jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashwin Mhadeshwar | Trupti Mohanty | Taylor D. Sparks
Source: Journal of Materials Informatics (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Borides as promising M<sub>2</sub>AX phase materials with high elastic modulus using machine learning and optimization There is growing interest in novel MAX phase materials for various applications ranging from aircraft/spacecraft and defense to energy and electronics due to their unique combination of metallic and ceramic properties. Traditional materials discovery has mostly relied on human intuition coupled with rigorous experiments; however, this approach has been time-consuming and inefficient. Over the last few decades, advances in fundamental and data-driven approaches such as first-principles modeling, materials informatics, machine learning and optimization, coupled with an exponential rise in computational power, have enabled faster and more efficient materials discovery. Here, we present an exploration of high elastic modulus novel boride-based M2AX phase materials using a combination of the aforementioned methods. Specifically, an ensemble of gradient boosted machine learning models was developed to predict the elastic modulus from informatics-based structural features by leveraging a dataset of Density Functional Theory (DFT)-predicted elastic moduli for 223 M2AX phase materials (carbides and nitrides). Using Bayesian optimization, inverse modeling was carried out to maximize the model-predicted elastic modulus by identifying the optimal features. Finally, model predictions for 1,035 candidate M2AX materials were generated to compare their features with the optimal features to identify potential novel promising materials. We found that Ta2PB, Nb2PB, and V2PB have similar high elastic moduli (371.7, 351.5, and 347.4 GPa) to their carbide counterparts (364.7, 357.7, and 373.5 GPa), and our results support the possibility that borides can be a viable tertiary element for M2AX phases. Elastic modulus ; Materials science ; Phase (matter) ; Composite material ; Modulus ; Physics MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Wonseok Jeong | Wenyu Sun | Marcos F. Calegari Andrade | Liwen F. Wan | Trevor M. Willey | Michael H. Nielsen | Tuan Anh Pham
Source: Chemistry of Materials (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. We discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes. XANES ; Characterization (materials science) ; Carbon fibers ; Amorphous solid ; Materials science ; Amorphous carbon ; Genetic algorithm ; Chemical space ; Chemical state ; Nanotechnology ; Carbon nitride ; Absorption (acoustics) ; Density functional theory ; Boron nitride ; Spectroscopy ; Chemistry ; Computational chemistry ; Physics ; Crystallography ; X-ray photoelectron spectroscopy ; Organic chemistry Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Diamond and Carbon-based Materials Research
Authors: Yuxi Yang | Haoyang Fu | Weihong Gao | Wenlong Su | Bin Sun | Xiaoyang Yi | Ting Zheng | Xianglong Meng
Source: Materials Letters (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Data-driven high elastocaloric NiMn-based shape memory alloy optimization with machine learning Materials science ; Shape-memory alloy ; Alloy ; Pseudoelasticity ; Composite material ; Metallurgy ; Machine learning ; Microstructure ; Computer science ; Martensite Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Hatem Helal | Jesun Firoz | Jenna A. Bilbrey | Henry W. Sprueill | Kristina M. Herman | Mario Michael Krell | Tom Murray | Manuel Lopez Roldan | Mike Kraus | Ang Li | Payel Das | Sotiris S. Xantheas | Sutanay Choudhury
Source: Journal of Chemical Information and Modeling (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges, such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs, such as social networks. We demonstrate a novel hardware–software codesign approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations, including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed codesign approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes, and sparsity. We demonstrate that such a codesign approach can reduce the training time of atomistic GNNs and can improve their performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs. Computer science ; Artificial neural network ; Graph ; Theoretical computer science ; Software ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Yibin Xu | Yen‐Ju Wu | Huiping Li | Lei Fang | Shigenobu Hayashi | Ayako Oishi | Natsuko Shimizu | Riccarda Caputo | P. Villars
Source: Science and Technology of Advanced Materials (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

A comprehensive data network for data-driven study of battery materials Data-driven material research for property prediction and material design using machine learning methods requires a large quantity, wide variety, and high-quality materials data. For battery materials, which are commonly polycrystalline, ceramics, and composites, multiscale data on substances, materials, and batteries are required. In this work, we develop a data network composed of three interlinked databases, from which we can obtain comprehensive data on substances such as crystal structures and electronic structures, data on materials such as chemical composition, structure, and properties, and data on batteries such as battery composition, operation conditions, and capacity. The data are extracted from research papers on solid electrolytes and cathode materials, selected by screening more than 330 thousand papers using natural language processing tools. Data extraction and curation are carried out by editors specialized in material science and trained in data standardization. Battery (electricity) ; Materials science ; Computer science ; Data science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Battery Technologies Research
Authors: Alain Bertrand Bomgni | Ribot Fleury Téné Ceskoutsé | Kevin Jordan Njike Njingang | Thomas Bouétou Bouétou | Venkataramana Gadhamshetty | Etienne Z. Gnimpiéba
Source:  (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step; however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR’s unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Natural language processing ; Chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Igor Poltavsky | Mirela Puleva | Anton Charkin-Gorbulin | 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: Molecular Dynamics in the TEA Challenge 2023 Publication status: Published Crash ; Molecular dynamics ; Nanotechnology ; Computer science ; Materials science ; Engineering ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Jing Wan | Guanting Li | Zeyu Guo | Huasong Qin
Source: Journal of Physics Condensed Matter (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Thermal transport in C<sub>6</sub>N<sub>7</sub> monolayer: a machine learning based molecular dynamics study The successful synthesis of a novel C<sub>6</sub>N<sub>7</sub>carbon nitride monolayer offers expansive prospects for applications in the fields of semiconductors, sensors, and gas separation technologies, in which the thermal transport properties of C<sub>6</sub>N<sub>7</sub>are crucial for optimizing the functionality and reliability of these applications. In this work, based on our developed machine learning potential (MLP), molecular dynamics (MD) simulations including homogeneous non-equilibrium, non-equilibrium, and their respective spectral decomposition methods are performed to investigate the effects of phonon transport, temperature, and length on the thermal conductivity of C<sub>6</sub>N<sub>7</sub>monolayer. Our results reveal that low-frequency and in-plane phonon modes dominate the thermal conductivity. Notably, thermal conductivity decreases with an increase in temperature due to temperature-induced increase in phonon-phonon scattering of in-plane phonon modes, while it increases with an extension in sample length. Our findings based on MD simulations with MLP contribute new insights into the lattice thermal conductivity of holey carbon nitride compounds, which is helpful for the development of next-generation electronic and photonic devices. Phonon ; Thermal conductivity ; Monolayer ; Materials science ; Molecular dynamics ; Condensed matter physics ; Semiconductor ; Scattering ; Phonon scattering ; Chemical physics ; Nanotechnology ; Chemistry ; Optoelectronics ; Computational chemistry ; Optics ; Physics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Semiconductor Quantum Structures and Devices
Authors: Dianhui Mao | J Q Liu | Xuebo Li | Min Zuo | Wenjing Yan
Source: Chemical Engineering Journal (article, 2024-01-21)
Field: Materials Science — Materials Chemistry

Holo-Mol: An explainable hybrid deep learning framework for predicting reactivity of hydroxyl radical to water contaminants based on holographic fused molecular representations Interpretability ; Reactivity (psychology) ; Aqueous solution ; Chemistry ; Molecular descriptor ; Hydroxyl radical ; Reaction rate constant ; Deep learning ; Biological system ; Computer science ; Biochemical engineering ; Computational chemistry ; Artificial intelligence ; Machine learning ; Quantitative structure–activity relationship ; Organic chemistry ; Radical ; Kinetics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis
Authors: A. Welsh | D. Husbands | A. Frei
Source: Angewandte Chemie International Edition (review, 2024-12-23)
Field: Materials Science — Materials Chemistry

High‐Throughput Combinatorial Metal Complex Synthesis High-throughput combinatorial metal complex synthesis has emerged as a powerful tool for rapidly generating and screening diverse libraries of metal complexes, enabling accelerated discovery in fields such as catalysis, medicinal chemistry, and materials science. By systematically combining building blocks under mild and efficient conditions, researchers can explore broad chemical spaces, increasing the likelihood of identifying complexes with desired properties. This method streamlines hit identification and optimisation, especially when integrated with high-throughput screening and data-driven approaches like machine learning. Despite challenges such as scalability and purity control, recent advancements in automation and predictive modelling are enhancing the efficiency of combinatorial synthesis, opening new avenues for the development of metal-based catalysts, therapeutic agents, and functional materials. Scalability ; Combinatorial synthesis ; Throughput ; Computer science ; Identification (biology) ; High-throughput screening ; Automation ; Nanotechnology ; Biochemical engineering ; Combinatorial chemistry ; Chemistry ; Materials science ; Engineering ; Database ; Biology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Gouthamaraj Kanapaakala | Venkatesan Subramani
Source: Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Predictive modeling for design of low elastic modulus and biocompatible β-Ti alloys: A comprehensive machine learning-data driven approach with XGBoost as the key predictor Mechanical compatibility and biocompatibility are critical considerations in the design of β-Ti alloys. The former hinges on achieving a reduced elastic modulus, while the latter is contingent on the toxic alloying element’s absence. Although conventional alloy design methods have been employed for developing low-modulus β-Ti alloys, these theoretical approaches encounter limitations due to the high non-linear relationship between composition and elastic modulus. To address these challenges and improve the design of low-modulus and biocompatible β-Ti alloys, an alternative approach utilizing machine learning (ML) techniques has been explored. ML leverages sophisticated algorithms to discern intricate connections between material composition and elastic modulus, making it a promising avenue for alloy design. In this study, seven supervised ML regression models were developed and compared to identify the most effective model for the given dataset. Notably, the XGBoost model exhibited superior performance compared to the other models, boasting a remarkable accuracy with an R-squared ( R 2 ) value of 0.962 and an RMSE (Root Mean Square Error) of 3.16 GPa for the test data. Due to the inherent complexity of comprehending the structures and patterns present within datasets and simultaneously formulating effective predictive models, this study pursued the exploration of dependable models through the avenue of statistical inference analysis, a novel approach yet unexplored in prior research studies. Furthermore, a feature importance analysis was conducted to ascertain crucial input parameters significantly influencing the output, based on the best-performing model. The validity of the best model’s results was confirmed by comparing them with experimental data, ensuring the reliability and applicability of the ML-based alloy design method. Mean squared error ; Modulus ; Elastic modulus ; Computer science ; Machine learning ; Biocompatible material ; Predictive modelling ; Materials science ; Artificial intelligence ; Mathematics ; Biomedical engineering ; Composite material ; Engineering ; Statistics Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tonglei Li | Nicholas J. Huls | Shan Lu | Peng Hou
Source: Communications Chemistry (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data Molecular representation is critical in chemical machine learning. It governs the complexity of model development and the fulfillment of training data to avoid either over- or under-fitting. As electronic structures and associated attributes are the root cause for molecular interactions and their manifested properties, we have sought to examine the local electron information on a molecular manifold to understand and predict molecular interactions. Our efforts led to the development of a lower-dimensional representation of a molecular manifold, Manifold Embedding of Molecular Surface (MEMS), to embody surface electronic quantities. By treating a molecular surface as a manifold and computing its embeddings, the embedded electronic attributes retain the chemical intuition of molecular interactions. MEMS can be further featurized as input for chemical learning. Our solubility prediction with MEMS demonstrated the feasibility of both shallow and deep learning by neural networks, suggesting that MEMS is expressive and robust against dimensionality reduction. Nonlinear dimensionality reduction ; Embedding ; ENCODE ; Manifold (fluid mechanics) ; Dimensionality reduction ; Computer science ; Artificial intelligence ; Representation (politics) ; Curse of dimensionality ; Molecular graph ; Feature learning ; Manifold alignment ; Biological system ; Theoretical computer science ; Machine learning ; Chemical physics ; Chemistry ; Biology ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Xiangdong Wang | Yan Cao | Jialin Ji | Ye Sheng | Jiong Yang | Xuezhi Ke
Source: Journal of Materiomics (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

A multi-objective, multi-interpretable machine learning demonstration verified by domain knowledge for ductile thermoelectric materials Multi-objective machine learning (ML) methods are widely used in the field of materials because material optimizations are always multi-objective. Traditional multi-objective optimization methods mainly use a combination of hierarchical single-objective optimization. However, this strategy often has difficulty in finding features that can optimize multiple objectives simultaneously. In this work, taking the two objectives of ductility and thermoelectric performance as examples, interpretable and explainable ML strategies are used to find features that can simultaneously optimize multiple objectives. Specifically, SHAP and SISSO are applied for qualitative analysis and quantitative analysis between key features and target values. Both SISSO and SHAP show that EN(ab)A/B and V are both positively correlated with zT and negatively correlated with Pugh’s ratio. Furthermore, domain knowledge helps to rationalize the two favorable features. The compounds with large EN(ab)A/B tend to have high band degeneracies, resulting in high zT. High EN(ab)A/B correspond to weak B–X bonds, reducing the G and Pugh's ratio, and improving the ductility of materials. On the other hand, large V will cause small G, which is beneficial to small Pugh’s ratio and large zT (via low κL). The present work demonstrates the significance of multi-objective optimization and domain knowledge in the development of materials informatics. Ductility (Earth science) ; Thermoelectric effect ; Materials science ; Domain (mathematical analysis) ; Computer science ; Work (physics) ; Field (mathematics) ; Materials informatics ; Key (lock) ; Machine learning ; Artificial intelligence ; Mechanical engineering ; Mathematics ; Composite material ; Physics ; Health informatics ; Engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion