jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seongmin Kim | Jiaxin Xu | Wenjie Shang | Zhihao Xu | Eungkyu Lee | Tengfei Luo
Source: Progress in Energy (review, 2024-08-21)
Field: Materials Science — Materials Chemistry

A review on machine learning-guided design of energy materials Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome. Computer science ; Sustainability ; Energy (signal processing) ; Scarcity ; Systems engineering ; Efficient energy use ; Risk analysis (engineering) ; Industrial engineering ; Management science ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: V. A. Milyutin | Radovan Búreš | Mária Fáberová | Zuzana Birčáková | Zuzana Molčanová | B. Kunca | Л. А. Сташкова | P. Kollář | J. Füzer
Source: Journal of Materials Research and Technology (article, 2024-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning assisted optimization of soft magnetic properties in ternary Fe–Si–Al alloys The transition from the traditional “post-analysis” strategy for developing soft magnetic materials to an innovative “pre-design” one is highly desirable for the development of advanced electrical devices. In this work, we present the creation of a machine learning (ML) model capable of accurately predicting the soft magnetic properties (JS, HC, μ, ρ) of Fe–Si–Al alloys based on their composition. Through extensive ML experiments employing various algorithms commonly utilized in ML-assisted materials science, including SVM, RFR, KNR, XGB, and others, we achieved high accuracy in predictions, as indicated by R2 values close to 1. The best models were used to predict the properties of the 22800 FeSiAl alloys with the Al and Si content up to 15 wt % and step 0.1 %. Out of this vast compositional space, five alloys were selected for experimental validation, demonstrating the high quality of the predictions. The performance of ML models for specific properties is analyzed in terms of the nature of the distribution of the data used for training. In addition, some composition-properties correlations in the Fe–Si–Al system were analyzed and discussed. Materials science ; Ternary operation ; Metallurgy ; Nanotechnology ; Computer science Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloy Microstructure Properties
Authors: Fei-Teng Wang | Xiandong Liu | Jun Cheng
Source: Materials Futures (article, 2024-09-02)
Field: Materials Science — Materials Chemistry

Water structures and anisotropic dynamics at Pt(211)/water interface revealed by machine learning molecular dynamics Abstract Water molecules at solid–liquid interfaces play a pivotal role in governing interfacial phenomena that underpin electrochemical and catalytic processes. The organization and behavior of these interfacial water molecules can significantly influence the solvation of ions, the adsorption of reactants, and the kinetics of electrochemical reactions. The stepped structure of Pt surfaces can alter the properties of the interfacial water, thereby modulating the interfacial environment and the resulting surface reactivity. Revealing the in situ details of water structures at these stepped Pt/water interfaces is crucial for understanding the fundamental mechanisms that drive diverse applications in energy conversion and material science. In this work, we have developed a machine learning potential for the Pt(211)/water interface and performed machine learning molecular dynamics simulations. Our findings reveal distinct types of chemisorbed and physisorbed water molecules within the adsorbed layer. Importantly, we identified three unique water pairs that were not observed in the basal plane/water interfaces, which may serve as key precursors for water dissociation. These interfacial water structures contribute to the anisotropic dynamics of the adsorbed water layer. Our study provides molecular-level insights into the anisotropic nature of water behavior at stepped Pt/water interfaces, which can influence the reorientation and distribution of intermediates, molecules, and ions—crucial aspects for understanding electrochemical and catalytic processes. Dynamics (music) ; Molecular dynamics ; Interface (matter) ; Anisotropy ; Chemical physics ; Computer science ; Materials science ; Chemistry ; Physics ; Optics ; Computational chemistry ; Parallel computing Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Teng Yang | Zefeng Cai | Zhengtao Huang | Wenlong Tang | Ruosong Shi | A. Godfrey | Hanxing Liu | Yuan‐Hua Lin | Ce‐Wen Nan | Meng Ye | LinFeng Zhang | Ke Wang | Han Wang | Ben Xu
Source: Physical review. B./Physical review. B (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Deep learning illuminates spin and lattice interaction in magnetic materials Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the spin. However, accurately tracing the simultaneous evolution of both lattice and spin in magnetic materials at an atomic scale is a substantial challenge. This is largely due to the complexity involved in depicting the interaction energy precisely, and its influence on lattice and spin-driving forces, such as atomic forces and magnetic torques, which continues to be a daunting task in computational science. Addressing this deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torques in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials. Condensed matter physics ; Lattice (music) ; Materials science ; Physics Machine Learning in Materials Science ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Yilin Zheng | Zhuo Li | Zhigong Song
Source: Computer Methods in Applied Mechanics and Engineering (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

A generative machine learning model for the 3D reconstruction of material microstructure and performance evaluation Generative grammar ; Microstructure ; Artificial intelligence ; Machine learning ; Generative Design ; Computer science ; Generative model ; Engineering drawing ; Mechanical engineering ; Engineering ; Materials science ; Composite material Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Manufacturing Process and Optimization
Authors: Connor V. Headley | Roberto J. Herrera del Valle | Ji Ma | Prasanna V. Balachandran | Vijayabarathi Ponnambalam | Saniya LeBlanc | Dylan J. Kirsch | Joshua B. Martin
Source: Journal of Manufacturing Processes (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials Bismuth telluride ; Materials science ; Thermoelectric effect ; Process (computing) ; Fusion ; Thermoelectric materials ; Iterative and incremental development ; Mechanical engineering ; Computer science ; Composite material ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Sara Tkaczyk | Johannes Karwounopoulos | Andreas Schöller | H. Lee Woodcock | Thierry Langer | Stefan Boresch | Marcus Wieder
Source: Journal of Chemical Theory and Computation (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. <i>bioRxiv</i> 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement. Molecular dynamics ; Potential energy ; Outlier ; Statistical physics ; Energy (signal processing) ; Robustness (evolution) ; Perturbation (astronomy) ; Artificial neural network ; Work (physics) ; Free energy perturbation ; Computer science ; Physics ; Chemistry ; Classical mechanics ; Thermodynamics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Yujun Tao | Timothy J. Giese | Şölen Ekesan | Jinzhe Zeng | Bálint Aradi | B. Hourahine | Hasan Metin Aktulga | Andreas W. Götz | Kenneth M. Merz | Darrin M. York
Source: The Journal of Chemical Physics (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM-ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client-server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM-ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications. Quantum ; Interoperability ; Energy (signal processing) ; Software ; Computer science ; Mechanical energy ; Computational science ; Physics ; Quantum mechanics ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Liying Cui | Qing Li | Yanchang Zhang | Jianyu Zhang | Zhe Wang | Jiankang Chen | Bing Zheng
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted high-throughput screening of transparent organic light-emitting diode anode materials Securing optimal work functions for two-dimensional (2D) nanomaterials in Organic Light-Emitting Diodes (OLEDs) is crucial for enhancing the internal quantum efficiency of a device. However, the conventional approach to material discovery, which relies on empirical methods and iterative experimentation, is often time-consuming and inefficient. Here, we propose a target-driven material design framework that combines high-throughput virtual screening and interpretable machine learning (ML) to accelerate the discovery of transparent OLED anode materials. We developed an ML regression model (CatBoost), which accurately predicts work functions for 2D nanomaterials with a mean absolute error (MAE) of 0.20 eV. Remarkably, global and local model interpretation based on the SHapley Additive exPlanations (SHAP) method revealed that the space group is the decisive factor in work function prediction for most materials, while atomic-scale features of the material composition are the dominant factors for other materials, refreshing the traditional understanding of the nature of material work functions. Certain space groups (<i>Pmn</i>2_1 and <i>P</i>6̄<i>m</i>2) tend to exhibit relatively higher work functions (>7 eV), while some other space groups (<i>P</i>4/<i>mmm</i> and <i>P</i>1̄) often present relatively lower work functions (<4 eV). Our methodology, combining robust ML models, multi-condition screening, and DFT calculations, has identified a promising 2D nanomaterial-PS. The material demonstrates exceptional conductivity (<i>σ</i> > 10<sup>6</sup> S m<sup>-1</sup>), high transparency (transmittance > 90%), and favorable work function (>5 eV), significantly outperforming the commonly used indium tin oxide (ITO), emerging as a potential candidate for transparent OLED anodes. This study provides new insights into the intrinsic mechanisms affecting the work function of 2D nanomaterials, and provides a cost-effective design framework for identifying other high-performance materials. Anode ; OLED ; Throughput ; Materials science ; Optoelectronics ; Diode ; High-throughput screening ; Nanotechnology ; Computer science ; Electrode ; Telecommunications ; Chemistry ; Wireless Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Memory and Neural Computing
Authors: Türkan Altun | Hüseyin Ecevit
Source: Materials Chemistry and Physics (article, 2024-03-03)
Field: Materials Science — Biomaterials

Fabrication and characterization of Halloysite–Fe3O4–Ag nanocomposite as efficient catalyst for metronidazole degradation by using sodium borohydride: Artificial neural network modeling Sodium borohydride ; Halloysite ; Nanocomposite ; Catalysis ; Materials science ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Chemical engineering ; Borohydride ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Composite material Clay minerals and soil interactions ; Pharmacological Effects and Toxicity Studies ; Iron oxide chemistry and applications
Authors: Giulia Frigerio | Edoardo Donadoni | Paulo Siani | Jacopo Vertemara | Stefano Motta | Laura Bonati | Luca De Gioia | Cristiana Di Valentin
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Biomaterials

Mechanism of RGD-conjugated nanodevice binding to its target protein integrin α<sub>V</sub>β<sub>3</sub> by atomistic molecular dynamics and machine learning Active targeting strategies have been proposed to enhance the selective uptake of nanoparticles (NPs) by diseased cells, and recent experimental findings have proven the effectiveness of this approach. However, no mechanistic studies have yet revealed the atomistic details of the interactions between ligand-activated NPs and integrins. As a case study, here we investigate, by means of advanced molecular dynamics simulations (MD) and machine learning methods (namely equilibrium MD, binding free energy calculations and training of self-organized maps), the interaction of a cyclic-RGD-conjugated PEGylated TiO<sub>2</sub> NP (the nanodevice) with the extracellular segment of integrin α<sub>V</sub>β<sub>3</sub> (the target), the latter experimentally well-known to be over-expressed in several solid tumors. Firstly, we proved that the cyclic-RGD ligand binding to the integrin pocket is established and kept stable even in the presence of the cumbersome realistic model of the nanodevice. In this respect, the unsupervised machine learning analysis allowed a detailed comparison of the ligand/integrin binding in the presence and in the absence of the nanodevice, which unveiled differences in the chemical features. Then, we discovered that unbound cyclic RGDs conjugated to the NP largely contribute to the interactions between the nanodevice and the integrin. Finally, by increasing the density of cyclic RGDs on the PEGylated TiO<sub>2</sub> NP, we observed a proportional enhancement of the nanodevice/target binding. All these findings can be exploited to achieve an improved targeting selectivity and cellular uptake, and thus a more successful clinical outcome. Nanodevice ; Molecular dynamics ; Integrin ; Conjugated system ; Biophysics ; Ligand (biochemistry) ; Chemistry ; Materials science ; Nanotechnology ; Biochemistry ; Computational chemistry ; Biology ; Receptor Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Linke Huang | Zachary Gariepy | Ethan Halpren | Li Du | Chung Hsuan Shan | Chun Cheng Yang | Zhiwen Chen | Chandra Veer Singh
Source: Small Methods (article, 2024-11-10)
Field: Materials Science — Materials Chemistry

Bayesian Learning Aided Theoretical Optimization of IrPdPtRhRu High Entropy Alloy Catalysts for the Hydrogen Evolution Reaction The complex compositional space of high entropy alloys (HEAs) has shown a great potential to reduce the cost and further increase the catalytic activity for hydrogen evolution reaction (HER) by compositional optimization. Without uncovering the specifics of the HER mechanism on a given HEA surface, it is unfeasible to apply compositional modifications to enhance the performance and save costs. In this work, a combination of density functional theory and Bayesian machine learning is used to demonstrate the unique catalytic mechanism of IrPdPtRhRu HEA catalysts for HER. At high coverage of underpotential-deposited hydrogen, a d-band investigation of the active sites of the HEA surface is conducted to elucidate the superior catalytic performance through electronic interactions between elements. At low coverage, a novel Bayesian learning with oversampling approach is then outlined to optimize the HEA composition for performance improvement and cost reduction. This approach proves more efficacious and efficient and yields higher-quality structures with less training set bias compared with neural-network optimization. The proposed HEA optimization theoretically outperforms benchmark Pt catalysts' overpotential by ≈40% at a 15% reduced synthesis cost comparing to the equiatomic ratio HEA. Bayesian optimization ; Alloy ; Bayesian probability ; Catalysis ; Computer science ; Chemistry ; Artificial intelligence ; Materials science ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: Toshiaki Taniike | Felicia Daniela Cannavacciuolo | Mostafa Khoshsefat | Diego De Canditiis | Giuseppe Antinucci | Patchanee Chammingkwan | Roberta Cipullo | Vincenzo Busico
Source: ACS Catalysis (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler–Natta Propylene Polymerization Internal donors (IDs) play a decisive role in shaping the structure and performance of Ziegler–Natta catalyst formulations for isotactic polypropylene production. Unfortunately, their diverse and intricate functions remain elusive, and rational ID discovery, therefore, is still problematic. Exploitation of artificial intelligence methods such as machine learning, in turn, has been hindered by the lack of training data sets with adequate quality and size. This study proposes an integrated high-throughput workflow encompassing catalyst synthesis, propylene polymerization, and polypropylene characterization. Its application to an ID library of 35 molecules generated a robust and consistent data set, which highlighted important and intriguing quantitative structure–property relations (QSPRs). Furthermore, by fingerprinting ID molecular structure in combination with feature selection, a black box QSPR model correlating ID molecular structure and catalytic performance was successfully implemented. This study demonstrates that the combination of high-throughput experimentation and machine learning is a promising asset for accelerating the research and development of Ziegler–Natta catalysts. Natta ; Catalysis ; Throughput ; Tacticity ; Polymerization ; Polypropylene ; Workflow ; Materials science ; Rational design ; Biochemical engineering ; Computer science ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Polymer ; Engineering ; Database Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis