jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marinella de Giovanetti | Sondre H. Hopen Eliasson | Sigbjørn Løland Bore | Odile Eisenstein | Michele Cascella
Source: ChemRxiv (preprint, 2024-11-07)
Field: Chemistry — Electrochemistry

Morphology of Lithium Halides in Tetrahydrofuran from Molecular Dynamics with Machine Learning Potentials The preferred structures of lithium halides (LiX, with X = Cl, Br, I) in organic solvents have been the subject of a wide scientific debate, and a large variety of forms has been isolated and characterized by X-ray diffraction. The identified molecular scaffolds for LiX are diverse, often built on (LiX)n rings with a prevalence of rhomboidal arrangements and an appropriate number of solvent or Lewis base molecules coordinating the lithium ions. Much less is known about the structures of LiX in solution, limiting the understanding of the synergistic role of LiX in reactions with various organometallic complexes, as prominently represented by the turbo Grignard reaction. Here, we trained a machine learning potential on ab initio data to explore the complex conformational landscape for systems comprising 4 LiX moieties in tetrahydrofuran (THF). For all the considered halogens a large number of scaffolds were found at thermally accessible free energy values, indicating that LiX in solution are a diverse ensemble constituted by (LiX)n moieties of various size, completed by the appropriate number of coordinating THF. LiCl shows preference for compact, pseudo-cubane [(LiCl)(THF)]4 structures, coexisting with open rings. At concen- trations close to the solubility limit, LiCl forms hexagonal structures, in analogy with literature observations on pre-nucleating NaCl. LiBr tends to favor less compact, more solvated aggregates. LiI significantly differs from the two other cases, producing highly solvated, monomeric, dimeric or linear structures. This study provides a comprehensive view of LiX in organic solvent, revealing dynamical polymorphism that is not easily experimentally observable. Tetrahydrofuran ; Halide ; Morphology (biology) ; Lithium (medication) ; Molecular dynamics ; Dynamics (music) ; Chemistry ; Materials science ; Chemical physics ; Nanotechnology ; Chemical engineering ; Computational chemistry ; Geology ; Physics ; Organic chemistry ; Engineering ; Paleontology ; Psychology Electrochemical Analysis and Applications ; Solid-state spectroscopy and crystallography
Authors: Yuxuan Zeng | Wei Cao | Yijing Zuo | Tan Peng | Yue Hou | Ling Miao | Ziyu Wang | Shi, Jing
Source: arXiv (Cornell University) (preprint, 2024-12-08)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of Materials with Expected Thermal Conductivity via a Synergistic Strategy of DFT and Interpretable Deep Learning Lattice thermal conductivity (LTC) is a critical parameter for thermal transport properties, playing a pivotal role in advancing thermoelectric materials and thermal management technologies. Traditional computational methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD), are resource-intensive, limiting their applicability for high-throughput LTC prediction. While AI-driven approaches have made significant strides in material science, the trade-off between accuracy and interpretability remains a major bottleneck. In this study, we introduce an interpretable deep learning framework that enables rapid and accurate LTC prediction, effectively bridging the gap between interpretability and precision. Leveraging this framework, we identify and validate four promising thermal conductors/insulators using DFT and MD. Moreover, by combining sensitivity analysis with DFT calculations, we uncover novel insights into phonon thermal transport mechanisms, providing a deeper understanding of the underlying physics. This work not only accelerates the discovery of thermal materials but also sets a new benchmark for interpretable AI in material science. Lattice (music) ; Thermal conductivity ; Deep learning ; Conductivity ; Artificial intelligence ; Materials science ; Data science ; Computer science ; Physics ; Chemistry ; Composite material ; Physical chemistry Machine Learning in Materials Science
Authors: Guofeng Xie | Shiyi Li | Qian Liu | Yu‐Jia Zeng | Wu‐Xing Zhou
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Influence of Na-Substitution on Thermal Conductivity of Cathode Material Lifepo4:A Machine Learning Interatomic Potential-Assisted Investigation Substitution (logic) ; Cathode ; Materials science ; Thermal conductivity ; Thermal ; Thermodynamics ; Composite material ; Computer science ; Chemistry ; Physical chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Siwen Liu | Huiying Cheng | Furong Cao | Jingchang Sun | Zijiang Yang
Source: Molecules (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Ab Initio Neural Network Potential Energy Surface and Quantum Dynamics Calculations on Na(2S) + H2 → NaH + H Reaction The collisions between Na atoms and H<sub>2</sub> molecules are of great significance in the field of chemical reaction dynamics, but the corresponding dynamics results of ground-state reactions have not been reported experimentally or theoretically. Herein, a global and high-precision potential energy surface (PES) of NaH<sub>2</sub> (1<sup>2</sup><i>A</i>') is constructed by the neural network model based on 21,873 high-level ab initio points. On the newly constructed PES, the quantum dynamics calculations on the Na(<sup>2</sup>S) + H<sub>2</sub>(<i>v</i><sub>0</sub> = 0, <i>j</i><sub>0</sub> = 0) → NaH + H reaction are carried out using the time-dependent wave packet method to study the microscopic reaction mechanism at the state-to-state level. The calculated results show that the low-vibrational products are mainly formed by the dissociation of the triatomic complex; whereas, the direct reaction process dominates the generation of the products with high-vibrational states. The reaction generally follows the direct H-abstraction process, and there is also the short-lived complex-forming mechanism that occurs when the collision energy exceeds the reaction threshold slightly. The PES could be used to further study the stereodynamics effects of isotope substitution and rovibrational excitations on the title reaction, and the presented dynamics data would provide an important reference on the corresponding experimental research at a higher level. Potential energy surface ; Ab initio ; Potential energy ; Quantum ; Chemistry ; Computational chemistry ; Ab initio quantum chemistry methods ; Dynamics (music) ; Quantum dynamics ; Reaction dynamics ; Chemical physics ; Atomic physics ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Parham Valipoorsalimi | Mihriban Pekguleryuz | Yuksel Asli Sari
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Biomaterials

Computational Thermodynamics-Assisted Machine Learning Approach to Predict Mechanical Properties of Magnesium Alloys for Bio-Implant Applications Magnesium ; Materials science ; Thermodynamics ; Mechanical engineering ; Computer science ; Metallurgy ; Engineering ; Physics Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties
Authors: Usman Syed | Federico Cunico | Uzair Khan | Eros Radicchi | Francesco Setti | Adolfo Speghini | Paolo Marone | Filiberto Semenzin | Marco Cristani
Source: arXiv (Cornell University) (preprint, 2024-11-21)
Field: Materials Science — Materials Chemistry

Material synthesis through simulations guided by machine learning: a position paper In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual from stone-cutting processes, can be repurposed by mixing it with various ingredients. However, determining the optimal mix design is challenging due to the variability in sludge composition and the costly, time-consuming nature of experimental data collection. Also, we investigate the possibility of using machine learning models using meta-learning as an optimization tool to estimate the correct quantity of stone-cutting sludge to be used in aggregates to obtain a mix design with specific mechanical properties that can be used successfully in the building industry. Our approach offers two key advantages: (i) through simulations, a large dataset can be generated, saving time and money during the data collection phase, and (ii) Utilizing machine learning models, with performance enhancement through hyper-parameter optimization via meta-learning, to estimate optimal mix designs reducing the need for extensive manual experimentation, lowering costs, minimizing environmental impact, and accelerating the processing of quarry sludge. Our idea promises to streamline the marble sludge reuse process by leveraging collective data and advanced machine learning, promoting sustainability and efficiency in the stonecutting sector. Position (finance) ; Computer science ; Artificial intelligence ; Business ; Finance Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Naoki Matsumura | Yuta Yoshimoto | Tamio Yamazaki | Tomohito Amano | Tomoyuki Noda | Naoki Ebata | Takatoshi Kasano | Yasufumi Sakai
Source: arXiv (Cornell University) (preprint, 2024-11-26)
Field: Materials Science — Materials Chemistry

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing &gt;10,000 atoms with the accuracy comparable to ab initio methods and play a crucial role in material studies. Although NNPs are valuable for short-duration MD simulations, maintaining the stability of long-duration MD simulations remains challenging due to the uncharted regions of the potential energy surface (PES). Currently, there is no effective methodology to address this issue. To overcome this challenge, we developed an automatic generator of robust and accurate NNPs based on an active learning (AL) framework. This generator provides a fully integrated solution encompassing initial dataset creation, NNP training, evaluation, sampling of additional structures, screening, and labeling. Crucially, our approach uses a sampling strategy that focuses on generating unstable structures with short interatomic distances, combined with a screening strategy that efficiently samples these configurations based on interatomic distances and structural features. This approach greatly enhances the MD simulation stability, enabling nanosecond-scale simulations. We evaluated the performance of our NNP generator in terms of its MD simulation stability and physical properties by applying it to liquid propylene glycol (PG) and polyethylene glycol (PEG). The generated NNPs enable stable MD simulations of systems with &gt;10,000 atoms for 20 ns. The predicted physical properties, such as the density and self-diffusion coefficient, show excellent agreement with the experimental values. This work represents a remarkable advance in the generation of robust and accurate NNPs for organic materials, paving the way for long-duration MD simulations of complex systems. Nanosecond ; Generator (circuit theory) ; Scale (ratio) ; Dynamics (music) ; Computer science ; Artificial neural network ; Molecular dynamics ; Network dynamics ; Artificial intelligence ; Biological system ; Statistical physics ; Physics ; Mathematics ; Biology ; Optics ; Acoustics ; Laser ; Power (physics) Machine Learning in Materials Science
Authors: Sung Woo Lee | Marcel Jonathan Hidajat | Seung Hyeok | Gwang‐Nam Yun | Dong Won Hwang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Analysis in the Selective Oligomerization of Long-Chain Linear Alpha Olefin on Zeolite Catalysts: A Machine Learning-Based Parameter Study Olefin fiber ; Alpha (finance) ; Zeolite ; Catalysis ; Chain (unit) ; Chemistry ; Computer science ; Organic chemistry ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science
Authors: Kenta Matsutani | Shusuke Kasamatsu | Takeshi Usuki
Source: arXiv (Cornell University) (preprint, 2024-09-11)
Field: Materials Science — Ceramics and Composites

Comparison of intermediate-range order in GeO$_2$ glass: molecular dynamics using machine-learning interatomic potential vs.\ reverse Monte Carlo fitting to experimental data The short and intermediate-range order in GeO$_2$ glass are investigated by molecular dynamics using machine-learning interatomic potential trained on ab initio calculation data and compared with reverse Monte Carlo fitting of neutron diffraction data. To characterize the structural differences in each model, the total/partial structure factors, coordination number, ring size and shape distributions, and persistent homology analysis were performed. These results show that although the two approaches yield similar two-body correlations, they can lead to three-dimensional models with very different short and intermediate-range ordering. A clear difference was observed especially in the ring distributions; RMC models exhibit a broad distribution in the ring size distribution, while neural network potential molecular dynamics yield much narrower ring distributions. This confirms that the density functional approximation in the ab initio calculations determines the preferred network assembly more strictly than RMC with simple coordination constraints and neutron diffraction data with isotope substitution. Reverse Monte Carlo ; Monte Carlo method ; Statistical physics ; Molecular dynamics ; Range (aeronautics) ; Computer science ; Dynamics (music) ; Order (exchange) ; Physics ; Materials science ; Mathematics ; Statistics ; Optics ; Quantum mechanics Glass properties and applications ; Theoretical and Computational Physics
Authors: Kohji Omata
Source: Journal of the Japan Petroleum Institute (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Application of Machine Learning to Catalyst Design and Process Design for Methanol Synthesis 低圧におけるメタノール／ジメチルエーテル（DME）合成で，温度勾配型反応器を対象に（1）銅系触媒（Cu/Zn/Al）への添加成分を探索，（2）触媒層に適切な温度勾配を設ける，（3）温度ごとに触媒成分を最適化する，という手順で高いワンパスコンバーションを達成した。各手順で機械学習を活用して，研究の加速を試みた。（1）では主成分分析とサポートベクターマシーン（svm），（2）および（3）では直交表とsvmと遺伝的アルゴリズム（GA）を用いた。また，生成物のメタノール · 水をin situで凝縮することで触媒層から除去し，反応平衡をシフトさせる内部凝縮型反応器を対象として，凝縮速度を推定するためにGAを用いて反応ネットワークを決定した。その際にも機械学習を活用した。実験結果ならびに機械学習のためのコードについて詳述する。 Catalysis ; Methanol ; Process engineering ; Process (computing) ; Computer science ; Manufacturing engineering ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Programming language Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Joshua Ojih | Mohammed Al-Fahdi | Yagang Yao | Jianjun Hu | Ming Hu
Source: Scholar Commons (University of South Carolina) (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Correction: Graph Theory and Graph Neural Network Assisted High-Throughput Crystal Structure Prediction and Screening for Energy Conversion and Storage Correction for ‘Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage’ by Joshua Ojih et al., J. Mater. Chem. A, 2024, 12, 8502–8515, https://doi.org/10.1039/D3TA06190F. On page 8511 in section 3.2 of the published article, the text “The single values of bonding and antibonding for each structure are obtained by performing integration over COHP curves for each atomic pair as evaluated by the LOBSTER package” should have included citations to six references. The missing references are listed below as ref. 1–6. The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers. Artificial neural network ; Graph theory ; Crystal structure ; Graph ; Antibonding molecular orbital ; Single crystal ; Network structure Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; CO2 Reduction Techniques and Catalysts
Authors: Yasmin Makki Mohialden | Walaa A. Abd Alrada | Nadia Mahmood Hussien
Source: AIP conference proceedings (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Advanced machine learning for automated chemical reaction balancing: A systematic review Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ganesan Raman
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells Methanol ; Catalysis ; Platinum ; Anode ; Variety (cybernetics) ; Methanol fuel ; Nanostructure ; Materials science ; Carbon fibers ; Chemical engineering ; Nanotechnology ; Computer science ; Chemistry ; Engineering ; Artificial intelligence ; Composite material ; Electrode ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Attila Cangi | Lenz Fiedler | Bartosz Brzoza | Karan Shah | Timothy J. Callow | Daniel Kotik | Steve Schmerler | Matthew C. Barry | James Goff | Andrew Rohskopf | Dayton J. Vogel | Normand A. Modine | Aidan P. Thompson | Sivasankaran Rajamanickam
Source: arXiv (Cornell University) (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research. Scale (ratio) ; Scalability ; Computer science ; Computational science ; Artificial intelligence ; Algorithm ; Machine learning ; Physics ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Alegre-Requena, Juan V. | Dalmau, David
Source: DIGITAL.CSIC (Spanish National Research Council (CSIC)) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automated computational chemistry and machine learning workflows in chemistry Resumen del trabajo presentado a la 13th Electronic Structure Principles and Applications Conference (ESPA) and biennial Meeting of the RSEQ Group in Chemistry & Computation, celebradas del 3 al 7 de junio del 2024 en Tarragona (España). Workflow ; Work (physics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Graph Neural Networks
Authors: Daniele Lanzoni | Andrea Fantasia | Roberto Bergamaschini | Olivier Pierre-Louis | Francesco Montalenti
Source: arXiv (Cornell University) (preprint, 2024-07-29)
Field: Materials Science — Materials Chemistry

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and the ground truth ones obtained via conventional integration schemes. The method can accurately reproduce the evolution of microstructures not represented in the training set at a fraction of the computational costs. Extremely long-time extrapolation capabilities are achieved, up to reaching the theoretically expected equilibrium state of the system, consisting of a layered, phase-separated morphology, despite the training set containing only relatively-short, initial phases of the evolution. Quantitative accordance with the decay rate of the Free energy is also demonstrated up to the late coarsening stages, proving that this class of Machine Learning approaches can become a new and powerful tool for the long timescale and high throughput simulation of materials, while retaining thermodynamic consistency and high-accuracy. Extrapolation ; Microstructure ; Consistency (knowledge bases) ; Artificial neural network ; Statistical physics ; Computer science ; Physics ; Materials science ; Artificial intelligence ; Mathematics ; Mathematical analysis ; Metallurgy Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Jose A. Arjona-Medina | Ramil Nugmanov
Source: arXiv (Cornell University) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular representations that effectively generalize in real-world scenarios to novel compounds remains an elusive and unresolved task. This study examines how atom-level pretraining with quantum mechanics (QM) data can mitigate violations of assumptions regarding the distributional similarity between training and test data and therefore improve performance and generalization in downstream tasks. In the public dataset Therapeutics Data Commons (TDC), we show how pretraining on atom-level QM improves performance overall and makes the activation of the features distributes more Gaussian-like which results in a representation that is more robust to distribution shifts. To the best of our knowledge, this is the first time that hidden state molecular representations are analyzed to compare the effects of molecule-level and atom-level pretraining on QM data. QM/MM ; Graph ; Property (philosophy) ; Artificial neural network ; Computer science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Theoretical computer science ; Molecular dynamics ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials