jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Donchev | Andrew G. Taube | Elizabeth Decolvenaere | Cory Hargus | Robert T. McGibbon | Ka-Hei Law | Brent A. Gregersen | Je-Luen Li | Kim Palmö | Karthik Siva | Michael Bergdorf | John L. Klepeis | David E. Shaw
Source: Scientific Data (article, 2021-02-10)
Field: Materials Science — Materials Chemistry

Quantum chemical benchmark databases of gold-standard dimer interaction energies Advances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods. Benchmark (surveying) ; Coupled cluster ; Wave function ; Quantum chemistry ; Computer science ; Cluster (spacecraft) ; Dimer ; Quantum ; Quantum chemical ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Junde Li | Rasit Onur Topaloglu | Swaroop Ghosh
Source: IEEE Transactions on Quantum Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Quantum Generative Models for Small Molecule Drug Discovery Existing drug discovery pipelines take 5–10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space, which could be on the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{60}$</tex-math></inline-formula> . Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative adversarial networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity toward binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from training instabilities. The practical utility of such models is limited due to the vast size of the search space, characterized by millions of parameters. A full quantum GAN may require more than 90 qubits even to generate small molecules with up to nine heavy atoms. The proposed quantum GAN with a hybrid generator (QGAN-HG) model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and a classical discriminator. The QGAN-HG with less than 20% of the original parameters can learn molecular distributions as efficiently as its classical counterpart. Another extended version of the proposed QGAN-HG, which utilizes multiple quantum subcircuits, considerably accelerates our standard QGAN-HG training process and avoids the potential gradient vanishing issue of deep neural networks. Chemical space ; Computer science ; Generator (circuit theory) ; Drug discovery ; Qubit ; Hidden variable theory ; Generative model ; Quantum ; Theoretical computer science ; Discriminator ; Generative grammar ; Statistical physics ; Artificial intelligence ; Quantum mechanics ; Physics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Jéssica Oliveira de Brito Lira | Humberto Gracher Riella | Natan Padoin | Cíntia Soares
Source: Chemical Engineering Journal (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Computational fluid dynamics (CFD), artificial neural network (ANN) and genetic algorithm (GA) as a hybrid method for the analysis and optimization of micro-photocatalytic reactors: NOx abatement as a case study Computational fluid dynamics ; Artificial neural network ; Genetic algorithm ; Light intensity ; Mean squared error ; Residence time (fluid dynamics) ; Biological system ; Computer science ; Algorithm ; Process engineering ; Simulation ; Engineering ; Mathematics ; Artificial intelligence ; Machine learning Catalytic Processes in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Solar-Powered Water Purification Methods
Authors: Chiara Bastiancich | Alessio Malfanti | Véronique Préat | Ruman Rahman
Source: Advanced Drug Delivery Reviews (review, 2021-08-27)
Field: Materials Science — Biomaterials

Rationally designed drug delivery systems for the local treatment of resected glioblastoma Glioblastoma (GBM) is a particularly aggressive brain cancer associated with high recurrence and poor prognosis. The standard of care, surgical resection followed by concomitant radio- and chemotherapy, leads to low survival rates. The local delivery of active agents within the tumor resection cavity has emerged as an attractive means to initiate oncological treatment immediately post-surgery. This complementary approach bypasses the blood-brain barrier, increases the local concentration at the tumor site while reducing or avoiding systemic side effects. This review will provide a global overview on the local treatment for GBM with an emphasis on the lessons learned from past clinical trials. The main parameters to be considered to rationally design fit-of-purpose biomaterials and develop drug delivery systems for local administration in the GBM resection cavity to prevent the tumor recurrence will be described. The intracavitary local treatment of GBM should i) use materials that facilitate translation to the clinic; ii) be characterized by easy GMP effective scaling up and easy-handling application by the neurosurgeons; iii) be adaptable to fill the tumor-resected niche, mold to the resection cavity or adhere to the exposed brain parenchyma; iv) be biocompatible and possess mechanical properties compatible with the brain; v) deliver a therapeutic dose of rationally-designed or repurposed drug compound(s) into the GBM infiltrative margin. Proof of concept with high translational potential will be provided. Finally, future perspectives to facilitate the clinical translation of the local perisurgical treatment of GBM will be discussed. Medicine ; Drug delivery ; Glioblastoma ; Brain tumor ; Chemotherapy ; Concomitant ; Temozolomide ; Drug ; Brain cancer ; Surgery ; Oncology ; Cancer ; Internal medicine ; Pharmacology ; Cancer research ; Pathology ; Nanotechnology Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment ; 3D Printing in Biomedical Research
Authors: Dávid Péter Kovács | William McCorkindale | Alpha A. Lee
Source: Nature Communications (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias Organic synthesis remains a major challenge in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models are making correct predictions because they inferred the salient chemistry, nor is it clear which training data they are relying on to reach a prediction. This opaqueness hinders both model developers and users. In this paper, we quantitatively interpret the Molecular Transformer, the state-of-the-art model for reaction prediction. We develop a framework to attribute predicted reaction outcomes both to specific parts of reactants, and to reactions in the training set. Furthermore, we demonstrate how to retrieve evidence for predicted reaction outcomes, and understand counterintuitive predictions by scrutinising the data. Additionally, we identify Clever Hans predictions where the correct prediction is reached for the wrong reason due to dataset bias. We present a new debiased dataset that provides a more realistic assessment of model performance, which we propose as the new standard benchmark for comparing reaction prediction models. Computer science ; Salient ; Machine learning ; Counterintuitive ; Artificial intelligence ; Benchmark (surveying) ; Set (abstract data type) ; Predictive modelling ; Interpretation (philosophy) ; Benchmarking ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Diptendu Roy | Shyama Charan Mandal | Biswarup Pathak
Source: ACS Applied Materials & Interfaces (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO<sub>2</sub> Hydrogenation to Methanol The revolutionary development of machine learning and data science and exploration of its application in material science are huge achievements of the scientific community in the past decade. In this work, we have reported an efficient approach of machine learning-aided high-throughput screening for finding selective earth-abundant high-entropy alloy-based catalysts for CO<sub>2</sub> to methanol formation using a machine learning algorithm and microstructure model. For this, we have chosen earth-abundant Cu, Co, Ni, Zn, and Mg metals to form various alloy-based compositions (bimetallic, trimetallic, tetrametallic, and high-entropy alloys) for selective CO<sub>2</sub> reduction reaction toward CH<sub>3</sub>OH. Since there are several possible surface microstructures for different alloys, we have used machine learning along with DFT calculations for high-throughput screening of the catalysts. In this study, the stability of various 8-atom fcc periodic (111) surface unit cells has been calculated using the atomic-size difference factor (δ) as well as the ratio taken from Gibbs free energy of mixing (Ω). Thinking about the simplicity and accuracy, microstructure models by considering the neighboring atoms of the adsorption sites and others as Cu atoms have been considered for different adsorption sites (on-top, bridge, and hollow-hcp). Moreover, the adsorption energies of the *H, *O, *CO, *HCO, *H<sub>2</sub>CO, and *H<sub>3</sub>CO intermediates have been predicted using the best fitted algorithm of the training set. The predicted adsorption energies have been screened based on the pure Cu adsorption energy. Furthermore, the screened catalysts have been correlated among different adsorption site microstructures. At the end, we were able to find seven active catalysts, among which two catalysts are CuCoNiZn-based tetrametallic, three catalysts are CuNiZn-based trimetallic, and two catalysts are CuCoZn-based trimetallic alloys. Hence, this work demonstrates not an ultimate but an efficient approach for finding new product-selective catalysts, and we expect that it can be convenient for other similar types of reactions in forthcoming days. Bimetallic strip ; Adsorption ; Materials science ; Catalysis ; Alloy ; Microstructure ; Atomic radius ; Gibbs free energy ; Atom (system on chip) ; Methanol ; Chemical engineering ; Physical chemistry ; Nanotechnology ; Metal ; Thermodynamics ; Metallurgy ; Computer science ; Chemistry ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Luis Enrique Vivanco-Benavides | Claudia Lizbeth Martínez‐González | C. Mercado-Zúñiga | C. Torres-Torres
Source: Computational Materials Science (review, 2021-10-11)
Field: Materials Science — Materials Chemistry

Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review Carbon nanotube ; Materials science ; Artificial neural network ; Computer science ; Materials informatics ; Artificial intelligence ; Machine learning ; Nanotechnology Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Letian Chen | Xu Zhang | An Chen | Sai Yao | Xu Hu | Zhen Zhou
Source: CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) (article, 2021-11-16)
Field: Materials Science — Materials Chemistry

Targeted design of advanced electrocatalysts by machine learning Electrocatalyst ; Computer science ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Nanotechnology ; Materials science ; Electrochemistry ; Engineering ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Victor García Satorras | Emiel Hoogeboom | Max Welling
Source: arXiv (Cornell University) (preprint, 2021-02-19)
Field: Materials Science — Materials Chemistry

E(n) Equivariant Graph Neural Networks This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance. In addition, whereas existing methods are limited to equivariance on 3 dimensional spaces, our model is easily scaled to higher-dimensional spaces. We demonstrate the effectiveness of our method on dynamical systems modelling, representation learning in graph autoencoders and predicting molecular properties. Equivariant map ; Graph ; Computer science ; Representation (politics) ; Artificial neural network ; Theoretical computer science ; Contrast (vision) ; Artificial intelligence ; Algorithm ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Protein Structure and Dynamics
Authors: Maria Marinescu
Source: Antibiotics (review, 2021-08-19)
Field: Chemistry — Organic Chemistry

Synthesis of Antimicrobial Benzimidazole–Pyrazole Compounds and Their Biological Activities The synthesis of new compounds with antimicrobial and antiviral properties is a central objective today in the context of the COVID-19 pandemic. Benzimidazole and pyrazole compounds have remarkable biological properties, such as antimicrobial, antiviral, antitumor, analgesic, anti-inflammatory, anti-Alzheimer's, antiulcer, antidiabetic. Moreover, recent literature mentions the syntheses and antimicrobial properties of some benzimidazole-pyrazole hybrids, as well as other biological properties thereof. In this review, we aim to review the methods of synthesis of these hybrids, the antimicrobial activities of the compounds, their correlation with various groups present on the molecule, as well as their pharmaceutical properties. Antimicrobial ; Benzimidazole ; Pyrazole ; Context (archaeology) ; Combinatorial chemistry ; Chemistry ; Biological activity ; Organic chemistry ; Biology ; Biochemistry ; In vitro Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Synthesis and Biological Evaluation
Authors: Reza Peymanfar | Shahrzad Javanshir | Mohammad Reza Naimi‐Jamal | Seyed Hassan Tavassoli
Source: Journal of Materials Science (review, 2021-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Morphology and medium influence on microwave characteristics of nanostructures: A review Microwave ; Materials science ; Attenuation ; Scattering ; Electromagnetic radiation ; Permittivity ; Cloaking ; Reflection loss ; Nanostructure ; Metamaterial ; Optoelectronics ; Nanotechnology ; Engineering physics ; Optics ; Computer science ; Telecommunications ; Dielectric ; Physics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Kiumars Aryana | John T. Gaskins | Joyeeta Nag | Derek A. Stewart | Zhaoqiang Bai | Saikat Mukhopadhyay | John C. Read | David H. Olson | Eric R. Hoglund | James M. Howe | Ashutosh Giri | Michael K. Grobis | Patrick E. Hopkins
Source: Nature Communications (article, 2021-02-03)
Field: Materials Science — Materials Chemistry

Interface controlled thermal resistances of ultra-thin chalcogenide-based phase change memory devices Phase change memory (PCM) is a rapidly growing technology that not only offers advancements in storage-class memories but also enables in-memory data processing to overcome the von Neumann bottleneck. In PCMs, data storage is driven by thermal excitation. However, there is limited research regarding PCM thermal properties at length scales close to the memory cell dimensions. Our work presents a new paradigm to manage thermal transport in memory cells by manipulating the interfacial thermal resistance between the phase change unit and the electrodes without incorporating additional insulating layers. Experimental measurements show a substantial change in interfacial thermal resistance as GST transitions from cubic to hexagonal crystal structure, resulting in a factor of 4 reduction in the effective thermal conductivity. Simulations reveal that interfacial resistance between PCM and its adjacent layer can reduce the reset current for 20 and 120 nm diameter devices by up to ~ 40% and ~ 50%, respectively. These thermal insights present a new opportunity to reduce power and operating currents in PCMs. Phase-change memory ; Thermal ; Reset (finance) ; Thermal resistance ; Phase-change material ; Interfacial thermal resistance ; Work (physics) ; Power (physics) ; Thermal conductivity Phase-change materials and chalcogenides ; Transition Metal Oxide Nanomaterials ; Chemical and Physical Properties of Materials
Authors: Haoxin Huang | Jiajia Zha | Shisheng Li | Chaoliang Tan
Source: Chinese Chemical Letters (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

Two-dimensional alloyed transition metal dichalcogenide nanosheets: Synthesis and applications Nanotechnology ; Materials science ; Characterization (materials science) ; Transition metal ; Catalysis ; Electronics ; Chemistry ; Physical chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Maria Korshunova | Boris Ginsburg | Alexander Tropsha | Olexandr Isayev
Source: Journal of Chemical Information and Modeling (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design Deep learning models have demonstrated outstanding results in many data-rich areas of research, such as computer vision and natural language processing. Currently, there is a rise of deep learning in computational chemistry and materials informatics, where deep learning could be effectively applied in modeling the relationship between chemical structures and their properties. With the immense growth of chemical and materials data, deep learning models can begin to outperform conventional machine learning techniques such as random forest, support vector machines, and nearest neighbor. Herein, we introduce OpenChem, a PyTorch-based deep learning toolkit for computational chemistry and drug design. OpenChem offers easy and fast model development, modular software design, and several data preprocessing modules. It is freely available via the GitHub repository. Deep learning ; Computer science ; Artificial intelligence ; Cheminformatics ; Modular design ; Preprocessor ; Machine learning ; Drug discovery ; Informatics ; Data science ; Chemistry ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Yuanyuan Jiang | Zongwei Yang | Jiali Guo | Hongzhen Li | Yijing Liu | Yanzhi Guo | Menglong Li | Xuemei Pu
Source: Nature Communications (article, 2021-10-12)
Field: Chemistry — Physical and Theoretical Chemistry

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π-π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community. Cocrystal ; Robustness (evolution) ; Computer science ; Generalization ; Graph ; Crystallization ; Machine learning ; Theoretical computer science ; Chemistry ; Molecule ; Mathematics ; Hydrogen bond Crystallography and molecular interactions ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Matthew J. McDermott | Shyam Dwaraknath | Kristin A. Persson
Source: Nature Communications (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable "synthesis by design" in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO<sub>3</sub>, Y<sub>2</sub>Mn<sub>2</sub>O<sub>7</sub>, Fe<sub>2</sub>SiS<sub>4</sub>, and YBa<sub>2</sub>Cu<sub>3</sub>O<sub>6.5</sub>. The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials. Thermochemistry ; Computer science ; Graph ; Solid-state ; Chemistry ; Biological system ; Theoretical computer science ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions