jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Isabel Abánades Lázaro | Horațiu Szalad | Pablo Valiente | Josep Albero | Hermenegildo Garcı́a | Carlos Martí‐Gastaldo
Source: ACS Applied Materials & Interfaces (article, 2022-04-28)
Field: Chemistry — Inorganic Chemistry

Tuning the Photocatalytic Activity of Ti-Based Metal–Organic Frameworks through Modulator Defect-Engineered Functionalization Defect engineering is a valuable tool to tune the photocatalytic activity of metal-organic frameworks (MOFs). Inducing defects through the attachment of functionalized modulators can introduce cooperative units that can tune the bandgap of the material and enhance their chemical, thermal, and photostabilities among other properties. However, the majority of defect engineering studies for photocatalytic applications are limited to Zr-based MOFs, and there is still a lack of interrelation between synthetic variables, the resultant MOF properties, and their effect on their photocatalytic performance. We report a comprehensive study on the defect engineering of the titanium heterometallic MOF MUV-10 by fluoro- and hydroxy-isophthalic acid (Iso) modulators, rationalizing the effect of the materials' properties on their photocatalytic activity for hydrogen production. The Iso-OH modified MOFs present a volcano-type profile with a 2.3-fold increase in comparison to the pristine materials, whereas the Iso-F modified samples have a gradual increase with up to a 4.2-fold enhancement. It has been demonstrated that ∼9% of Iso-OH modulator incorporation produces ∼40% defects, inducing band gap reduction and longer excited states lifetime. Similar defect percentages have been generated upon near 40% Iso-F modulator incorporation; however, negligible band gap changes and shorter excited states lifetimes were determined. The higher photocatalytic activity in Iso-F modulator derived MOF has been attributed to the effect of the divergent defect-compensation modes on the materials' photostability and to the increase in the external surface area upon introduction of Iso-F modulator. Surface modification ; Materials science ; Photocatalysis ; Metal-organic framework ; Nanotechnology ; Metal ; Chemical engineering ; Metallurgy ; Organic chemistry ; Catalysis ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Wei Tan | Yandi Cai | Shaohua Xie | Juntian Xu | Kaili Ma | Kailong Ye | Lu Ma | Steven N. Ehrlich | Weixin Zou | Fei Gao | Lin Dong | Fudong Liu
Source: Chemical Engineering Journal (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

Constructing efficient CuO -CeO2 catalyst for NO reduction by CO: New insights into the structure–activity relationship Catalysis ; Raman spectroscopy ; Diffuse reflectance infrared fourier transform ; Materials science ; Adsorption ; Redox ; Fourier transform infrared spectroscopy ; Selectivity ; Chemical engineering ; In situ ; Chemistry ; Metallurgy ; Photocatalysis ; Physical chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Mohammadamin Tavakoli | Aaron Mood | David L. Van Vranken | Pierre Baldi
Source: Journal of Chemical Information and Modeling (review, 2022-01-12)
Field: Materials Science — Materials Chemistry

Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C-C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large data set of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated data set in combination with different representations of molecular structures. Using 10-fold cross-validation, we show that graph attention neural networks applied to a relational model of molecular structures produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* ± 3.0 or MAA* ± 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction and (2) combinatorial generation of reaction mechanisms. The curated data sets of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at cdb.ics.uci.edu under Chemical Reactivities data sets. Reactivity (psychology) ; Affinities ; Density functional theory ; Chemistry ; Solvation ; Computational chemistry ; Computer science ; Chemical space ; Molecule ; Drug discovery ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: H. Shaun Kwak | Yuling An | David J. Giesen | Thomas F. Hughes | Christopher T. Brown | Karl Leswing | Hadi Abroshan | Mathew D. Halls
Source: Frontiers in Chemistry (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials. These large-scale molecular simulations also demonstrate a rapid, cost-effective method to identify new materials in OLEDs while also enabling expansion into many other verticals such as catalyst design, aerospace, life science, and petrochemicals. Computer science ; Generative grammar ; Chemical space ; Artificial neural network ; Artificial intelligence ; Deep learning ; Aerospace ; OLED ; Reinforcement learning ; Throughput ; Computer architecture ; Nanotechnology ; Materials science ; Engineering ; Aerospace engineering ; Telecommunications ; Wireless ; Bioinformatics ; Layer (electronics) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Green IT and Sustainability
Authors: Henrik Levämäki | Ferenc Tasnádi | Davide G. Sangiovanni | L.J.S. Johnson | Rickard Armiento | Igor A. Abrikosov
Source: npj Computational Materials (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Predicting elastic properties of hard-coating alloys using ab-initio and machine learning methods Abstract Accelerated design of hard-coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models. We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials, such as binary and ternary nitrides. We use the high-throughput toolkit to automate the density functional theory calculation workflow. We present results, including elastic constants that are a key parameter determining mechanical properties of hard-coatings, for X 1− x Y x N ternary nitrides, where X , Y ∈ {Al, Ti, Zr, Hf} and fraction $$x=0,\frac{1}{4},\frac{1}{2},\frac{3}{4},1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>x</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:mfrac> <mml:mo>,</mml:mo> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> <mml:mo>,</mml:mo> <mml:mfrac> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:mfrac> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . We also explore ways for machine learning to support and complement the designed databases. We find that the crystal graph convolutional neural network trained on ordered lattices has sufficient accuracy for the disordered nitrides, suggesting that existing databases provide important data for predicting mechanical properties of qualitatively different types of materials, in our case disordered hard-coating alloys. Algorithm ; Computer science ; Machine learning ; Materials science ; Ternary operation ; Artificial intelligence ; Database Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Hao Tang | Yin Zhang | Qing‐Jie Li | Haowei Xu | Yuchi Wang | Yunzhi Wang | Ju Li
Source: Acta Materialia (article, 2022-07-30)
Field: Materials Science — Materials Chemistry

High accuracy neural network interatomic potential for NiTi shape memory alloy Nickel titanium ; Shape-memory alloy ; Materials science ; Pseudoelasticity ; Martensite ; Diffusionless transformation ; Interatomic potential ; Molecular dynamics ; Nanowire ; Density functional theory ; Work (physics) ; Condensed matter physics ; Metallurgy ; Thermodynamics ; Microstructure ; Nanotechnology ; Computational chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Susumu Fujii | Atsuto Seko
Source: Computational Materials Science (article, 2022-01-09)
Field: Materials Science — Materials Chemistry

Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics In silicon, lattice thermal conductivity plays an important role in a wide range of applications such as thermoelectric and microelectronic devices. Grain boundaries (GBs) in polycrystalline silicon can significantly reduce lattice thermal conductivity, but the impact of GB atomic structures on it remains to be elucidated. This study demonstrates accurate predictions of the GB structures, GB energies, and GB phonon properties in silicon using machine learning potentials (MLPs). The results indicate that the MLPs enable robust GB structure searches owing to the fact that the MLPs were developed from a training dataset covering a wide variety of structures. We also investigate lattice thermal conduction at four GB atomic structures using large-scale perturbed molecular dynamics and phonon wave-packet simulations. The comparison of these results indicates that the GB structure dependence of thermal conductivity stems from anharmonic vibrations at GBs rather than from the phonon transmission behavior at GBs. The advantages of the MLPs compared with a typical empirical potential of silicon are also thoroughly investigated. Phonon ; Thermal conductivity ; Silicon ; Materials science ; Anharmonicity ; Microelectronics ; Molecular dynamics ; Thermoelectric materials ; Grain boundary ; Condensed matter physics ; Thermal conduction ; Nanotechnology ; Optoelectronics ; Chemistry ; Computational chemistry ; Physics ; Microstructure ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Qingxiang Ji | Yunchao Qi | Chenwei Liu | Songhe Meng | Jun Liang | Muamer Kadic | Guodong Fang
Source: International Journal of Heat and Mass Transfer (article, 2022-02-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of thermal cloaks with isotropic materials based on machine learning Cloak ; Cloaking ; Metamaterial ; Isotropy ; Computer science ; Invisibility ; Thermal ; Artificial neural network ; Artificial intelligence ; Acoustics ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research
Authors: Wei Zhu | Jiebo Luo | Andrew Dickson White
Source: Patterns (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Federated learning of molecular properties with graph neural networks in a heterogeneous setting Chemistry research has both high material and computational costs to conduct experiments. Intuitions are interested in differing classes of molecules, creating heterogeneous data that cannot be easily joined by conventional methods. This work introduces federated heterogeneous molecular learning. Federated learning allows end users to build a global model collaboratively while keeping their training data isolated. We first simulate a heterogeneous federated-learning benchmark (FedChem) by jointly performing scaffold splitting and latent Dirichlet allocation on existing datasets. Our results on FedChem show that significant learning challenges arise when working with heterogeneous molecules across clients. We then propose a method to alleviate the problem: Federated Learning by Instance reweighTing (FLIT(+)). FLIT(+) can align local training across clients. Experiments conducted on FedChem validate the advantages of this method. This work should enable a new type of collaboration for improving artificial intelligence (AI) in chemistry that mitigates concerns about sharing valuable chemical data. Computer science ; Benchmark (surveying) ; Artificial intelligence ; Artificial neural network ; Machine learning ; Heterogeneous network ; Graph ; Data science ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Min Htoo Lin | Zhengkai Tu | Connor W. Coley
Source: Journal of Cheminformatics (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

Improving the performance of models for one-step retrosynthesis through re-ranking Retrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel machine learning approaches for data-driven synthesis planning. These methods learn complex patterns from reaction databases in order to predict, for a given product, sets of reactants that can be used to synthesise that product. However, their performance as measured by the top-N accuracy in matching published reaction precedents still leaves room for improvement. This work aims to enhance these models by learning to re-rank their reactant predictions. Specifically, we design and train an energy-based model to re-rank, for each product, the published reaction as the top suggestion and the remaining reactant predictions as lower-ranked. We show that re-ranking can improve one-step models significantly using the standard USPTO-50k benchmark dataset, such as RetroSim, a similarity-based method, from 35.7 to 51.8% top-1 accuracy and NeuralSym, a deep learning method, from 45.7 to 51.3%, and also that re-ranking the union of two models' suggestions can lead to better performance than either alone. However, the state-of-the-art top-1 accuracy is not improved by this method. Retrosynthetic analysis ; Ranking (information retrieval) ; Benchmark (surveying) ; Computer science ; Rank (graph theory) ; Artificial intelligence ; Machine learning ; Matching (statistics) ; Product (mathematics) ; Learning to rank ; Similarity (geometry) ; Data mining ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Julia Westermayr | Shayantan Chaudhuri | Andreas Jeindl | Oliver T. Hofmann | Reinhard J. Maurer
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an important role in their rational design. However, the rich diversity of molecular configurations and the important role of long-range interactions in such systems make it difficult to use machine learning (ML) potentials to facilitate structure exploration that otherwise requires computationally expensive electronic structure calculations. We present an ML approach that enables fast, yet accurate, structure optimizations by combining two different types of deep neural networks trained on high-level electronic structure data. The first model is a short-ranged interatomic ML potential trained on local energies and forces, while the second is an ML model of effective atomic volumes derived from atoms-in-molecules partitioning. The latter can be used to connect short-range potentials to well-established density-dependent long-range dispersion correction methods. For two systems, specifically gold nanoclusters on diamond (110) surfaces and organic π-conjugated molecules on silver (111) surfaces, we train models on sparse structure relaxation data from density functional theory and show the ability of the models to deliver highly efficient structure optimizations and semi-quantitative energy predictions of adsorption structures. Range (aeronautics) ; Dispersion (optics) ; Coupling (piping) ; Computer science ; Materials science ; Artificial intelligence ; Optics ; Physics ; Composite material Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Changxin Wang | Yan Zhang | Cheng Wen | Mingli Yang | Turab Lookman | Yanjing Su | Tong‐Yi Zhang
Source: Journal of Material Science and Technology (article, 2022-03-12)
Field: Materials Science — Materials Chemistry

Symbolic regression in materials science via dimension-synchronous-computation Symbolic regression ; Genetic programming ; Consistency (knowledge bases) ; Computer science ; Dimension (graph theory) ; Artificial intelligence ; Interpretation (philosophy) ; Feature (linguistics) ; Regression ; Computation ; Field (mathematics) ; Machine learning ; Regression analysis ; Energy (signal processing) ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: Rituparno Mandal | Corneel Casert | Peter Sollich
Source: Nature Communications (article, 2022-07-30)
Field: Materials Science — Materials Chemistry

Robust prediction of force chains in jammed solids using graph neural networks Force chains are quasi-linear self-organised structures carrying large stresses and are ubiquitous in jammed amorphous materials like granular materials, foams or even cell assemblies. Predicting where they will form upon deformation is crucial to describe the properties of such materials, but remains an open question. Here we demonstrate that graph neural networks (GNN) can accurately predict the location of force chains in both frictionless and frictional materials from the undeformed structure, without any additional information. The GNN prediction accuracy also proves to be robust to changes in packing fraction, mixture composition, amount of deformation, friction coefficient, system size, and the form of the interaction potential. By analysing the structure of the force chains, we identify the key features that affect prediction accuracy. Our results and methodology will be of interest for granular matter and disordered systems, e.g. in cases where direct force chain visualisation or force measurements are impossible. Deformation (meteorology) ; Computer science ; Scalability ; Biological system ; Materials science ; Particle (ecology) ; Statistical physics ; Physics ; Composite material ; Geology Material Dynamics and Properties ; Granular flow and fluidized beds ; Force Microscopy Techniques and Applications
Authors: Andrey A. Kistanov | S. A. Shcherbinin | Romain Botella | Artur R. Davletshin | Wei Cao
Source: The Journal of Physical Chemistry Letters (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Family of Two-Dimensional Transition Metal Dichlorides: Fundamental Properties, Structural Defects, and Environmental Stability A large number of novel two-dimensional (2D) materials are constantly being discovered and deposited in databases. Consolidated implementation of machine learning algorithms and density functional theory (DFT)-based predictions have allowed the creation of several databases containing an unimaginable number of 2D samples. As the next step in this chain, the investigation leads to a comprehensive study of the functionality of the invented materials. In this work, a family of transition metal dichlorides have been screened out for systematic investigation of their structural stability, fundamental properties, structural defects, and environmental stability via DFT-based calculations. The work highlights the importance of using the potential of the invented materials and proposes a comprehensive characterization of a new family of 2D materials. Stability (learning theory) ; Density functional theory ; Characterization (materials science) ; Transition metal ; Structural stability ; Work (physics) ; Materials science ; Computer science ; Nanotechnology ; Chemical physics ; Computational chemistry ; Chemistry ; Thermodynamics ; Physics ; Machine learning ; Engineering ; Catalysis ; Structural engineering 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Konstantinos Papastamatiou | Filippos Sofos | Theodoros E. Karakasidis
Source: AIP Advances (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Machine learning symbolic equations for diffusion with physics-based descriptions This work incorporates symbolic regression to propose simple and accurate expressions that fit to material datasets. The incorporation of symbolic regression in physical sciences opens the way to replace “black-box” machine learning techniques with representations that carry the physical meaning and can reveal the underlying mechanism in a purely data-driven approach. The application here is the extraction of analytical equations for the self-diffusion coefficient of the Lennard-Jones fluid by exploiting widely incorporating data from the literature. We propose symbolic formulas of low complexity and error that achieve better or comparable results to well-known microscopic and empirical expressions. Results refer to the material state space both as a whole and in distinct gas, liquid, and supercritical regions. Symbolic regression ; Computer science ; Diffusion ; Theoretical computer science ; Regression ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Genetic programming ; Thermodynamics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics