jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raghav Dangayach | Nohyeong Jeong | Yongsheng Chen
Source: ACS ES&T Engineering (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning Analysis and Monomer Screening of Polyamide Nanofiltration Membranes for Lithium Separation Nanofiltration (NF) membranes are increasingly being used to achieve precise solute-solute separation. These membranes are commonly synthesized using interfacial polymerization, offering great potential to separate lithium from magnesium. In this study, we have developed machine learning models that relate fabrication conditions, membrane properties, and operational conditions of NF membranes to predict water permeability and lithium/magnesium selectivity. Morgan fingerprints (MFs) and molecular descriptors (MDs) are used to represent the chemical and physical properties of the monomers. Explainable artificial intelligence tools such as Shapley additive explanations (SHAP) and partial dependence plots are used to evaluate the effects of the synthesis conditions and membrane properties on membrane performance. Based on the insights obtained from SHAP analysis, we developed a material screening approach to find promising monomers from a list of amines and cation-based ionic liquids. We construct a reference MF using the functional groups that positively contribute to membrane performance and compute a screening score that favors potential candidates with more desirable MDs. Finally, the synthesizability of these monomers is assessed using the synthetic accessibility score to find the most promising candidates. We compared the performance of screened monomers against traditional ones to validate the reliability of our approach. The results of this study provide critical insights into the relationships between synthesis conditions, membrane properties, and performance and establishes a novel, strategic framework for rational screening of monomers for NF membrane synthesis. This approach holds promise to accelerate the discovery of high-performance membranes tailored for specific separation challenges, thereby advancing the field of membrane technology. Membrane ; Nanofiltration ; Monomer ; Polyamide ; Synthetic membrane ; Molecular descriptor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Membrane Separation Technologies
Authors: Martin Priessner | Richard J. Lewis | Isak Lemurell | Magnus Johansson | Jonathan M. Goodman | Jon Paul Janet | Anna Tomberg
Source: Angewandte Chemie International Edition (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Advancing Structure Elucidation with a Flexible Multi‐Spectral AI Model Validating chemical synthesis success requires confirming the desired product using various analytical techniques. While spectroscopic data collection is increasingly automated, interpreting results remains a major bottleneck, often requiring expert input. With advances in laboratory automation and high-throughput synthesis, this challenge is expected to intensify. We introduce the MultiModalSpectralTransformer (MMST), a machine learning method that predicts chemical structures directly from diverse spectral data (NMR, IR, and MS). Trained on 4 million simulated compounds, MMST achieves 72% and 80% as top-1 and top-3 accuracy, respectively. To address out-of-distribution challenges, we implemented an active learning improvement cycle that generates molecules in similar chemical spaces, enabling the model to adapt to chemical structures beyond its original training data. We demonstrate MMST's capabilities through comprehensive benchmarking across diverse molecular weight ranges and chemical spaces. Notably, despite training solely on simulated data, MMST demonstrates good performance with experimental spectra. This research represents a significant advancement in automated structure elucidation, offering a powerful and adaptable tool that bridges the gap between simulated and real-world data. Benchmarking ; Automation ; Data collection ; Product (mathematics) ; Chemical process ; Training set Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Steve Dave Wansi Wendji | Rémi Piotrowski | Antonio Familiari | Carlo Massobrio | Mauro Boero | Christine Tugène | Firas Shuaib | David Hamani | Pierre‐Marie Geffroy | Philippe Thomas | Alfonso Pedone | Assil Bouzid | Olivier Masson | Gaëlle Delaizir | Guido Ori
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Structure, bonding and ionic mobility in Na–V–P–O glasses for energy storage applications Na-V-P-O glasses are promising materials for sodium ion batteries, and yet a thorough understanding of their atomic scale behavior has so far been elusive. In this work we leverage structural and electrochemical experiments with first-principles and large-scale machine learning-accelerated molecular dynamics to elucidate quantitatively the interplay among structure, bonding, and ion mobility on space and time scales of unprecedented extensions. We unravel the existence of a broad V coordination distribution together with heterogeneous Na-ion mobility featuring percolation channels. Our results are instrumental in the search for NVP glass optimization for electrochemical applications. Ionic bonding ; Ion ; Sodium ; Cathode ; Battery (electricity) ; Materials science ; Energy storage ; Crystallography ; Chemistry ; Chemical physics ; Physical chemistry ; Thermodynamics ; Physics ; Metallurgy ; Organic chemistry Glass properties and applications ; Advancements in Battery Materials ; Transition Metal Oxide Nanomaterials
Authors: Yusuf Uzunoğlu | Yusuf Alaca
Source: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi (article, 2025-03-26)
Field: Materials Science — Materials Chemistry

Inverse Prediction of the CALPHAD-Modeled Physical Properties of Superalloys Using Explainable Artificial Intelligence and Artificial Neural Networks The CALPHAD methodology models the physical, mechanical, and thermodynamic properties of materials based on specified alloy compositions using phase equilibrium calculations and thermodynamic databases. With the CALPHAD approach, millions of material-property data can be obtained for each alloy composition over various temperature ranges. However, finding an alloy with the desired properties often requires lengthy trial-and-error processes that involve manually adjusting the composition. In this study, the goal is to inverse this approach using artificial intelligence to predict alloy compositions that yield the desired properties. Accordingly, in the JMatPro software based on the CALPHAD methodology, the physical properties (density, thermal conductivity, linear expansion, Young's modulus, bulk modulus, shear modulus, and Poisson's ratio) of 250 different Ni-Cr-Fe-based superalloys in the temperature range of 540–920 °C were modeled. A dataset with 5000 rows was created from the generated data, of which 80% was used to train the artificial intelligence model, while 20% was reserved for validation and testing. Through analyses using Explainable Artificial Intelligence (XAI) and Artificial Neural Networks (ANN), alloy compositions with the desired physical properties at a given temperature were predicted with a high accuracy rate of 98.03%. In conclusion, beyond obtaining material properties from alloy compositions through the CALPHAD approach, artificial intelligence techniques make it possible to accurately inverse predict alloy compositions that yield specified physical properties at a particular temperature. Artificial neural network ; CALPHAD ; Inverse ; Artificial intelligence ; Computer science ; Mathematics ; Chemistry ; Phase diagram Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Qiaoling Li | Lei Ju | Lu Zeng | Zhongli Ye | Hui Liang | Ting Fei | Guotian Cai | Yanping Lin | Wei Deng | Yi Wang
Source: Journal of Analytical and Applied Pyrolysis (article, 2025-09-22)
Field: Chemistry — Analytical Chemistry

Minireview on tobacco classification technologies: A vital bridge linking raw leaf properties with end product quality Standardization ; Hyperspectral imaging ; Bridge (graph theory) ; Consistency (knowledge bases) ; Identification (biology) ; Raw data ; Raw material ; Deep learning Spectroscopy and Chemometric Analyses ; Potato Plant Research ; Phytochemicals and Antioxidant Activities
Authors: Mashqoor Alam | Samina Husain
Source: Composites Communications (article, 2025-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-driven optimization and compositional experimental validation of polythiophene/graphene nanoplatelet nanocomposite for symmetric supercapacitor Polythiophene ; Supercapacitor ; Nanocomposite ; Materials science ; Graphene ; Nanotechnology ; Conductive polymer ; Composite material ; Polymer ; Capacitance ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Graphene and Nanomaterials Applications
Authors: Ogheneyoma Aghoghovbia | Riccardo Rurali | Mohammed Al‐Fahdi | Joshua Ojih | De‐en Jiang | Ming Hu
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted discovery of lattice dynamics signatures of sodium superionic conductors Sodium superionic conductors are key to the development of all-solid-state sodium batteries. Discovery of new superionic conductors has traditionally relied on insights from material defect chemistry and the transition/hopping theory, while the role of lattice vibrations, <i>i.e.</i>, phonons, remains underexplored. We identify key lattice dynamics signatures that govern ionic conductivity by analyzing the phonon mean squared displacement (MSD) of Na<sup>+</sup> ions. By high-throughput screening of a dataset of 3903 Na-containing structures, we establish a strong positive correlation between phonon MSD and diffusion coefficients, providing a quantitative correlation between lattice dynamics and ion transport. To accelerate this discovery, we incorporate machine learning (ML) into our screening workflow, using phonon-derived descriptors to rapidly predict ionic transport properties across a broad structural space. Our findings reveal that low acoustic cutoff phonon frequencies, low center vibrational density of states of Na<sup>+</sup> ions, slightly higher than the acoustic cutoff frequencies, and enhanced low-frequency vibrational coupling between Na<sup>+</sup> ions and the host sublattice promote superionic conductivity. Phonon mode analysis further demonstrates that only a small subset of low-frequency acoustic and optic modes contribute dominantly to large phonon MSDs and Na<sup>+</sup> ion migration, while higher-energy modes contribute negligibly. These insights enable the integration of lattice dynamics descriptors, phonon MSD, Na<sup>+</sup> VDOS center, acoustic cutoff frequency, and low-frequency phonon coupling into machine learning frameworks, accelerating the discovery and rational design of high-performance sodium superionic conductors. Phonon ; Ion ; Lattice (music) ; Fast ion conductor ; Ionic bonding ; Molecular dynamics ; Coupling (piping) ; Mean squared displacement Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Solid-state spectroscopy and crystallography
Authors: Kevin Conley | Colton Gerber | Andrew C. Novick | Terra Berriodi | Eric S. Toberer | Antti J. Karttunen
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Heat transport properties of PbTe<sub>1−<i>x</i></sub>Se<sub><i>x</i></sub> alloys using equivariant graph neural network interatomic potential The suppression of heat transport in disordered crystals arises from a competition between mass fluctuations and bond disorder, but their relative contributions remain difficult to disentangle. We address this challenge using a machine-learned interatomic potential trained on <i>ab initio</i> data across the PbTe<sub>1-<i>x</i></sub>Se<sub><i>x</i></sub> alloy series. Molecular dynamics simulations with the trained machine-learned interatomic potential reproduce experimental lattice thermal conductivities and density-functional theory phonon dispersions while enabling frequency-resolved analysis of heat transport from 300 to 800 K. We find a narrow window near 1.7-2.0 THz dominates heat transport across all compositions, where heat is primarily carried by mixed longitudinal acoustic and optical modes. Alloying dramatically reduces spectral diffusivity near 2 THz leading to the deterioration of thermal conductivity. For the parent compounds both the spectral diffusivity and overall thermal conductivity decrease at elevated temperatures. However, the thermal degradation is weaker for the mid-range composition (<i>x</i> = 0.5) due to the greater thermal occupation of vibrational modes and increased heat capacity. Alchemical simulations show that force-constant disorder, not mass contrast, plays the dominant role in this suppression. These results highlight the microscopic mechanisms underlying thermal transport breakdown in alloys and demonstrate how machine-learned interatomic potentials now offer a tractable path toward predictive, physics-rich thermal transport modeling in complex disordered solids. Materials science ; Equivariant map ; Artificial neural network ; Graph ; Condensed matter physics ; Crystallography ; Physics ; Discrete mathematics ; Mathematics ; Computer science ; Chemistry ; Machine learning ; Pure mathematics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Zicun Li | Jianxing Huang | Xinguo Ren | Jinbin Li | Ruijuan Xiao | Hong Li
Source: Journal of Power Sources (article, 2025-03-08)
Field: Materials Science — Materials Chemistry

Mechanistic insights into temperature effects for ionic conductivity in Li <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e319"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>6</mml:mn> </mml:mrow> </mml:msub> </mml:math> PS <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.svg" display="inline" id="d1e327"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msub> </mml:math> Cl Ionic bonding ; Scalable Vector Graphics ; Computer science ; Chemistry ; Materials science ; Thermodynamics ; Physics ; Organic chemistry ; World Wide Web ; Ion Solid-state spectroscopy and crystallography ; Advanced Battery Materials and Technologies ; Thermal and Kinetic Analysis
Authors: Oleh Yasniy | Dmytro Tymoshchuk | Iryna Didych | Roman Zolotyi | Vitaliy Tymoshchuk
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modeling of shape memory alloys hysteresis behavior considering the loading cycle frequency Shape memory alloys (SMAs) can restore their original shape when exposed to temperature changes or mechanical stress. One of the main characteristics of SMA is superelasticity, which allows the material to return to its original shape after deformation. This property results from phase transitions between martensite and austenite under loading or temperature changes. The loading frequency plays an important role in the behavior of shape memory alloys, as it can significantly affect their functional properties. This study uses artificial neural networks (ANNs) to predict the hysteresis behavior of SMA nickel-titanium alloy at different cyclic loading frequencies. The experimentally obtained hysteresis loops for four loading frequencies were used to train the artificial neural network: 0.1, 0.5, 1, and 5 Hz. The input data contained such parameters as stress σ (MPa), cycle number N , and loading frequency f (Hz). The neural network learned to recognize patterns in the material behavior by analyzing the functional relationships between these parameters. This made it possible to predict the strain ε (%) at different frequencies and loading conditions. The results show high accuracy of material behavior predictions, which is confirmed by the small values of MAE, MSE, and MAPE errors. Hysteresis ; Shape-memory alloy ; Materials science ; Structural engineering ; Mechanics ; Composite material ; Engineering ; Physics ; Condensed matter physics Shape Memory Alloy Transformations ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Wai Yuet Chiu | C. H. Zhang | Rongzhi Gao | Ziyang Hu | GuanHua Chen
Source: Journal of Chemical Theory and Computation (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Open-Circuit Voltages of Lithium-Ion Batteries via Delta Learning Accurate prediction of lithium-ion battery capacity before material synthesis is crucial for accelerating battery material discovery. The capacity can be theoretically determined by integrating open-circuit voltage vs state of charge (OCV-SoC) curves of electrode materials. OCV-SoC curves are traditionally computed using first-principles methods, either through geometry optimization (GO) with density functional theory (DFT) or molecular dynamics (MD) simulations of lithiation/delithiation processes using DFT or force fields. While MD simulations incorporate temperature effects that GO lacks, even DFT-based MD simulated OCV-SoC curves show systematic deviations from experimental results due to inherent approximations in DFT functionals. In this study, we performed MD simulations on 43 cathode materials to obtain their OCV-SoC curves. Initial results showed only moderate agreement with experimental data, yielding a coefficient of determination (<i>R</i><sup>2</sup>) of 0.249 and a mean absolute error (MAE) of 1.561 V. Considering the scarcity of data, we implemented a delta learning approach to calibrate the MD results without substantial computational overhead, achieving an improved <i>R</i><sup>2</sup> of 0.933 and an MAE of 0.131 V on the testing set. This calibration method significantly enhanced the accuracy of energy density predictions, reducing the MAE from 106.0 to 10.7 Wh/kg. We also developed an automated delta learning platform to make this approach accessible to researchers without machine learning expertise. Open-circuit voltage ; Computer science ; Lithium (medication) ; Ion ; Voltage ; Electrical engineering ; Chemistry ; Engineering ; Psychology Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fault Detection and Control Systems
Authors: Zechen Tang | Haoxiang Chen | Yang Li | Yubing Qian | Yuxiang Wang | Weizhong Fu | Jialin Li | Chen Si | WenHui DUAN | Ji Chen | Yong Xu
Source: Nature Computational Science (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Deep-learning electronic structure calculations Electronic structure ; Density functional theory ; Quantum Monte Carlo ; Quantum ; Monte Carlo method ; Electron Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Lian Zhuo | Yan-Ping Zheng | Lei Zeng | Yilin Zhao | Meng Li | Chunying Rong | Shubin Liu
Source: Journal of Computational Chemistry (article, 2025-08-11)
Field: Chemistry — Organic Chemistry

Quantification of Lewis Acidity and Lewis Basicity: A Density‐Based Reactivity Theory Study Lewis acidity and basicity are among the most widely applied concepts across chemistry, biology, and related disciplines. Yet, their accurate calculation and prediction remain challenging. In this study, we employ descriptors derived from density-based reactivity theory to offer a new and quantitative perspective. To this end, we analyzed four series of Lewis acids and bases across two types of reactions. Our results demonstrate that Lewis acidity and basicity can be effectively quantified using a range of global and local descriptors from conceptual density functional theory and an information-theoretic approach in density functional theory. Additionally, various electronic properties, including frontier molecular orbitals, molecular electrostatic potential, natural valence atomic orbital energies, and several types of atomic charges, were identified as robust descriptors. Leveraging these features, we constructed machine-learning models capable of accurately predicting Lewis acidity and basicity. We also uncovered a strong correlation between Lewis acidity/basicity and electrophilicity/nucleophilicity, further bridging these conceptual frameworks. The consistent high correlations obtained across descriptors, coupled with the performance of our machine learning models, confirm that Lewis acidity and Lewis basicity can be quantitatively characterized with high fidelity. This work suggests that density-based frameworks could provide a powerful and novel foundation for understanding the hard and soft acids and bases principle. Lewis acids and bases ; Chemistry ; Density functional theory ; Computational chemistry ; Valence (chemistry) ; Atomic orbital ; Organic chemistry ; Catalysis ; Electron ; Physics Chemical Reaction Mechanisms ; Advanced Chemical Physics Studies ; Organic Chemistry Cycloaddition Reactions
Authors: Jan Elsner | K. Nikolas Lausch | Jörg Behler
Source: Chemical Physics Reviews (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Atomistic simulations of oxide–water interfaces using machine learning potentials Oxide–water interfaces govern a wide range of physical and chemical processes fundamental to many fields like catalysis, geochemistry, corrosion, electrochemistry, and sensor technology. Near solid oxide surfaces, water behaves differently than in the bulk, exhibiting pronounced structuring and increased reactivity, typically requiring ab initio-level accuracy for reliable modeling. However, explicit ab initio calculations are often computationally prohibitive, especially if large system sizes and long simulation time scales are required. By “learning” the potential energy surface (PES) from data obtained from electronic structure calculations, machine learning potentials (MLPs) have emerged as transformative tools, enabling simulations with ab initio accuracy at dramatically reduced computational expense. Here, we provide an overview of recent progress in the application of MLPs to atomistic simulations of oxide–water interfaces. Specifically, we review insights that have been gained into the reactivity of interfacial systems involving the dissociation and recombination of water molecules, proton transfer processes between the solvent and the surface and the dynamic nature of aqueous oxide surfaces. Moreover, we discuss open challenges and future possible research directions in this rapidly evolving but challenging field. Ab initio ; Dissociation (chemistry) ; Ab initio quantum chemistry methods ; Range (aeronautics) ; Experimental data ; Physical system ; Electronic structure Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions