jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yicheng Chen | Wenjie Yan | Zhanfeng Wang | Jianming Wu | Xin Xu
Source: Journal of Chemical Theory and Computation (article, 2024-10-31)
Field: Materials Science — Materials Chemistry

Constructing Accurate and Efficient General-Purpose Atomistic Machine Learning Model with Transferable Accuracy for Quantum Chemistry Density functional theory (DFT) has been a cornerstone in computational science, providing powerful insights into structure-property relationships for molecules and materials through first-principles quantum-mechanical (QM) calculations. However, the advent of atomistic machine learning (ML) is reshaping the landscape by enabling large-scale dynamics simulations and high-throughput screening at DFT-equivalent accuracy with drastically reduced computational cost. Yet, the development of general-purpose atomistic ML models as surrogates for QM calculations faces several challenges, particularly in terms of model capacity, data efficiency, and transferability across chemically diverse systems. This work introduces a novel extension of the polarizable atom interaction neural network (namely, XPaiNN) to address these challenges. Two distinct training strategies have been employed, one direct-learning and the other Δ-ML on top of a semiempirical QM method. These methodologies have been implemented within the same framework, allowing for a detailed comparison of their results. The XPaiNN models, in particular the one using Δ-ML, not only demonstrate competitive performance on standard benchmarks, but also demonstrate the effectiveness against other ML models and QM methods on comprehensive downstream tasks, including noncovalent interactions, reaction energetics, barrier heights, geometry optimization and reaction thermodynamics, etc. This work represents a significant step forward in the pursuit of accurate and efficient atomistic ML models of general-purpose, capable of handling complex chemical systems with transferable accuracy. Computer science ; Quantum chemistry ; Quantum ; Machine learning ; Data science ; Chemistry ; Physics ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Haochen Shi | Yiming Shi | Zhiqin Liang | Suling Zhao | Bo Qiao | Zheng Xu | Lijuan Wang | Dandan Song
Source: Chemical Engineering Journal (article, 2024-06-14)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning-enabled discovery of multi-resonance TADF molecules: Unraveling PLQY predictions from molecular structures Resonance (particle physics) ; Molecule ; Materials science ; Nanotechnology ; Chemical physics ; Chemistry ; Physics ; Atomic physics ; Organic chemistry Photochemistry and Electron Transfer Studies ; Receptor Mechanisms and Signaling ; Computational Drug Discovery Methods
Authors: Yuncheng Du | Dongping Du
Source: Materials Letters (article, 2024-11-19)
Field: Chemistry — Inorganic Chemistry

Bayesian optimization enhanced neural networks for predicting metal-organic framework morphology: A ZIF-8 synthesis case study Materials science ; Metal-organic framework ; Bayesian optimization ; Artificial neural network ; Morphology (biology) ; Bayesian network ; Biological system ; Nanotechnology ; Chemical engineering ; Artificial intelligence ; Computer science ; Organic chemistry ; Engineering ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Chemical Synthesis and Reactions
Authors: Joshua Ojih | Mohammed Al‐Fahdi | Yagang Yao | Jianjun Hu | Ming Hu
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage Prediction of crystal structures with desirable material properties is a grand challenge in materials research. We deployed graph theory assisted structure searcher and combined with universal machine learning potentials to accelerate the process. Crystal structure prediction ; Computer science ; Graph ; Graph theory ; Throughput ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Crystal structure ; Mathematics ; Chemistry ; Combinatorics ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Patrizia Mazzeo | Edoardo Cignoni | Amanda Arcidiacono | Lorenzo Cupellini | Benedetta Mennucci
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules We propose a strategy to perform electrostatic embedding machine learning (ML)/molecular mechanics (MM) molecular dynamics simulations. Excited state ; Ground state ; Embedding ; Molecule ; Molecular dynamics ; Chemical physics ; Dynamics (music) ; Physics ; Computer science ; Chemistry ; Computational chemistry ; Atomic physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Seung‐Hyun Victor Oh | Su‐Hyun Yoo | Woosun Jang
Source: npj Computational Materials (article, 2024-07-30)
Field: Materials Science — Materials Chemistry

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting Abstract Aiming toward a sustainable energy era, the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied. One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying. However, the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space, providing an opportunity for machine learning (ML) approaches to help accelerate the discovery of new multicomponent alloy materials. A conventional prerequisite for ML approaches is a large database of accurate material properties, which may require exhaustive computational and/or experimental resources. This study demonstrates that the screening of solid-solution alloys (up to hexanary systems) can be performed using a small database to minimize (and optimize) the number of high-level computational calculations. Specifically, we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method ( α -method). Furthermore, we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions. Computer science ; Entropy (arrow of time) ; High entropy alloys ; Alloy ; Space (punctuation) ; Process engineering ; Materials science ; Engineering ; Thermodynamics ; Physics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Photocatalysis Techniques
Authors: Chengran Chai | Yang Wang | Shuai Zhao | Yuanxiang Zhang | Feng Fang | Peng Lin | Xiaoming Zhang
Source: Computational Materials Science (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Machine learning-assisted design of low elastic modulus β-type medical titanium alloys and experimental validation Materials science ; Elastic modulus ; Titanium ; Titanium alloy ; Type (biology) ; Composite material ; Modulus ; Metallurgy ; Geology ; Alloy Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Additive Manufacturing Materials and Processes
Authors: Mingying Lu | Haining Ji | Yongxing Chen | Fenglin Gao | Bin Liu | Long Peng | Cong Deng | Yi Wang | Jundong Tao
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted layer-controlled synthesis of MoS<sub>2</sub> Machine learning, one of the most powerful data-driven methods, has been developed to optimize layer-controlled synthesis of MoS 2 for electronic and optoelectronic devices. Materials science ; Layer (electronics) ; Nanotechnology ; Optoelectronics 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Iradat Hussain Mafat | Dadi Venkata Surya | Sumeet K. Sharma | Chinta Sankar Rao
Source: Journal of Analytical and Applied Pyrolysis (review, 2024-04-25)
Field: Chemistry — Analytical Chemistry

Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: A comprehensive review Biomass (ecology) ; Petroleum ; Biochemical engineering ; Computer science ; Process engineering ; Environmental science ; Pyrolysis oil ; Pyrolysis ; Machine learning ; Artificial intelligence ; Waste management ; Chemistry ; Engineering ; Organic chemistry ; Geology Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Petroleum Processing and Analysis
Authors: Diva Addini Maghribi Muyassiroh | Fitri Aulia Permatasari | Tomoyuki Hirano | Takashi Ogi | Ferry Iskandar
Source: ACS Applied Nano Materials (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Synthesis of Room-Temperature Phosphorescent Carbon Dots for Enhanced Phosphorescence Lifetime and Information Encryption Room-temperature phosphorescent (RTP) carbon dots (CDs) have been increasingly used in many applications, including anticounterfeiting and information encryption. However, synthesizing RTP CDs with a specific average lifetime of phosphorescence remains a formidable challenge. A breakthrough is needed in formulating the synthesis process to find a suitable synthesis formulation to produce CDs with an optimal lifetime of phosphorescence. Machine learning (ML) has recently been successfully used for guiding material synthesis and offering insight into the prediction, optimization, and acceleration of the CDs’ synthesis process. A regression ML model on microwave-assisted CD synthesis is established to reveal the relationship between various synthesis parameters and enhance the average lifetime of phosphorescence of CDs in the solid-state phase. RTP CDs exhibit a blue emission when irradiating with UV and a green emission afterglow after the UV is turned off. These green emissions can last for 7 s, are easily observed by the naked eye, and show an ultralong phosphorescence lifetime of up to 1.6 s. Moreover, designed and guided by ML, this afterglow feature was explored to achieve multilevel anticounterfeiting and information encryption to encrypt and decrypt secret information in dynamic time-dependent displays. Our results provide a strategy for synthesizing RTP CDs with a specific lifetime and extending their application scope to high-level information security. Phosphorescence ; Afterglow ; Encryption ; Materials science ; Nanoclusters ; Computer science ; Naked eye ; Nanotechnology ; Optics ; Physics ; Computer network ; Gamma-ray burst Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Julia Santana-Andreo | Antonio M. Márquez | José J. Plata | Ernesto J. Blancas | José‐Luis González‐Sánchez | Javier Fdez. Sanz | Pinku Nath
Source: ACS Applied Materials & Interfaces (article, 2024-01-22)
Field: Materials Science — Materials Chemistry

High-Throughput Prediction of the Thermal and Electronic Transport Properties of Large Physical and Chemical Spaces Accelerated by Machine Learning: Charting the <i>ZT</i> of Binary Skutterudites Thermal and electronic transport properties are the keys to many technological applications of materials. Thermoelectric, TE, materials can be considered a singular case in which not only one but three different transport properties are combined to describe their performance through their TE figure of merit, <i>ZT</i>. Despite the availability of high-throughput experimental techniques, synthesizing, characterizing, and measuring the properties of samples with numerous variables affecting <i>ZT</i> are not a cost- or time-efficient approach to lead this strategy. The significance of computational materials science in discovering new TE materials has been running in parallel to the development of new frameworks and methodologies to compute the electron and thermal transport properties linked to <i>ZT</i>. Nevertheless, the trade-off between computational cost and accuracy has hindered the reliable prediction of TE performance for large chemical spaces. In this work, we present for the first time the combination of new ab initio methodologies to predict transport properties with machine learning and a high-throughput framework to establish a solid foundation for the accurate prediction of thermal and electron transport properties. This strategy is applied to a whole family of materials, binary skutterudites, which are well-known as good TE candidates. Following this methodology, it is possible not only to connect <i>ZT</i> with the experimental synthetic (carrier concentration and grain size) and operando (temperature) variables but also to understand the physical and chemical phenomena that act as driving forces in the maximization of <i>ZT</i> for p-type and n-type binary skutterudites. Materials science ; Binary number ; Throughput ; Thermoelectric materials ; Thermal conductivity ; Ab initio ; Thermal ; Computer science ; Figure of merit ; Machine learning ; Thermodynamics ; Physics ; Mathematics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Peng‐Fei Hou | Qi Yu | Feng Luo | Jincheng Liu
Source: ACS Catalysis (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Reactant-Induced Dynamic Active Sites on Cu Catalysts during the Water–Gas Shift Reaction Adsorbates can trigger surface reconstruction on metal surfaces, a common yet highly important phenomenon in heterogeneous catalysis that has not been fully explored. Here, we develop a reliable Cu–C–O machine learning force field (MLFF) with ab initio accuracy, providing insights into the reconstruction mechanism and distribution of active sites on the Cu surface under a CO atmosphere through state-of-the-art deep potential molecular dynamics (DPMD). Combining statistical cluster analysis with microkinetic modeling, we establish a strategy to quantitatively assess the turnover frequency (TOF) of catalyst surfaces during the dynamic catalytic process. Our findings reveal that edge Cu atoms undergo rearrangement, ejection, diffusion, and aggregation under a CO atmosphere, leading to the formation of cluster active sites. These small clusters in dynamic equilibrium are identified as the origin of the high catalytic activity of Cu-based catalysts for a low-temperature water–gas shift reaction (WGSR). This work not only elucidates intrinsic activity in metal catalysis and the dynamic catalysis theory but also offers valuable insights for computational catalysis methods to identify effective catalysts for practical applications. Catalysis ; Chemistry ; Water-gas shift reaction ; Heterogeneous catalysis ; Molecular dynamics ; Cluster (spacecraft) ; Chemical physics ; Active site ; Computational chemistry ; Nanotechnology ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Jing Zhang | Tianshu Zhai | Faizal Arifurrahman | Yuguo Wang | Andrew Hitt | Zelai He | Qing Ai | Yifeng Liu | Chen-yang Lin | Yifan Zhu | Ming Tang | Jun Lou
Source: Nano Letters (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real-Time Crystal Morphology Evolutions The rich morphology of 2D materials grown through chemical vapor deposition (CVD), is a distinctive feature. However, understanding the complex growth of 2D crystals under practical CVD conditions remains a challenge due to various intertwined factors. Real-time monitoring is crucial to providing essential data and enabling the use of advanced tools like machine learning for unraveling these complexities. In this study, we present a custom-built miniaturized CVD system capable of observing and recording 2D MoS<sub>2</sub> crystal growth in real time. Image processing converts the real-time footage into digital data, and machine learning algorithms (ML) unveil the significant factors influencing growth. The machine learning model successfully predicts CVD growth parameters for synthesizing ultralarge monolayer MoS<sub>2</sub> crystals. It also demonstrates the potential to reverse engineer CVD growth parameters by analyzing the as-grown 2D crystal morphology. This interdisciplinary approach can be integrated to enhance our understanding of controlled 2D crystal synthesis through CVD. Chemical vapor deposition ; Nanotechnology ; Monolayer ; Crystal (programming language) ; Crystal growth ; Materials science ; Morphology (biology) ; Feature (linguistics) ; Computer science ; Artificial intelligence ; Biological system ; Chemistry ; Crystallography ; Geology 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Alessio Fallani | Leonardo Medrano Sandonas | Alexandre Tkatchenko
Source: Nature Communications (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Inverse mapping of quantum properties to structures for chemical space of small organic molecules Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the inverse mapping in chemical space remain elusive. We address this challenge by demonstrating the possibility of parametrizing a chemical space with a finite set of QM properties. Our proof-of-concept implementation achieves an approximate property-to-structure mapping, the QIM model (which stands for "Quantum Inverse Mapping"), by forcing a variational auto-encoder with a property encoder to obtain a common internal representation for both structures and properties. After validating this mapping for small drug-like molecules, we illustrate its capabilities with an explainability study as well as by the generation of de novo molecular structures with targeted properties and transition pathways between conformational isomers. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces. Chemical space ; Inverse ; Quantum chemical ; Representation (politics) ; Computer science ; Space (punctuation) ; Property (philosophy) ; Quantum ; Set (abstract data type) ; Molecule ; Computational chemistry ; Theoretical computer science ; Topology (electrical circuits) ; Chemistry ; Physics ; Mathematics ; Drug discovery ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Antonios P. Sarikas | Konstantinos Gkagkas | George E. Froudakis
Source: Scientific Reports (article, 2024-01-26)
Field: Chemistry — Inorganic Chemistry

Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks Intrinsic properties of metal-organic frameworks (MOFs), such as their ultra porosity and high surface area, deem them promising solutions for problems involving gas adsorption. Nevertheless, due to their combinatorial nature, a huge number of structures is feasible which renders cumbersome the selection of the best candidates with traditional techniques. Recently, machine learning approaches have emerged as efficient tools to deal with this challenge, by allowing researchers to rapidly screen large databases of MOFs via predictive models. The performance of the latter is tightly tied to the mathematical representation of a material, thus necessitating the use of informative descriptors. In this work, a generalized framework to predict gaseous adsorption properties is presented, using as one and only descriptor the capstone of chemical information: the potential energy surface (PES). In order to be machine understandable, the PES is voxelized and subsequently a 3D convolutional neural network (CNN) is exploited to process this 3D energy image. As a proof of concept, the proposed pipeline is applied on predicting [Formula: see text] uptake in MOFs. The resulting model outperforms a conventional model built with geometric descriptors and requires two orders of magnitude less training data to reach a given level of performance. Moreover, the transferability of the approach to different host-guest systems is demonstrated, examining [Formula: see text] uptake in COFs. The generic character of the proposed methodology, inherited from the PES, renders it applicable to fields other than reticular chemistry. Computer science ; Convolutional neural network ; Adsorption ; Pipeline (software) ; Artificial intelligence ; Machine learning ; Surface (topology) ; Algorithm ; Representation (politics) ; Metal-organic framework ; Deep learning ; Energy (signal processing) ; Artificial neural network ; Chemistry ; Mathematics ; Physical chemistry ; Geometry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: B. W. Robertson | R. Sapna | Vinod Hegde | K. Hareesh
Source: RSC Advances (review, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A comprehensive review on MoSe<sub>2</sub> nanostructures with an overview of machine learning techniques for supercapacitor applications In the past few decades, supercapacitors (SCs) have emerged as good and reliable energy storage devices due to their impressive power density, better charge-discharge rates, and high cycling stability. The main components of a supercapacitor are its electrode design and composition. Many compositions are tested for electrode preparations, which can provide good performance. Still, research is widely progressing in developing optimum high-performance electrodes. Metal chalcogenides have recently gained a lot of interest for application in supercapacitors due to their intriguing physical and chemical properties, unique crystal structures, tuneable interlayer spacings, broad oxidation states, <i>etc.</i> MoSe<sub>2</sub>, belonging to the family of Transition Metal Dichalcogenides (TMDs), has also been well explored recently for application in supercapacitors due to its similar properties to 2D materials. In this review, we briefly discuss supercapacitors and their classification. Various available synthesis routes for MoSe<sub>2</sub> preparation are summarized. A detailed assessment of the electrochemical performances of different MoSe<sub>2</sub> composites, including cyclic voltammetry (CV) analysis and galvanostatic charge-discharge (GCD) analysis, is given for symmetric and asymmetric supercapacitors. The limitations of MoSe<sub>2</sub> and its composites are mentioned briefly. The use of machine learning methods and algorithms for supercapacitor applications is discussed for forecasting valuable details. Finally, a summary is provided, along with conclusions. Supercapacitor ; Nanotechnology ; Nanostructure ; Materials science ; Computer science ; Chemistry ; Electrochemistry ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications