jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sebastien Röcken | Anton F. Burnet | Julija Zavadlav
Source: The Journal of Chemical Physics (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Predicting solvation free energies with an implicit solvent machine learning potential Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parameterize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. Learning on a combination of experimental hydration free energy data and ab initio data of molecules in vacuum, ReSolv bypasses the need for intractable ab initio data of molecules in an explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way for deep molecular models that are more accurate yet computationally more cost-effective than classical atomistic models. Solvation ; Computer science ; Ab initio ; Speedup ; Molecular dynamics ; Computation ; Artificial neural network ; Solvent models ; Solvent effects ; Potential of mean force ; Potential energy ; Statistical physics ; Computational chemistry ; Chemistry ; Algorithm ; Artificial intelligence ; Molecule ; Solvent ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wouter Edeling | Maxime Vassaux | Yiming Yang | Shunzhou Wan | Serge Guillas | Peter V. Coveney
Source: npj Computational Materials (article, 2024-05-03)
Field: Materials Science — Materials Chemistry

Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields Abstract Uncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated. Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability, it is essential that practitioners pay attention to the issues concerned. Here a UQ study is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters, which affect key quantities of interest, including material properties and binding free energy predictions in drug discovery and personalized medicine. Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes, the sensitivity of the input parameters is ranked. Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed. This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters. Molecular dynamics ; Uncertainty quantification ; Sensitivity (control systems) ; Ranking (information retrieval) ; Computer science ; Force field (fiction) ; Statistical physics ; Field (mathematics) ; Scalability ; Gaussian ; Biochemical engineering ; Data science ; Machine learning ; Artificial intelligence ; Computational chemistry ; Physics ; Mathematics ; Chemistry ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Ryo Maezono
Source: Artificial Intelligence Chemistry (article, 2024-07-10)
Field: Chemistry — Organic Chemistry

A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R2) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1), methyl quinoxaline-6-carboxylate (Q2), (2E,3E)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3), (2E,3E) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4), (E)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5), and 2-(4-methoxyphenyl)-7-methylthieno[3,2-b] quinoxaline (Q6), show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials. Quinoxaline ; Artificial neural network ; Quantitative structure–activity relationship ; Quantum chemical ; Quantum ; Mean squared error ; Corrosion ; Chemistry ; Quantum chemistry ; Absolute deviation ; Mathematics ; Computational chemistry ; Biological system ; Physical chemistry ; Artificial intelligence ; Computer science ; Stereochemistry ; Physics ; Organic chemistry ; Statistics ; Molecule ; Quantum mechanics ; Electrochemistry Synthesis and Biological Evaluation ; Electrochemical Analysis and Applications ; Multicomponent Synthesis of Heterocycles
Authors: Hao Xiao | Shuang Zhao | Jun Zhang | Shijun Zhao | Youbing Li | Ke Chen | Liuxuan Cao | Yugang Wang | Qing Huang | Chenxu Wang
Source: npj Computational Materials (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Distinct amorphization resistance in high-entropy MAX-phases (Ti, M)2AlC (M=Nb, Ta, V, Zr) under in situ irradiation High-entropy materials have been proposed for applications in nuclear systems recently due to their outstanding properties in extreme environments. Chemical complexity in these materials plays an important role in irradiation tolerance since it significantly affects energy dissipation and defect behaviors under particle bombardment. Indeed, better resistance to irradiation-induced amorphization was observed in the high-entropy MAX (HE-MAX) phase (Ti, M)2SnC (M = V, Nb, Zr, Hf). However, in this work, we report an opposite trend in another series of HE-MAX phases (Ti, M)2AlC (M = Nb, Ta, V, Zr). It is demonstrated that the amorphization resistance is sequentially reduced as the number of components increases from single-component Ti2AlC to (TiNbTa)2AlC and (TiNbTaVZr)2AlC. These phenomena are verified through AIMD simulations and interpreted by analyzing the underlying properties combining lattice distortion and bonding characteristics through the first-principle calculation. By developing a machine-learning (ML) model, we can directly predict lattice distortion to screen HE-MAX phases with excellent resistance to irradiation-induced amorphization. We highlight that the elemental species plays a more crucial role in the irradiation tolerance of these MAX phases than the number of constituent elements. Knowledge gained from this study will enable an improved understanding of the irradiation tolerance in HE-MAX phases and other multi-elemental ceramics. Irradiation ; In situ ; Materials science ; High entropy alloys ; Metallurgy ; Chemistry ; Physics ; Nuclear physics ; Microstructure MXene and MAX Phase Materials ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Sana Jawaid | Bharat Prasad Sharma | Sadam Hussain Tumrani | Zaheer Abbas | Razium Ali Soomro | Selcan Karakuş | Tarık Küçükdeniz | Ayman Nafady
Source: Materials Science and Engineering B (article, 2024-07-10)
Field: Materials Science — Polymers and Plastics

Enhanced electrochemical oxidation and machine learning-assisted sensing of tetrabromobisphenol A using activated carbon facilitated CoWO4 heterostructures Tetrabromobisphenol A ; Electrochemistry ; Heterojunction ; Activated carbon ; Materials science ; Nanotechnology ; Computer science ; Chemical engineering ; Chemistry ; Electrode ; Engineering ; Optoelectronics ; Composite material ; Organic chemistry Conducting polymers and applications ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Weiqi Chen | Kang Wang | Xinran Miao | Jie Zhang | Aisheng Song | Xinchun Chen | Jianbin Luo | Tianbao Ma
Source: Small (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Ultralow‐Friction at Cryogenic Temperature Induced by Hydrogen Correlated Quantum Effect Temperature is one of the governing factors affecting friction of solids. Undesired high friction state has been generally reported at cryogenic temperatures due to the prohibition of thermally activated processes, following conventional Arrhenius equation. This has brought huge difficulties to lubrication at extremely low temperatures in industry. Here, the study uncovers a hydrogen-correlated sub-Arrhenius friction behavior in hydrogenated amorphous carbon (a-C:H) film at cryogenic temperatures, and a stable ultralow-friction over a wide temperature range (103-348 K) is achieved. This is attributed to hydrogen-transfer-induced mild structural ordering transformation, confirmed by machine-learning-based molecular dynamics simulations. The anomalous sub-Arrhenius temperature dependence of structural ordering transformation rate is well-described by a quantum mechanical tunneling (QMT) modified Arrhenius model, which is correlated with quantum delocalization of hydrogen in tribochemical reactions. This work reveals a hydrogen-correlated friction mechanism overcoming the Arrhenius temperature dependence and provides a new pathway for achieving ultralow friction under cryogenic conditions. Arrhenius equation ; Materials science ; Hydrogen ; Lubricity ; Atmospheric temperature range ; Amorphous solid ; Thermodynamics ; Quantum tunnelling ; Lubrication ; Delocalized electron ; Activation energy ; Chemical physics ; Physical chemistry ; Chemistry ; Composite material ; Crystallography ; Physics Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; High-pressure geophysics and materials
Authors: Seungpyo Kang | Joonchul Kim | Taehyun Park | Joonghee Won | Chul Baik | Jungim Han | Kyoungmin Min
Source: Materials Today Advances (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors Under thin film deposition, when used in conjunction with the semiconductor atomic layer deposition (ALD) method, the choice of precursor determines the properties and quality of the thin film. Organometallic precursors such as alkaline earth metals (Sr and Ba) and group 4 transition metals (Zr and Hf) with cyclopentadienyl and tetrakis (ethylmethylamino) ligands have recently gained attention for their stable deposition within high-temperature windows in the ALD. The design of organometallic precursors with an ab initio molecular dynamics (AIMD) simulations-based approach ensures high accuracy but comes with significant computational costs. In this study, we aim to develop a machine-learning interatomic potential (MLIP) through moment tensor potential (MTP) for fast and accurate potential development of Sr, Ba, Zr, and Hf precursors. To establish the reliable training database for MTP construction, we conducted AIMD simulations on each precursor across a range of temperature settings, resulting in a variety of atomic structures. Constructed MTPs enable efficient utilization of molecular dynamics (MD) simulations as well as calculations that achieve an accuracy that approximates density functional theory (DFT). MTP construction coupled with active learning ensures that the MTP for each precursor is reliable and that databases can be expanded. High prediction accuracy is demonstrated by a mean absolute error (MAE) of less than 0.04 eV/atom in all structures. In addition, generalization performance is confirmed for general structures (structures with the same chemical elements but different proportions) and is extended to cluster structures. The constructed MTP exhibits an MAE of less than 0.15 eV/atom, even for untrained cluster structures. These results demonstrate adequate representation and scalability as a basis for the development of MLIPs capable of atomic simulations of organometallic precursors under various thermodynamic conditions. Atomic layer deposition ; Atom (system on chip) ; Interatomic potential ; Density functional theory ; Chemistry ; Ab initio ; Deposition (geology) ; Thin film ; Molecular dynamics ; Coupled cluster ; Computational chemistry ; Cyclopentadienyl complex ; Materials science ; Nanotechnology ; Molecule ; Computer science ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Taoufiq Kaoutari | Hasna Louahlia | Pierre Schaetzel | Hamid Gualous
Source: Applied Thermal Engineering (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Modeling of a metal hydride energy storage tank dynamics using hybrid numerical, experimental, and machine learning methods Hydride ; Energy storage ; Materials science ; Energy (signal processing) ; Dynamics (music) ; Mechanical engineering ; Metal ; Engineering ; Metallurgy ; Thermodynamics ; Physics ; Acoustics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Jiewei Cheng | Ahmed H. Ati | Yoshiyuki Kawazoe | Qiang Sun
Source: Journal of the American Chemical Society (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Introducing Noble Gas as Space Holder under High Pressure to Design Porous Titanium Carbides with Open Metal Sites for Hydrogen Storage at Near-Ambient Conditions It has been a long-standing challenge to develop high-performance solid-state hydrogen storage materials operated under near-ambient conditions. In this work, we propose a new strategy of using noble gases for space holding to design porous titanium carbides with abundant open metal sites for hydrogen storage. By using machine learning and graph theory-assisted universal structure searching methods, we obtain 28 porous titanium carbides from three precursors (TiC dimer, C atom, and Kr atom) under 30 GPa of pressure. The stability and hydrogen storage performance of the resulting structures are further assessed and validated through density function theory and grand canonical Monte Carlo simulations with a DFT-fitted force field. Finally, <i>p</i>-TiC<sub>2</sub> is identified as a promising quasi-molecular hydrogen storage material with capacity of 4.0 wt % and 106.0 g/L at 230 K and 16 bar. Hydrogen storage ; Chemistry ; Hydrogen ; Density functional theory ; Porosity ; Titanium ; Ambient pressure ; Carbide ; Bar (unit) ; Nanotechnology ; Chemical engineering ; Thermodynamics ; Computational chemistry ; Materials science ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science
Authors: Chih‐Hung Chen | Kuan‐Ying Chen | Yi-Chung Shu
Source: Journal of the Mechanics and Physics of Solids (article, 2024-09-19)
Field: Materials Science — Biomaterials

Data-driven bio-mimetic composite design: Direct prediction of stress–strain curves from structures using cGANs Composite number ; Strain (injury) ; Composite material ; Materials science ; Stress–strain curve ; Stress (linguistics) ; Mathematics ; Structural engineering ; Deformation (meteorology) ; Biology ; Engineering ; Anatomy Calcium Carbonate Crystallization and Inhibition ; Material Selection and Properties ; Machine Learning in Materials Science
Authors: Tao Chen | Qianrui Liu | Yu Liu | Liang Sun | Mohan Chen
Source: Matter and Radiation at Extremes (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter In traditional finite-temperature Kohn–Sham density functional theory (KSDFT), the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome this limitation. Recently, SDFT and the related mixed stochastic–deterministic density functional theory, based on a plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Q. Liu and M. Chen, Phys. Rev. B 106, 125132 (2022)]. In this study, we combine SDFT with the Born–Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Subsequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. Density functional theory ; Molecular dynamics ; Statistical physics ; Functional theory ; Eigenvalues and eigenvectors ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Peter B. R. Hartog | Fabian Krüger | Samuel Genheden | Igor V. Tetko
Source: Journal of Cheminformatics (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to produce human-understandable and consistent interpretations. In computational toxicity, augmentation of text-based molecular representations has been used successfully for transfer learning on downstream tasks. Augmentations of molecular representations can also be used at inference to compare differences between multiple representations of the same ground-truth. In this study, we investigate the robustness of eight XAI methods using test-time augmentation for a molecular-representation model in the field of computational toxicity prediction. We report significant differences between explanations for different representations of the same ground-truth, and show that randomized models have similar variance. We hypothesize that text-based molecular representations in this and past research reflect tokenization more than learned parameters. Furthermore, we see a greater variance between in-domain predictions than out-of-domain predictions, indicating XAI measures something other than learned parameters. Finally, we investigate the relative importance given to expert-derived structural alerts and find similar importance given irregardless of applicability domain, randomization and varying training procedures. We therefore caution future research to validate their methods using a similar comparison to human intuition without further investigation. SCIENTIFIC CONTRIBUTION: In this research we critically investigate XAI through test-time augmentation, contrasting previous assumptions about using expert validation and showing inconsistencies within models for identical representations. SMILES augmentation has been used to increase model accuracy, but was here adapted from the field of image test-time augmentation to be used as an independent indication of the consistency within SMILES-based molecular representation models. Computer science ; Intuition ; Inference ; Artificial intelligence ; Machine learning ; Robustness (evolution) ; Variance (accounting) ; Natural language processing ; Psychology ; Cognitive science Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Hisham Kazim | Moin Sabri | Amani Al–Othman | Muhammad Tawalbeh
Source: Nano-Structures & Nano-Objects (article, 2024-10-10)
Field: Materials Science — Polymers and Plastics

Functionalized conducting polymers in photocatalysis and opportunities for artificial intelligence applications Photocatalysis ; Nanotechnology ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Organic chemistry ; Catalysis Conducting polymers and applications ; Polyoxometalates: Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: Akiyasu Yamamoto | Shinnosuke Tokuta | Akimitsu Ishii | Akinori Yamanaka | Yusuke Shimada | Mark Ainslie
Source: NPG Asia Materials (article, 2024-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design Abstract Iron-based high-temperature (high- T c ) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high- T c superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba 0.6 K 0.4 Fe 2 As 2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability. Magnet ; Materials science ; Condensed matter physics ; Grain boundary ; Superconductivity ; Crystallite ; Supercurrent ; Engineering physics ; Coherence length ; Microstructure ; Anisotropy ; Pairing ; High-temperature superconductivity ; Nanotechnology ; Mechanical engineering ; Metallurgy ; Physics ; Josephson effect ; Optics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Rare-earth and actinide compounds