bahrani | N. Vijayakameswara Rao | Aziz Babapoor | Ahmad Gholami | Wei‐Hung Chiang
Source: Polymers (review, 2022-02-05)
Field: Materials Science — Materials Chemistry

Bioactive Graphene Quantum Dots Based Polymer Composite for Biomedical Applications Today, nanomedicine seeks to develop new polymer composites to overcome current problems in diagnosing and treating common diseases, especially cancer. To achieve this goal, research on polymer composites has expanded so that, in recent years, interdisciplinary collaborations between scientists have been expanding day by day. The synthesis and applications of bioactive GQD-based polymer composites have been investigated in medicine and biomedicine. Bioactive GQD-based polymer composites have a special role as drug delivery carriers. Bioactive GQDs are one of the newcomers to the list of carbon-based nanomaterials. In addition, the antibacterial and anti-diabetic potentials of bioactive GQDs are already known. Due to their highly specific surface properties, π-π aggregation, and hydrophobic interactions, bioactive GQD-based polymer composites have a high drug loading capacity, and, in case of proper correction, can be used as an excellent option for the release of anticancer drugs, gene carriers, biosensors, bioimaging, antibacterial applications, cell culture, and tissue engineering. In this paper, we summarize recent advances in using bioactive GQD-based polymer composites in drug delivery, gene delivery, thermal therapy, thermodynamic therapy, bioimaging, tissue engineering, bioactive GQD synthesis, and GQD green resuscitation, in addition to examining GQD-based polymer composites. Nanotechnology ; Drug delivery ; Materials science ; Polymer ; Nanomedicine ; Graphene ; Graphene quantum dot ; Gene delivery ; Nanomaterials ; Nanoparticle ; Chemistry ; Composite material ; Genetic enhancement Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Ang Gao | Richard C. Remsing
Source: Nature Communications (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Self-consistent determination of long-range electrostatics in neural network potentials Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network - a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions - and demonstrate its utility by modeling liquid water with and without applied fields. Artificial neural network ; Locality ; Computer science ; Electrostatics ; Range (aeronautics) ; Field (mathematics) ; Statistical physics ; Artificial intelligence ; Interatomic potential ; Machine learning ; Molecular dynamics ; Physics ; Quantum mechanics ; Materials science ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Chenglong Lin | Shunshun Liang | Yusi Peng | Long Li | Yanyan Li | Zhengren Huang | Nguyen Viet Long | Xiaoying Luo | Jianjun Liu | Zhi‐Yuan Li | Yong Yang
Source: Nano-Micro Letters (article, 2022-03-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Visualized SERS Imaging of Single Molecule by Ag/Black Phosphorus Nanosheets Single-molecule detection and imaging are of great value in chemical analysis, biomarker identification and other trace detection fields. However, the localization and visualization of single molecule are still quite a challenge. Here, we report a special-engineered nanostructure of Ag nanoparticles embedded in multi-layer black phosphorus nanosheets (Ag/BP-NS) synthesized by a unique photoreduction method as a surface-enhanced Raman scattering (SERS) sensor. Such a SERS substrate features the lowest detection limit of 10–20 mol L−1 for R6G, which is due to the three synergistic resonance enhancement of molecular resonance, photo-induced charge transfer resonance and electromagnetic resonance. We propose a polarization-mapping strategy to realize the detection and visualization of single molecule. In addition, combined with machine learning, Ag/BP-NS substrates are capable of recognition of different tumor exosomes, which is meaningful for monitoring and early warning of the cancer. This work provides a reliable strategy for the detection of single molecule and a potential candidate for the practical bio-application of SERS technology. Raman scattering ; Molecule ; Materials science ; Surface plasmon resonance ; Black phosphorus ; Nanotechnology ; Raman spectroscopy ; Nanoparticle ; Detection limit ; Resonance (particle physics) ; Substrate (aquarium) ; Chemistry ; Optoelectronics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Extracellular vesicles in disease
Authors: Karama Asleh | Gian Luca Negri | Sandra E. Spencer Miko | Shane Colborne | Christopher S. Hughes | Xiu Q. Wang | Dongxia Gao | C. Blake Gilks | Stephen Chia | Torsten O. Nielsen | Gregg B. Morin
Source: Nature Communications (article, 2022-02-16)
Field: Chemistry — Spectroscopy

Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification. Breast cancer ; Immune system ; Proteomics ; Oncology ; Medicine ; Triple-negative breast cancer ; Cancer ; Bioinformatics ; Biology ; Computational biology ; Internal medicine ; Immunology ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Ferroptosis and cancer prognosis ; Cancer Genomics and Diagnostics
Authors: Jianyu Zhang | Parvej Alam | Siwei Zhang | Hanchen Shen | Lianrui Hu | Herman H. Y. Sung | Ian D. Williams | Jianwei Sun | Jacky W. Y. Lam | Haoke Zhang | Ben Zhong Tang
Source: Nature Communications (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Secondary through-space interactions facilitated single-molecule white-light emission from clusteroluminogens Clusteroluminogens refer to some non-conjugated molecules that show visible light and unique electronic properties with through-space interactions due to the formation of aggregates. Although mature and systematic theories of molecular photophysics have been developed to study conventional conjugated chromophores, it is still challenging to endow clusteroluminogens with designed photophysical properties by manipulating through-space interactions. Herein, three clusteroluminogens with non-conjugated donor-acceptor structures and different halide substituents are designed and synthesized. These compounds show multiple emissions and even single-molecule white-light emission in the crystalline state. The intensity ratio of these emissions is easily manipulated by changing the halide atom and excitation wavelength. Experimental and theoretical results successfully disclose the electronic nature of these multiple emissions: through-space conjugation for short-wavelength fluorescence, through-space charge transfer based on secondary through-space interactions for long-wavelength fluorescence, and room-temperature phosphorescence. The introduction of secondary through-space interactions to clusteroluminogens not only enriches their varieties of photophysical properties but also inspires the establishment of novel aggregate photophysics for clusteroluminescence. Conjugated system ; Chromophore ; Fluorescence ; Phosphorescence ; Photochemistry ; Molecule ; Chemical physics ; Acceptor ; Wavelength ; Materials science ; Chemistry ; Optoelectronics ; Physics ; Optics ; Polymer ; Organic chemistry Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Porphyrin and Phthalocyanine Chemistry
Authors: Mojtaba Haghighatlari | Jie Li | Xingyi Guan | Oufan Zhang | Akshaya Kumar Das | Christopher J. Stein | Farnaz Heidar‐Zadeh | Meili Liu | Martin Head‐Gordon | Luke W. Bertels | Hongxia Hao | Itai Leven | Teresa Head‐Gordon
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable force vectors, and physics-infused operators that are inspired by Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality <i>ab initio</i> data sets including single small molecules, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models. Newtonian fluid ; Computer science ; Classical mechanics ; Physics ; Mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andleeb Amin | Tanzeela Qadir | Praveen Kumar Sharma | Ishtiaq Jeelani | Hitoshi Abe
Source: The Open Medicinal Chemistry Journal (review, 2022-11-16)
Field: Chemistry — Organic Chemistry

A Review on The Medicinal And Industrial Applications of N-Containing Heterocycles Nitrogen-containing heterocycles constitute an important division of organic chemistry. The structural and functional diversity in nitrogen-containing heterocyclic compounds emanates from the presence and nature of the heteroatom that optimizes the compound for a specific application. Nitrogen heterocycles have been found to mimic various endogenous metabolites and natural products, highlighting their pivotal role in current drug design. Their applications are manifold and are predominantly used as pharmaceuticals, corrosion inhibitors, polymers, agrochemicals, dyes, developers, etc . Additionally, their catalytic behavior has rendered these compounds notable precursors in synthesizing various important organic compounds. The rate at which nitrogen heterocycles are synthesized explains this organic chemistry domain's vitality and usefulness. The present review article focuses on nitrogen-containing heterocycles as a versatile scaffold for current applications of organic chemistry. Heteroatom ; Chemistry ; Nitrogen ; Organic chemistry ; Organic synthesis ; Catalysis ; Combinatorial chemistry ; Biochemical engineering ; Nanotechnology ; Materials science ; Engineering Synthesis and Biological Evaluation ; Synthesis of heterocyclic compounds ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Jessica Ceramella | Domenico Iacopetta | Angelica Franchini | Michele De Luca | Carmela Saturnino | Inmaculada Andreu | Maria Stefania Sinicropi | Alessia Catalano
Source: Applied Sciences (article, 2022-10-27)
Field: Chemistry — Spectroscopy

A Look at the Importance of Chirality in Drug Activity: Some Significative Examples Chirality plays an important role in the development of many pharmaceuticals, being a general property of ‘handedness’; nevertheless, a large number of pharmaceuticals are still marketed and administered as racemates. Chirality is all around and even within us; indeed, receptors and enzymes are chiral entities and interact in a specific manner with chiral drugs. Consequently, controlling enantiomeric purity and isolating the enantiomers from chiral drugs remains a crucial subject for analytical, clinical, and regulatory purposes, thus, improving the drug safety profile. The classical examples of spontaneous enantiomerization and severe toxicity related to chirality are represented by ibuprofen and thalidomide, respectively, but numerous other cases have been reported in the literature. This review intends to offer a brief overview on the most common chiral drugs used in therapy for the treatment of various diseases. Chirality (physics) ; Enantiomer ; Drug ; Thalidomide ; Pharmacology ; Medicine ; Chemistry ; Stereochemistry ; Physics ; Internal medicine Analytical Chemistry and Chromatography ; Pharmacogenetics and Drug Metabolism ; Molecular spectroscopy and chirality
Authors: Philippe Schwaller | Alain C. Vaucher | Rubén Laplaza | Charlotte Bunne | Andreas Krause | Clémence Corminbœuf | Teodoro Laino
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2022-03-07)
Field: Materials Science — Materials Chemistry

Machine intelligence for chemical reaction space Abstract Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances and more sustainable processes. The current wave of machine intelligence is revolutionizing all data‐rich disciplines. Machine intelligence has emerged as a potential game‐changer for chemical reaction space exploration and the synthesis of novel molecules and materials. Herein, we will address the recent development of data‐driven technologies for chemical reaction tasks, including forward reaction prediction, retrosynthesis, reaction optimization, catalysts design, inference of experimental procedures, and reaction classification. Accurate predictions of chemical reactivity are changing the R&amp;D processes and, at the same time, promoting an accelerated discovery scheme both in academia and across chemical and pharmaceutical industries. This work will help to clarify the key contributions in the fields and the open challenges that remain to be addressed. This article is categorized under: Data Science &gt; Artificial Intelligence/Machine Learning Data Science &gt; Computer Algorithms and Programming Data Science &gt; Chemoinformatics Cheminformatics ; Chemical space ; Computer science ; Artificial intelligence ; Space (punctuation) ; Data science ; Retrosynthetic analysis ; Machine learning ; Inference ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Haobo Li | Yan Jiao | Kenneth Davey | Shi‐Zhang Qiao
Source: Angewandte Chemie International Edition (review, 2022-12-13)
Field: Materials Science — Materials Chemistry

Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts. Catalysis ; Computer science ; Artificial intelligence ; Materials science ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Jiayu Peng | Daniel Schwalbe‐Koda | Karthik Akkiraju | Tian Xie | Livia Giordano | Yang Yu | C. John Eom | Jaclyn R. Lunger | Daniel J. Zheng | Reshma R. Rao | Sokseiha Muy | Jeffrey C. Grossman | Karsten Reuter | Rafael Gómez‐Bombarelli | Yang Shao‐Horn
Source: Nature Reviews Materials (review, 2022-08-24)
Field: Materials Science — Materials Chemistry

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization Computer science ; Workflow ; Chemical space ; Throughput ; Data science ; Intuition ; Artificial intelligence ; Machine learning ; Drug discovery ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Aliaksei Mazheika | Yang‐Gang Wang | Rosendo Valero | Francesc Viñes | Francesc Illas | Luca M. Ghiringhelli | Sergey V. Levchenko | Matthias Scheffler
Source: Nature Communications (article, 2022-01-20)
Field: Materials Science — Materials Chemistry

Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO<sub>2</sub>) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO<sub>2</sub> conversion. Catalysis ; Chemical space ; Rational design ; Gene ; Constraint (computer-aided design) ; Materials science ; Chemistry ; Nanotechnology ; Computational biology ; Combinatorial chemistry ; Biology ; Drug discovery ; Mathematics ; Biochemistry Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Catalysis and Oxidation Reactions
Authors: Artem V. Tsymbal | Lorenzo Delarue Bizzini | David W. C. MacMillan
Source: Journal of the American Chemical Society (article, 2022-11-14)
Field: Chemistry — Organic Chemistry

Nickel Catalysis via S<sub>H</sub>2 Homolytic Substitution: The Double Decarboxylative Cross-Coupling of Aliphatic Acids Cross-coupling platforms are traditionally built around a sequence of closed-shell steps, such as oxidative addition, transmetalation, and reductive elimination. Herein, we describe a dual photo/nickel catalytic manifold that performs cross-coupling via a complementary sequence involving free radical generation, radical sorting via selective binding to a Ni(II) center, and bimolecular homolytic substitution (S<sub>H</sub>2) at a high-valent nickel-alkyl complex. This catalytic manifold enables the hitherto elusive cross-coupling of diverse aliphatic carboxylic acids to generate valuable C(sp<sup>3</sup>)-C(sp<sup>3</sup>)-products. Notably, the powerful S<sub>H</sub>2 mechanism provides general access to sterically encumbered quaternary carbon centers, addressing a long-standing challenge in fragment coupling chemistry. Chemistry ; Homolysis ; Steric effects ; Nickel ; Transmetalation ; Reductive elimination ; Catalysis ; Stereochemistry ; Substitution (logic) ; Radical ; Combinatorial chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Upare Abhay Atmaram | Selvaraj Mohana Roopan
Source: Applied Microbiology and Biotechnology (review, 2022-05-01)
Field: Chemistry — Organic Chemistry

Biological activity of oxadiazole and thiadiazole derivatives Oxadiazole ; Combinatorial chemistry ; Chemistry ; Common core ; Antimicrobial ; Computational biology ; Core (optical fiber) ; Computer science ; Biology ; Organic chemistry Synthesis and biological activity ; Synthesis and Biological Evaluation ; Structural and Chemical Analysis of Organic and Inorganic Compounds