jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vincenzo Fazio | Nicola M. Pugno | Orazio Giustolisi | Giuseppe Puglisi
Source: Cell Reports Physical Science (article, 2024-01-30)
Field: Materials Science — Biomaterials

Physically based machine learning for hierarchical materials In multiscale phenomena, complex structure-function relationships emerge across different scales, making predictive modeling challenging. The recent scientific literature is exploring the possibility of leveraging machine learning, with a predominant focus on neural networks, excelling in data fitting, but often lacking insight into essential physical information. We propose the adoption of a symbolic data modeling technique, the “Evolutionary Polynomial Regression,” which integrates regression capabilities with the genetic programming paradigm, enabling the derivation of explicit analytical formulas, finally delivering a deeper comprehension of the analyzed physical phenomenon. To demonstrate the key advantages of our multiscale numerical approach, we consider the spider silk case. Based on a recent multiscale experimental dataset, we deduce the dependence of the macroscopic behavior from lower-scale parameters, also offering insights for improving a recent theoretical model by some of the authors. Our approach may represent a proof of concept for modeling in fields governed by multiscale, hierarchical differential equations. Computer science ; Artificial intelligence ; Machine learning Silk-based biomaterials and applications ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Zhicheng Li | Jing Zhong | Xingsong Jiang | Zhangxi Wang | Lijun Zhang
Source: Virtual and Physical Prototyping (article, 2024-06-19)
Field: Materials Science — Materials Chemistry

Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introducing a machine learning model that effectively predicted the performance of NiTi SMAs across diverse LPBF processing and equipment conditions. Trained on a dataset of 195 entries from 23 publications, the model accurately predicted critical metrics, including density, ultimate tensile strength, elongation, and thermal hysteresis. Validation using data from eight experimental groups confirmed its reliability and generalisation capability. Multi-objective optimisation identified processes yielding synergistic improvements, achieving a tensile strength of 783[Formula: see text]8 MPa, an elongation of 13.7[Formula: see text]0.8% and a low hysteresis of 15.1 K. This study also discussed strategic applications of the model for LPBF process optimisation and proposed a method for constructing tailored LPBF process maps for specific NiTi alloy performance attributes. Nickel titanium ; Shape-memory alloy ; Process (computing) ; Materials science ; Engineering drawing ; Computer science ; Artificial intelligence ; Mechanical engineering ; Process engineering ; Machine learning ; Engineering Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes
Authors: Sachin Poudel | Rubi Thapa | Rabin Basnet | Anna Timofiejczuk | Anil Kunwar
Source: Applied Energy (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning Scale (ratio) ; Piezoelectricity ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Composite material ; Physics Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Thermography and Photoacoustic Techniques
Authors: Sicong Ma | Yanwei Cao | Yunfei Shi | Cheng Shang | Lin He | Zhi‐Pan Liu
Source: Chemical Science (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Data-driven discovery of active phosphine ligand space for cross-coupling reactions The design of highly active catalysts is a main theme in organic chemistry, but it still relies heavily on expert experience. Herein, powered by machine-learning global structure exploration, we forge a Metal-Phosphine Catalyst Database (MPCD) with a meticulously designed ligand replacement energy metric, a key descriptor to describe the metal-ligand interactions. It pushes the rational design of organometallic catalysts to a quantitative era, where a ±10 kJ mol<sup>-1</sup> window of relative ligand binding strength, a so-called active ligand space (ALS), is identified for highly effective catalyst screening. We highlight the chemistry interpretability and effectiveness of ALS for various C-N, C-C and C-S cross-coupling reactions <i>via</i> a Sabatier-principle-based volcano plot and demonstrate its predictive power in discovering low-cost ligands in catalyzing Suzuki cross-coupling involving aryl chloride. The advent of the MPCD provides a data-driven new route for speeding up organometallic catalysis and other applications. Phosphine ; Ligand (biochemistry) ; Coupling reaction ; Combinatorial chemistry ; Aryl ; Catalysis ; Chemistry ; Inert ; Organic chemistry ; Biochemistry Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods
Authors: Neda Azimi | Esmail Sharifzadeh
Source: Computational Materials Science (article, 2024-09-30)
Field: Materials Science — Polymers and Plastics

Improved aggregation/agglomeration-dependent percolation theory to predict nanoparticle-aided electrical conductivity in polymer nanocomposites: A combination of analytical strategy and artificial neural network Economies of agglomeration ; Percolation (cognitive psychology) ; Nanoparticle ; Nanocomposite ; Percolation theory ; Materials science ; Polymer ; Conductivity ; Polymer nanocomposite ; Artificial neural network ; Nanotechnology ; Electrical resistivity and conductivity ; Percolation threshold ; Chemical engineering ; Composite material ; Chemistry ; Computer science ; Artificial intelligence ; Physics ; Engineering Conducting polymers and applications ; Electrochemical Analysis and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Hananeh Oliaei | N. R. Aluru
Source: APL Machine Learning (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Study of the adsorption sites of high entropy alloys for CO2 reduction using graph convolutional network Carbon dioxide reduction is a major step toward building a cleaner and safer environment. There is a surge of interest in exploring high-entropy alloys (HEAs) as active catalysts for CO2 reduction; however, so far, it is mainly limited to quinary HEAs. Inspired by the successful synthesis of octonary and denary HEAs, herein, the CO2 reduction reaction (CO2RR) performance of an HEA composed of Ag, Au, Cu, Pd, Pt, Co, Ga, Ni, and Zn is studied by developing a high-fidelity graph neural network (GNN) framework. Within this framework, the adsorption site geometry and physics are employed through the featurization of elements. Particularly, featurization is performed using various intrinsic properties, such as electronegativity and atomic radius, to enable not only the supervised learning of CO2RR performance descriptors, namely, CO and H adsorption energies, but also the learning of adsorption physics and generalization to unseen metals and alloys. The developed model evaluates the adsorption strength of ∼3.5 and ∼0.4 billion possible sites for CO and H, respectively. Despite the enormous space of the AgAuCuPdPtCoGaNiZn alloy and the rather small size of the training data, the GNN framework demonstrated high accuracy and good robustness. This study paves the way for the rapid screening and intelligent synthesis of CO2RR-active and selective HEAs. Adsorption ; Graph ; Reduction (mathematics) ; Computer science ; Materials science ; Chemistry ; Theoretical computer science ; Mathematics ; Physical chemistry Catalytic Processes in Materials Science ; Advanced Thermoelectric Materials and Devices ; CO2 Reduction Techniques and Catalysts
Authors: Igor Poltavsky | Anton Charkin-Gorbulin | Mirela Puleva | Grégory Fonseca | Ilyes Batatia | Nicholas J. Browning | Stefan Chmiela | Mengnan Cui | J. Thorben Frank | Stefan Heinen | Bing Huang | Silvan Käser | Adil Kabylda | Danish Khan | Carolin Müller | Alastair J. A. Price | Kai Riedmiller | Kai Töpfer | Tsz Wai Ko | Markus Meuwly | Matthias Rupp | Gábor Cśanyi | O. Anatole von Lilienfeld | Johannes T. Margraf | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2024-09-27)
Field: Materials Science — Materials Chemistry

Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023 Publication status: Published Crash ; Computer science ; Nanotechnology ; Materials science ; Operating system Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Jian‐Ming Liao | Hui‐Hsu Gavin Tsai
Source: Solar RRL (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening Adding a third component to binary organic solar cells (OSCs) enhances ternary OSCs, boosting power conversion efficiency (PCE). However, developing and optimizing appropriate donors, acceptors, and ternary materials remains a complex and demanding task. This study presents four machine‐learning (ML) predictive models using XGBoost and ANN approaches, utilizing both experimental and DFT‐derived HOMO and LUMO levels for efficient high‐throughput virtual screening (HTVS) of top candidates based on PCE. Two distinct latent databases were employed for HTVS: one consisting of 429 413 uniquely recombined ternary OSC systems from experimentally available data, and another comprising ≈2.3 million unique donor molecules from the Harvard Clean Energy Project database (CEPDB). The four ML models demonstrated notable predictive accuracy for PCE on a test dataset containing molecules closely aligned with the training set (interpolation). However, the XGBoost model showed constrained extrapolative ability for molecules significantly divergent from those in the training dataset. In contrast, the ANN models displayed a robust extrapolative capacity in HTVS, successfully predicting new potential ternary OSC systems and leading donors with PCE values exceeding 20%. Our ML models use HOMO and LUMO inputs for donors, acceptors, and ternaries, facilitating efficient optimization via rapid HTVS of high‐performance ternary materials. Fullerene ; Ternary operation ; Throughput ; Virtual screening ; Computer science ; Computable general equilibrium ; Machine learning ; Artificial intelligence ; Organic solar cell ; Binary number ; Chemistry ; Computational chemistry ; Mathematics ; Microeconomics ; Economics ; Organic chemistry ; Telecommunications Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Lin Wang | Tanjin He | Bin Ouyang
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

The Impact of Domain Knowledge on Universal Machine Learning Models Developing transferable universal machine learning models is trending in data-driven materials research. However, the effectiveness of adapting large machine learning model to specific research domain remains unclear. In this work, we choose high entropy materials as a platform and develop a specialized dataset with 145,323 DFT-relaxed high entropy materials. This dataset is used to explore the role of domain-specific knowledge in training models for broad chemical space. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influences on performance than the inclusion of critical domain knowledge. Specifically, the inclusion of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly improves the model's performance. We also find that domain knowledge-driven sampling tends to be more effective than commonly used unsupervised learning techniques. This research highlights that developing specialized datasets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed in AI development for materials science. Computer science ; Machine learning ; Domain knowledge ; Artificial intelligence ; Entropy (arrow of time) ; Knowledge graph ; Artificial neural network ; Domain (mathematical analysis) ; Deep learning ; Data science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Yao Hu | Bo Hu | Haihui Lan | Jiaxuan Gong | Renjing Hu | Donghui Wang | Wenda Zhang | Mo Yan | Qi Wang | Yulong Liu | Huicong Xia | Mingde Yao | Mingliang Du
Source: ACS Applied Materials & Interfaces (article, 2024-12-28)
Field: Chemistry — Organic Chemistry

Machine Learning Boosted Entropy-Engineered Synthesis of CuCo Nanometric Solid Solution Alloys for Near-100% Nitrate-to-Ammonia Selectivity Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization. Building on this enhanced data processing framework, we developed an entropy-engineered synthesis approach specifically designed to produce stable, nanometric copper and cobalt (CuCo) solid solution alloys. Under conditions of -0.425 V (vs RHE), the CuCo alloy exhibited nearly 100% Faraday efficiency (FE) and a high ammonia production rate of 232.17 mg h<sup>-1</sup> mg<sup>-1</sup>. Stability tests in a simulated industrial environment showed that the catalyst maintained over 80% FE and an ammonia production rate exceeding 170 mg h<sup>-1</sup> mg<sup>-1</sup> over a testing period of 120 h, outperforming most reported catalysts. To delve deeper into the synergistic interaction mechanisms between Cu and Co, in situ Raman spectroscopy was utilized for real-time monitoring, and density functional theory (DFT) calculations further substantiated our findings. These results not only highlight the exceptional catalytic performance of the CuCo alloy but also reflect the effective electronic and energy interactions between the two metals. Materials science ; Alloy ; Catalysis ; Solid solution ; Nanotechnology ; Chemical engineering ; Thermodynamics ; Metallurgy Nanomaterials for catalytic reactions ; Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques
Authors: Aikaterini-Artemis Agiomavriti | Maria P. Nikolopoulou | Thomas Bartzanas | Nikos Chorianopoulos | Konstantinos Demestichas | Athanasios Ι. Gelasakis
Source: Chemosensors (article, 2024-12-13)
Field: Chemistry — Analytical Chemistry

Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain. Adaptability ; Strengths and weaknesses ; Quality (philosophy) ; Computer science ; Dairy industry ; Biochemical engineering ; Biotechnology ; Machine learning ; Artificial intelligence ; Engineering ; Chemistry ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Jianfeng Xu | Peng Xu | Zheyi Yang | Fuhai Liu | Lizhen Xu | Jun Lou | Bo Fang | Xufeng Jing
Source: Applied Physics A (article, 2024-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Freeform metasurface design with a conditional generative adversarial network Generative adversarial network ; Adversarial system ; Generative grammar ; Computer science ; Generative Design ; Artificial intelligence ; Engineering ; Deep learning ; Operations management Metamaterials and Metasurfaces Applications ; Antenna Design and Optimization ; Advanced Antenna and Metasurface Technologies
Authors: Min Gee Cho | Katherine Sytwu | Luis Rangel DaCosta | Catherine Groschner | Myoung Hwan Oh | Mary Scott
Source: ACS Nano (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co<sub>3</sub>O<sub>4</sub> nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an "onset radius," which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications. Nanocrystal ; Characterization (materials science) ; Throughput ; Materials science ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; X-ray Diffraction in Crystallography
Authors: Xiangyu Chen | Nam Q. Le | Paulette Clancy
Source: Crystal Growth & Design (article, 2024-03-12)
Field: Materials Science — Materials Chemistry

Diffusion-Limited Crystal Growth of Gallium Nitride Using Active Machine Learning Gallium nitride (GaN) is an important semiconductor with properties that make it particularly suitable for high-temperature applications in power systems. Unfortunately, its crystalline synthesis involves an energy-intensive process requiring high temperatures and pressures. A new additive manufacturing process offers a lower-temperature alternative, but little is understood of the molecular-scale mechanisms that drive its crystallization from the melt. Traditional semiempirical force fields within a molecular dynamics (MD) simulation are typically unable to capture bond-making and -breaking, whereas ab initio MD, while more accurate, suffers from high computational expense. This paper uses a machine-learned force field based on the FLARE++ framework to mimic the liquid-phase epitaxy of GaN, simulating the diffusion of nitrogen atoms through liquid gallium to form GaN. We show that nitrogen diffusion through Ga is slow, and relatedly, there is a pronounced tendency for nitrogen to phase-segregate within liquid Ga. This leads to nitrogen being less available to react and form crystalline GaN. As a result, the predicted crystal growth at the melt/crystal interface is extremely slow, as seen experimentally. In this work, we demonstrate the potential of a MLFF to describe complex, multiphase behavior under different conditions. We also uncover the key atomistic-level mechanism of diffusion-limited GaN crystal growth, which is an important step toward further control of the additive manufacturing process. Gallium ; Diffusion ; Chemical physics ; Gallium nitride ; Crystal (programming language) ; Crystallization ; Materials science ; Crystal growth ; Molecular dynamics ; Nitride ; Phase (matter) ; Epitaxy ; Nanotechnology ; Chemistry ; Crystallography ; Computational chemistry ; Thermodynamics ; Computer science Machine Learning in Materials Science ; GaN-based semiconductor devices and materials ; Thermal properties of materials
Authors: Hua Men
Source: Nature Communications (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

A joint diffusion/collision model for crystal growth in pure liquid metals The kinetics of atomic attachments at the liquid/solid interface is one of the foundations of solidification theory, and to date one of the long-standing questions remains: whether or not the growth is thermal activated in pure liquid metals. Using molecular dynamics simulations and machine learning, I have demonstrated that a considerable fraction of liquid atoms at the interfaces of Al(111), (110) and (100) needs thermal activation for growth to take place while the others attach to the crystal without an energy barrier. My joint diffusion/collision model is proved to be robust in predicting the general growth behaviour of pure metals. Here, I show this model is able to quantitatively describe the temperature dependence of growth kinetics and to properly interpret some important experimental observations, and it significantly advances our understanding of solidification theory and also is useful for modelling solidification, phase change materials and lithium dendrite growth in lithium-ion battery. Diffusion ; Collision ; Joint (building) ; Crystal growth ; Growth model ; Materials science ; Chemical physics ; Computer science ; Chemistry ; Crystallography ; Physics ; Thermodynamics ; Mathematics ; Structural engineering Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Material Dynamics and Properties