jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guosen Wang | Xia Huang | Changmin Guo | Chuanyu Zhang | Xinlu Cheng
Source: Physical review. A/Physical review, A (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Unveiling <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Mg</mml:mi><mml:msub><mml:mi mathvariant="normal">C</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> reaction dynamics using a permutation-invariant-polynomial neural-network potential energy surface constructed via the trust region framework Recognized as an astrophysical molecule, $\mathrm{Mg}{\mathrm{C}}_{2}$ has garnered significant attention. In this study, we employed the DW-SA-CASSCF/icMRCI+Q/AV(Q+d)Z (Dynamic-weighting-state-averaged-complete active space self-consistent field/Internally contracted multi reference configuration interaction with Davidson correction based on augmented valence quadruple zeta with added diffuse and polarization functions) method to compute high-precision ab initio energy points. A neural-network approach was subsequently utilized to fit the global potential energy surface of the $\mathrm{Mg}{\mathrm{C}}_{2}$ molecule in its $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{X}{\phantom{\rule{0.16em}{0ex}}}^{1}{A}_{1}$ state. In contrast to previous methods, the optimization of neural-network parameters in this study was carried out using the trust region framework (TRF). The TRF algorithm exhibits superior performance in parameter optimization and demonstrates reduced dependency on neuron parameters compared to the Levenberg-Marquardt algorithm. Utilizing the obtained potential energy surface, three reaction pathways for $\mathrm{MgC}(X{\phantom{\rule{0.16em}{0ex}}}^{3}{\mathrm{\ensuremath{\Sigma}}}^{\ensuremath{-}})+\mathrm{C}(^{3}P_{g})\ensuremath{\rightarrow}\mathrm{Mg}+{\mathrm{C}}_{2}$ were identified, confirming that this reaction proceeds as a barrierless exothermic process. To further verify the accuracy of the fitted potential energy surface, the reaction cross section was calculated under the initial condition $v=0,j=0,20,50,80,120$ for this reaction. By integrating the cross section with the relative translational energy, rate coefficients were determined under various initial conditions. Under the initial condition $v=0,j=0$, the rate coefficient is at its maximum, and the trend of the rate coefficient with temperature first decreases and then increases. At a temperature of 100 K, the rate coefficient reaches its minimum value. At low temperatures, the rate coefficient is very high, which is consistent with the characteristics of a barrierless exothermic reaction. Computer science ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xiao-Bin Liu | Ning Shao | Qiuxia Huang | Lei Zhang | Zhenhua Gao | Yapeng Tian | Haiquan Wang | Huanfu Zhou
Source: Materials Today Chemistry (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Enhanced permittivity prediction in microwave dielectric ceramics and structure-property insights via CGCNN combined with GNNExplainer Electronegativity ; Permittivity ; Crystal (programming language) ; Convolutional neural network ; Artificial neural network ; Dielectric ; Microwave ; Feature (linguistics) Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: W. O. N. Haryanti | I Wayan Sutapa | Idin Sahidin | L. Ahmad | La Ode Ahmad Nur Ramadhan
Source: International Journal of Acta Material (review, 2025-08-31)
Field: Chemistry — Physical and Theoretical Chemistry

Unveiling Non-Covalent Molecular Interactions: A Comprehensive Review Integrating DFT and Crystallographic Insights Non-covalent interactions (NCIs) are crucial in elucidating molecular recognition, supramolecular architecture, and material properties. This review synthesizes findings from 20 recent studies (2013–2025), with a focus on key interaction types: hydrogen bonding, π–π stacking, Van der Waals forces, and less prevalent modes such as chalcogen bonding and excited-state interactions. Computational methodologies, particularly dispersion-corrected density functional theory (DFT), benchmark datasets (e.g., S22, HB300SPX), and emerging machine learning (ML) corrections, have markedly enhanced the accuracy of NCI modeling. Experimental techniques, including X-ray diffraction and infrared/Raman spectroscopy, continue to substantiate theoretical predictions. Hydrogen bonds are predominant in influencing structural stability and crystal packing, while π–π interactions play a vital role in stabilizing aromatic systems. Van der Waals interactions are particularly significant in layered materials and adsorption phenomena. Recent advancements, such as neural network potentials (NNPs) and non-empirical functionals (e.g., r²SCAN+MBD@HF), provide accurate predictions even in complex environments. This review underscores the interplay between theoretical and experimental approaches and highlights prospective directions in modeling weak interactions across a range of chemical systems. Covalent bond ; Crystallography ; Materials science ; Chemistry ; Nanotechnology Crystallography and molecular interactions ; Nonlinear Optical Materials Research ; Machine Learning in Materials Science
Authors: Joy Datta | Dibakar Datta
Source: Journal of the Indian Institute of Science (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Electro-Chemo-Mechanical Properties of 2D Materials for Energy Storage: Computational Frontiers Abstract Two-dimensional materials (2DM) and their heterostructures (2D + nD, where n = 0, 1, 2, 3) hold significant promise for electrochemical energy storage systems (EESS), such as batteries. 2DM can act as van der Waals (vdW) slick interfaces between conventional active materials (e.g., silicon) and current collectors, enhancing interfacial adhesion and mitigating stress-induced fractures. They can also serve as alternatives to traditional polymer binders (e.g., MXenes), highlighting the importance of interfacial mechanics between 2DM and active materials. During charge/discharge cycles, intercalation and deintercalation processes substantially affect the mechanical behavior of 2DM used as binders, collectors, or electrodes. For example, porous graphene networks have demonstrated capacities up to five times greater than traditional graphite anodes. However, modeling 2DM in EESS remains challenging due to the complex coupling between electrochemistry and mechanics. Defective graphene, for instance, promotes strong adatom adsorption (e.g., Li⁺), which can hinder desorption during discharge, thereby influencing mechanical properties. Despite the promise of 2DM, most current studies fall short in capturing these critical chemo-mechanical interactions. This perspective provides a comprehensive overview of recent advances in understanding the mechanical behavior of 2DM in EESS. It identifies key modeling challenges and outlines future research directions. Multiscale modeling approaches—including atomistic and molecular simulations, continuum mechanics, machine learning, and generative artificial intelligence—are discussed. This work aims to inspire deeper exploration of the chemo-mechanics of 2DM and offer valuable guidance for experimental design and optimization of 2DM-based EESS for practical applications. Energy storage ; Engineering physics ; Mechanical engineering ; Materials science ; Computer science ; Engineering ; Physics ; Thermodynamics 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices
Authors: Oliver Umney | Hayley Slaney | Christopher J.M. Williams | Philip Quirke | Michelle Peckham | Alistair Curd
Source: Small Science (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

ClusterNet: Classifying Single‐Molecule Localization Microscopy Datasets with Graph‐Based Deep Learning of Supracluster Structure Single-molecule localization microscopy (SMLM) data can reveal differences in protein organization between different disease types or samples. Classification of samples is an important task that allows for automated recognition and grouping of data by sample type for downstream analysis. However, methods for classifying structures larger than single clusters of localizations in SMLM point-cloud datasets are not well developed. A graph-based deep learning pipeline is presented for classification of SMLM point-cloud data over a field of view of any size. The pipeline combines features of individual clusters (calculated from their constituent localizations) with the structure formed by the positions of multiple clusters (supracluster structure). This method outperforms previous classification results on a model open-source DNA-PAINT dataset, with 99% accuracy. It is also applied to a challenging new SMLM dataset from colorectal cancer tissue. Explainability tools Uniform Manifold Approximation and Projection and SubgraphX allow exploration of the influence of spatial features and structures on classification results, and demonstrate the importance of supracluster structure in classification. Pipeline (software) ; Pattern recognition (psychology) ; Deep learning ; Projection (relational algebra) ; Field (mathematics) ; Task (project management) ; Sample (material) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Brett A. Smith | Haiyi Wu | Difan Zhang | Vassiliki‐Alexandra Glezakou
Source: ChemRxiv (preprint, 2025-03-10)
Field: Chemistry — Spectroscopy

Examining Solvents for 2,3-Butanediol Extraction From Water Using Molecular Modeling and Machine Learning The work presented in this study highlights an interesting and growing area in the biochemical separations field. Biochemical separations of 2,3-butanediol from a fermentation broth presents a non-trivial and multifaceted task that requires careful consideration and a deep understanding of the solvent-solute interactions present in the mixture. In this study, we have developed a database of 39,397 containing data with unique solvent-solute pairs, their SMILES string representation and the solubility of the pair. We have further partitioned these into organic and aqueous subsets that were used in predicting a solvent's affinity for 2,3-butanediol and water. Regression models were rigorously trained on both subsets and the models were further used for their predictive capabilities. In harnessing these predictive capabilities, we have validated the performance in systematic way utilizing a small experimental subset, density functional theory calculations and classical molecular dynamic simulations to ensure agreement amongst the available data and methods. Extraction (chemistry) ; 2,3-Butanediol ; 1,4-Butanediol ; Chromatography ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry Analytical Chemistry and Chromatography
Authors: Shuang Liao | Liling Wang | Cheng Chen | Shuangli Luo | Lin Xu | Houjun Wang | Peng Ye | Mengmeng Sun | Feng Tan
Source: Microchemical Journal (article, 2025-10-13)
Field: Chemistry — Analytical Chemistry

A novel molecularly imprinted polymers-QCM sensing platform: Design, construction and application in intelligent detection of Hexaconazole Molecularly imprinted polymer ; Detection limit ; Quartz crystal microbalance ; Adsorption ; Molecular imprinting ; Linear range Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Analytical Chemistry and Chromatography
Authors: C. Frueh | Caglayan Aras | Ömer Büyükuslu | Moritz to Baben
Source: Calphad (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

aiMP and aiOQ databases in FactSage: Materials informatics relying on ab initio, machine learning and CALPHAD data Informatics ; Ab initio ; CALPHAD ; Database ; Computer science ; Materials science ; Chemistry ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Marius Juul Nielsen | L. Kempen | Jôrgen Ravn | Raffaele Cheula | Mie Andersen
Source: The Journal of Chemical Physics (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Interpretable machine learned predictions of adsorption energies at the metal–oxide interface The conversion of CO2 to value-added compounds is an important part of the effort to store and reuse atmospheric CO2 emissions. Here, we focus on CO2 hydrogenation over so-called inverse catalysts: transition metal oxide clusters supported on metal surfaces. The conventional approach for computational screening of such candidate catalyst materials involves a reliance on density functional theory (DFT) to obtain accurate adsorption energies at a significant computational cost. Here, we present a machine learning (ML)-accelerated workflow for obtaining adsorption energies at the metal-oxide interface. We enumerate possible binding sites at the clusters and use DFT to sample a subset of these with diverse local adsorbate environments. The dataset is used to explore interpretable and black-box ML models with the aim of revealing the electronic and structural factors controlling adsorption at metal-oxide interfaces. Furthermore, the explored ML models can be used for low-cost prediction of adsorption energies on structures outside of the original training dataset. The workflow presented here, along with the insights into trends in adsorption energies at metal-oxide interfaces, will be useful for identifying active sites, predicting parameters required for microkinetic modeling of reactions on complex catalyst materials, and accelerating data-driven catalyst design. Workflow ; Oxide ; Adsorption ; Catalysis ; Density functional theory ; Metal ; Inverse ; Materials science ; Reuse ; Complex oxide ; Computer science ; Interface (matter) ; Chemistry ; Computational chemistry ; Physical chemistry ; Mathematics ; Database ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Joris Meurs | Aishah Nasir | Grazziela P. Figueredo | Laurence Burroughs | Salah M.A. Abdelrazig | Chris Denning | David A. Winkler | David A. Barrett | Dong‐Hyun Kim | Morgan R. Alexander
Source: Analytical Chemistry (article, 2025-06-10)
Field: Chemistry — Spectroscopy

High-Throughput Analysis of Protein Adsorption to a Large Library of Polymers Using Liquid Extraction Surface Analysis–Tandem Mass Spectrometry (LESA-MS/MS) Biomaterials play an important role in medicine from contact lenses to joint replacements. High-throughput screening coupled with machine learning has identified synthetic polymers that prevent bacterial biofilm formation, prevent fungal cell attachment, control immune cell attachment and phenotype, or direct stem cell fate. In-vitro preadsorption of proteins from culture medium plays a pivotal role in controlling cell response. However, there is a paucity of studies on the screening of protein adsorption into material libraries. Here, we show how quantitative analysis of protein adsorption on a 208-member polymer microarray can be achieved using liquid extraction surface analysis, combined with an adaptation of the droplet microarray (DMA) approach and tandem mass spectrometry (LESA-MS/MS) for protein identification. This study uses a fully defined cell culture medium containing only four proteins (Essential 8) to demonstrate the feasibility of the analysis approach. Our findings show that we can generate quantitative and predictive machine learning models of protein adsorption that elucidate key polymer features that describe the relationship between surface chemistry and protein adsorption. This information is of use for the rational design of new materials with bespoke protein attachment properties for biomaterials, medical devices, or in vitro compound screening. Chemistry ; Tandem ; Tandem mass spectrometry ; Chromatography ; Adsorption ; Mass spectrometry ; Extraction (chemistry) ; Polymer ; Throughput ; Liquid chromatography–mass spectrometry ; Analytical Chemistry (journal) ; Organic chemistry ; Aerospace engineering Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical chemistry methods development
Authors: Zhuang Xu | Ziming Wang | Rui Zhang | Archana Verma | Justin Neu | Priyotosh Bairagya | Sanghyun Jeon | Alec M. Damron | Joshua M. Rinehart | Justine Wagner | Sina Sabury | Xuyi Luo | Anna M. Österholm | Jianguo Mei | Jeffrey S. Moore | Wei You | John R. Reynolds | Nicholas E. Jackson | Ying Diao
Source: Journal of the American Chemical Society (article, 2025-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ubiquitous Chiral Symmetry Breaking of Conjugated Polymers via Liquid–Liquid Phase Separation Spontaneous chiral symmetry breaking remains a fascination in chemistry, biology, materials science, and even astronomy. Chiral symmetry breaking usually requires intrinsic molecular chirality or extrinsic chiral sources but remains rare in nonchiral systems. Here, we reveal a ubiquitous, entropy-driven chiral symmetry breaking mechanism observed in 22 out of 35 conjugated polymers in the absence of any chiral source─a phenomenon overlooked for decades. Chiral assemblies spontaneously occur through liquid-liquid phase separation (LLPS) of lyotropic mesophases from isotropic solutions upon a concentration increase. Machine learning identifies the underpinning molecular features validated by further molecular design. The universality of this phenomenon hints at a possible link between LLPS and chiral symmetry breaking in the origin of life, while paving the way for an emerging frontier of chiral electronics. Chiral symmetry breaking ; Symmetry breaking ; Chirality (physics) ; Chiral anomaly ; Lyotropic ; Polymer ; Spontaneous symmetry breaking ; Conjugated system Liquid Crystal Research Advancements ; Synthesis and Properties of Aromatic Compounds ; Lipid Membrane Structure and Behavior
Authors: Chih‐Shan Tan
Source: CrystEngComm (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating materials property discovery in uncharted domains through the integration of high-throughput computation and machine learning This work presents a data-driven framework that integrates HT computations and ML to accelerate materials discovery, enabling efficient feature extraction, structure–property analysis, and performance prediction for various applications. Property (philosophy) ; Throughput ; Computer science ; Computation ; Drug discovery ; Artificial intelligence ; Machine learning ; Biology ; Bioinformatics ; Algorithm ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Shuai He | Chao Gong | Mengru Li | Jiahang Wu | Qingjie Xi | Xiaoyu Geng | Xinchao Li | Heng‐Yong Nie | Xiaoyan Zhu | Zhenlian Han | Dingsheng Wang | Ligang Wang | Peng Liu
Source: Chinese Chemical Letters (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Atomic precision of cobalt single-atom nanozymes in environmental remediation and energy sustainability Cobalt ; Environmental remediation ; Sustainability ; Catalysis ; Pollutant ; Leverage (statistics) ; Scalability Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Zhuang Xu | Ziming Wang | Rui Zhang | Archana Verma | Justin Neu | Priyotosh Bairagya | Sanghyun Jeon | Alec M. Damron | Joshua M. Rinehart | Justine Wagner | Sina Sabury | Xuyi Luo | Anna M. Österholm | Jianguo Mei | Jeffrey S. Moore | Wei You | John R. Reynolds | Nicholas E. Jackson | Ying Diao
Source: ChemRxiv (preprint, 2025-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ubiquitous Chiral Symmetry Breaking of Conjugated Polymers via Liquid-Liquid Phase Separation Spontaneous chiral symmetry breaking remains a fascination in chemistry, biology, materials science, and even astronomy. Chiral symmetry breaking usually requires intrinsic molecular chirality or extrinsic chiral sources but remains rare in non-chiral systems. Here, we reveal a ubiquitous, entropy-driven chiral symmetry breaking mechanism observed in 22 out of 35 conjugated polymers in the absence of any chiral source – a phenomenon overlooked for decades. Chiral assemblies spontaneously occur through liquid-liquid phase separation (LLPS) of lyotropic mesophases from isotropic solutions upon concentration increase. Machine learning identifies underpinning molecular features validated by further molecular design. The universality of this phenomenon hints at a possible link between LLPS and chiral symmetry breaking in the origin of life while paving the way for an emerging frontier of chiral electronics. Conjugated system ; Chiral symmetry breaking ; Liquid liquid ; Polymer ; Phase (matter) ; Symmetry breaking ; Chiral symmetry ; Chemical physics ; Symmetry (geometry) ; Materials science ; Physics ; Chemistry ; Chromatography ; Organic chemistry ; Particle physics ; Mathematics Liquid Crystal Research Advancements ; Innovative Microfluidic and Catalytic Techniques Innovation ; Analytical Chemistry and Chromatography
Authors: Jingxuan Ding | Laura Zichi | Matteo Carli | M. J. Wang | Albert Musaelian | Yu Xie | Boris Kozinsky
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Materials Chemistry

Coupled reaction and diffusion governing interface evolution in solid-state batteries Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid state batteries. However, challenges persist due to difficulties in experimentally characterizing buried interfaces and limits in simulation speed and accuracy. We conduct large-scale explicit reactive simulations with quantum accuracy for a symmetric battery cell, {\symcell}, enabled by active learning and deep equivariant neural network interatomic potentials. To automatically characterize the coupled reactions and interdiffusion at the interface, we formulate and use unsupervised classification techniques based on clustering in the space of local atomic environments. Our analysis reveals the formation of a previously unreported crystalline disordered phase, Li$_2$S$_{0.72}$P$_{0.14}$Cl$_{0.14}$, in the SEI, that evaded previous predictions based purely on thermodynamics, underscoring the importance of explicit modeling of full reaction and transport kinetics. Our simulations agree with and explain experimental observations of the SEI formations and elucidate the Li creep mechanisms, critical to dendrite initiation, characterized by significant Li motion along the interface. Our approach is to crease a digital twin from first principles, without adjustable parameters fitted to experiment. As such, it offers capabilities to gain insights into atomistic dynamics governing complex heterogeneous processes in solid-state synthesis and electrochemistry. Dendrite (mathematics) ; Cluster (spacecraft) ; Cluster analysis ; Interface (matter) ; Diffusion ; Multiscale modeling ; Reaction–diffusion system ; Position (finance) ; Battery (electricity) ; Interphase Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Electrocatalysts for Energy Conversion