jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gustavo Guzman | Jeffrey Ting | Shashi K. Murthy | Thomas X. Neenan | Sean Kevlahan
Source: ChemRxiv (preprint, 2025-07-28)
Field: Materials Science — Materials Chemistry

ChainSpace: Sequence-Aware Design for Machine Learning-Driven Polymer Engineering Sequence is the critical determinant of macromolecular function, yet current polymer design approaches often optimize monomer composition and ratios while ignoring sequence. This creates poorly defined design spaces for active learning that miss the vast combinatorial landscape of sequence possibilities. We introduce ChainSpace, a computational framework that maps synthetically accessible sequence space through four orthogonal control mechanisms: compositional (random copolymerization), kinetic (relative reactivity differences), temporal (staged monomer injection), and spatial (post-polymerization functionalization). Using three orthogonal descriptors (second eigenvalue λ₂, Shannon entropy, and mean run length), we characterize sequence statistics across experimentally achievable synthesis conditions. Parameter space exploration reveals that accessible sequences form a curved three-dimensional manifold with distinct regions accessed by different synthesis strategies. Kinetic control provides the broadest sequence access, temporal control enables highly blocky architectures, and spatial control favors alternating motifs. We demonstrate this framework using cationic polymers for gene delivery, where sequence statistics correlate with transfection efficiency and salt resistance. A top-performing, multi-component gene delivery polymer achieved 85% transfection efficiency, with sequence analysis revealing optimal blockiness and entropy values. This sequence-centric approach provides a systematic foundation for active learning design spaces, enabling rational navigation of sequence space that is especially useful for the advancement of multimonomeric polymers for industrially relevant end-use technologies. Sequence (biology) ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Suvi Heinonen | Artturi Koivuniemi | Matthew Davies | Mikko Karttunen | Camilla Foged | Alex Bunker
Source: Journal of Chemical Information and Modeling (article, 2025-07-03)
Field: Chemistry — Spectroscopy

Combination of Molecular Dynamics Simulations and Machine Learning Reveals Structural Characteristics of Stereochemistry-Specific Interdigitation of Synthetic Monomycoloyl Glycerol Analogs Synthetic monomycoloyl glycerol (MMG) analogs possess robust immunostimulatory activity and are investigated as adjuvants for subunit vaccines in preclinical and clinical studies. These synthetic lipids consist of a glycerol moiety attached to a corynomycolic acid. Previous experimental studies have shown that the stereochemistry of the lipid acid moiety affects whether the MMG analogs self-assemble into interdigitated or noninterdigitated structures below the main phase transition temperature (<i>T</i><sub>m</sub>). In this study, we elucidated possible thermodynamic mechanisms governing the phase behavior of MMG analogs by exploring their conformations, interactions, and dynamics using a combination of machine learning (ML) and molecular dynamics (MD) simulations. We compared two analogs, MMG-1 and MMG-6, which differ only by the stereochemistry of the lipid acid moiety; the former has a configuration different from the natural MMG, and the latter displays a native-like stereochemistry. Three different membrane states were simulated: (1) a noninterdigitated single bilayer, (2) a noninterdigitated double bilayer, and (3) a fully interdigitated double bilayer. Our results indicate that the propensity for interdigitation of the MMG analogs in a bilayer is linked to the degree to which their hydrocarbon chains are ordered and oriented. This study demonstrates how combining MD simulations with ML can enhance the molecular understanding of lipid-based pharmaceutical formulations. Glycerol ; Molecular dynamics ; Dynamics (music) ; Chemistry ; Stereochemistry ; Computational chemistry ; Organic chemistry ; Physics Analytical Chemistry and Chromatography ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: R. Zhou | Kun Luo | Qi An
Source: Chemical Communications (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven atomistic modeling of crystalline and glassy solid-state electrolytes All-solid-state batteries promise safer, more stable, and higher-energy-density storage, but progress hinges on atomistic insight into solid electrolytes. Machine-learning force fields (ML-FFs) offer near-first-principles accuracy at molecular-dynamics scales, enabling simulations that are orders of magnitude larger and longer than <i>ab initio</i> approaches (<i>e.g.</i> density funcitonal theory). In this Review, we discuss recent ML-FF frameworks and the application of them on studying both crystalline and glassy solid electrolytes. Particually, we compare various ML-FF models and training strategies, examine transferability and uncertainty quantification, and outline best practices for data generation and validation. The applications of ML-FF on battery systems reveal advances in illustrating ionic-transport pathways, defect-mediated conduction, structure-property relationships, phase stability and transformations, and interfacial phenomena at grain boundaries and electrode|electrolyte contacts. Then we conclude with perspectives on key challenges-including long-range electrostatics, chemical reactivity, and multi-component complexity. Together, these developments position ML-FFs to accelerate the discovery and optimization of robust, high-performance solid electrolytes for practical all-solid-state batteries. Transferability ; Electrolyte ; Grain boundary ; Fast ion conductor ; Position (finance) ; Phase (matter) ; Stability (learning theory) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Bin Liu | Zhiguo Tian | А. А. Баринов | Moran Wang
Source: International Journal of Thermal Sciences (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Enhanced heat conduction in diamond/copper composites via interconnected structures: Machine learning molecular dynamics simulation Diamond/Cu composites have attracted considerable attention for thermal management applications due to their outstanding thermal conductivity. Recent studies have demonstrated that interconnected diamond network structures can significantly enhance the heat conduction of diamond/Cu composites, while the underlying microscopic heat transfer mechanisms remain to be fully elucidated. This study employed machine learning molecular dynamics simulations to validate and elucidate the heat conduction enhancement mechanisms of interconnected network structures. We developed a machine learning potential for diamond/Cu heterostructures based on the neuroevolution potential model and calculated the lattice thermal conductivity (LTC) of two series of network structures: diamond network/Cu (DN/Cu) and Cu network/diamond (CuN/D). The results demonstrate that DN/Cu exhibits monotonically increasing LTC with diamond content due to continuous heat transfer channels, while CuN/D shows non-monotonic behavior with a minimum LTC at intermediate diamond fractions, originating from the competitive mechanism between contributions from low-frequency Cu phonons (0–8 THz) and high-frequency diamond phonons (10–40 THz) to thermal transport. Through spectral LTC decomposition and wavelike phonon transmission analysis, we elucidated the suppressive effects of multiple heat transfer mechanisms on LTC, including phonon interfacial scattering, coherent interference effects, and total internal reflection in nano-network structures. This work establishes quantitative design thresholds for lattice thermal transport in DN/Cu, revealing a critical diamond volume fraction of ∼ 30% above which DN/Cu consistently outperforms CuN/D, and an unexpected LTC minimum at ∼ 50% diamond in CuN/D structures driven by phonon frequency competition. These findings explain why diamond network structures, despite demonstrating significant heat conduction enhancement compared to dispersed particles, still exhibit LTC values substantially lower than pure diamond, thus revealing substantial room for design optimization of network architectures. Thermal conduction ; Diamond ; Phonon ; Thermal conductivity ; Heat transfer ; Heat sink ; Molecular dynamics Thermal properties of materials ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Xianglong Hu | Rui Zhang | Ming Lei | Xinggui Zhou | De Chen | Yi‐An Zhu
Source: ACS Catalysis (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Universal Descriptors of Propane Dehydrogenation Activity at Atomically Dispersed Metal–X Sites (X = O, S, C, and N): Machine Learning-Powered Inverse Catalyst Design Atomically dispersed metal–X (M–X) sites (X = O, S, C, and N) have shown great promise as active centers for propane dehydrogenation (PDH). In this work, the formation energies of adsorbed H at the X site (EH@X) and coadsorbed H&H at the M–X site (EH&H@M–X) are identified as two universal descriptors of the PDH activity at M–X sites by establishing their scaling relations with the rate-determining state energies. The derived volcano-shaped activity map is capable of providing a rational interpretation of experimentally reported catalysts. To rapidly predict EH@X and EH&H@M–X without DFT calculations, motif-specific features, including intrinsic elemental properties and the number of atoms characterizing the coordination environments, are constructed to train two extra-trees regression models to perform a multitiered virtual screening of 1,659,373 potential catalysts. Pt1Co1–Ga2O3 is found to show a higher activity than previously known Ir1–Ga2O3, because the substrate imposes a specific geometrical arrangement of the surface Pt, Co, Ga, and O atoms, which strengthens the binding of the rate-determining transition state through Lewis acid–base interactions and therefore imparts specific function to the highly active Pt–O site for PDH. These findings provide a general strategy for the inverse catalyst design by combining DFT-based microkinetic analysis with machine-learning algorithms. Dehydrogenation ; Catalysis ; Propane ; Inverse ; Metal ; Materials science ; Chemical engineering ; Chemistry ; Metallurgy ; Organic chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Hang Xiao | Zhaohan Chu | Haodong Chen | Taichang Zhang | Jinghui Lu | Changyang Wang | Long Zhao | Bin Yang
Source: Combustion and Flame (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

An experimental and modeling study on norbornane pyrolysis aided by chemical information from neural network-assisted molecular dynamics Norbornane ; Pyrolysis ; Artificial neural network ; Biological system ; Biochemical engineering ; Dynamics (music) ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Biology ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Yusuke Kanematsu | Akiyoshi Ohta | Shunya Nagai | Yohei Adachi | Hiromasa Kaneko | Takayoshi Ishimoto | Takio Kurita | Joji Ohshita
Source: Molecules (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials We have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was demonstrated that our prediction model reasonably classified the quantum yields with an accuracy of 0.7. In particular, the low quantum yields were perfectly predicted for the synthesized molecules, demonstrating the usefulness of our prediction model to screen out weakly fluorescent molecules from the candidates. On the other hand, the low precision of 0.5 was attributed to the bias in the training dataset containing many fluorine-containing molecules with high quantum yields. Our prediction model was then revised with the generator of candidate molecular structures for more efficient development of fluorescent materials with taking the applicability domain into account, and the improvement of the applicability was confirmed owing to the increment of the dataset. Fluorescence ; Molecule ; Quantum ; Generator (circuit theory) ; Biological system ; Computer science ; Quantum chemical ; Materials science ; Chemistry ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Dan Liu | Yuan Li | Ryan Sun | Qi Xing | Baogen Shen
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research progress of rare earth magnetic materials based on machine learning Rare-earth elements possess unique atomic structures characterized by multiple unpaired 4f orbital electrons in inner shells, high atomic magnetic moments, and strong spin-orbit coupling. These attributes endow them with rich electronic energy levels, enabling them to form compounds with different valence states and coordination environments. Consequently, rare-earth materials typically exhibit excellent magnetic properties and complex magnetic domain structures, making them critical for the development of high-tech industries. The intricate magnetic configurations, different types of magnetic coupling, and direct/indirect magnetic exchange interactions in these materials not only facilitate the development of novel functional devices but also pose significant challenges to fundamental research. With the rapid advancement of data mining techniques, the emergence of big data and artificial intelligence provides researchers with a new method to efficiently analyze vast experimental and computational datasets, thereby accelerating the exploration and development of rare-earth magnetic materials. This work focuses on rare-earth permanent magnetic materials, rare-earth magnetocaloric materials, and rare-earth magnetostrictive materials, detailing the application progress of data mining techniques in property prediction, composition and process optimization, and microstructural analysis. This work also delves into the current challenges and future trends, aiming to provide a theoretical foundation for deepening the integration of data mining technologies with rare-earth magnetic material research. Rare earth ; Materials science ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Isaiah O. Betinol | Yutao Kuang | Junshan Lai | Christopher Yousofi | Jolene P. Reid
Source: ACS Catalysis (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation in Asymmetric Catalysis General reaction behavior is rarely reported in asymmetric catalysis, not simply because it is difficult to achieve, but also due to the methods used for its identification and study. Traditional approaches involve compartmentalization, where the impact of individual components is initially analyzed, followed by assimilation using simple response and structure matching techniques. However, extending this method to accommodate complex conditions and diverse reactions proves challenging. Here, we present a data-driven method that relies on clusterwise linear regression to derive and predictively apply general mechanistic models of enantioinduction, with minimal human intervention. When applied to the palladium-catalyzed decarboxylative asymmetric allylic alkylation (DAAA) reaction, unexpected interactions governing enantioselectivity are revealed, supported by high-level computations and additional experiments. Our results demonstrate this workflow as a powerful tool for automating mechanistic elucidation and effectively identifying general reaction performance. Catalysis ; Enantioselective synthesis ; Chemistry ; Mechanism (biology) ; Combinatorial chemistry ; Computer science ; Nanotechnology ; Materials science ; Organic chemistry ; Physics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Sadhana Barman | Utpal Sarkar
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Screening of potential candidates for solid electrolyte interphase materials for lithium-ion batteries through a data-driven approach Material property prediction through machine learning has emerged as a revolutionary approach for diminishing hardships in the design of optimal materials for practical applications. Herein, we used a machine learning approach to refine over 11 664 solid electrolyte interphase materials and identified potential candidates in terms of chemical stability at the molecular level, solvation energy and ease of synthesis, thereby obtaining insights for discoveries of new effective optimal interphase materials for lithium-ion batteries. The predicted accuracy of chemical reactivity parameters and solvation energy was in the range of 86.7-91.3% by uncovering atomistic input features. Dipole moments, number of heteroatoms, NHOH count, heavy atom count, number of hydrogen acceptors and donors, several surface area descriptors (PEOE_VSA1, PEOE_VSA4, SMR_VSA6, SMR_VSA10, EState_VSA10, VSA_EState1, VSA_EState2), kappa index (kappa1), and functional groups (fr_ketone, fr_alkyl_halide, fr_nitro), <i>etc.</i> have been identified as key factors influencing solvation energy and chemical reactivity, offering critical guidance for screening the materials. These insights enable the strategic selection of SEI materials with chemical stabilities that effectively impact dendrite formation, thereby having the potential to enhance the performance and longevity of electrochemical systems. For the ideal identified candidates that have solid electrolyte interphase-affecting characteristics, the predicted property values perfectly align with the actual values. The predicted solvation energy, chemical hardness, and electrophilicity index are in the ranges of 1.433-5.677 kcal mol<sup>-1</sup>, 10.796-17.530, and 0.270-0.390, respectively, along with a low synthetic accessible score of 1.219-2.260. Non-ideal materials with the predicted solvation energy, chemical hardness, electrophilicity index and synthetic accessibility score are in the ranges of 85.354-300.982 kcal mol<sup>-1</sup>, 1.820-4.005, 2.030-4.823, and 4.002-7.422, respectively, demonstrating the model's robustness for reliable prediction, along with poor solid electrolyte interphase-suppressing characteristics. The most intriguing feature of our work is the molecules containing the elements fluorine, nitrogen and carbon, which define stable SEI candidates, while sulphur, oxygen, nitrogen, and carbon-containing molecules reduce the stable SEI formation capability. This result highlights a robust workflow that can guide the future discovery of materials through property optimization, particularly for dendrite suppression. Solvation ; Electrolyte ; Interphase ; Chemical stability ; Electrochemistry ; Electrochemical potential ; Electronegativity Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Lawrence A. Limjuco | Jayson S. Garcia | Zhentao Wu | Paul D. Topham | Matthew J. Derry | Mona Semsarilar | Abed Alaswad
Source: Chemical Engineering Journal (article, 2025-11-16)
Field: Materials Science — Polymers and Plastics

Purpose-driven polymers: Mechanistic insights and empirical advances in inverse vulcanized polysulfides for mercury remediation Inverse vulcanized polysulfides (IV PS) are an emerging class of sulfur-rich polymers that have garnered increasing attention as promising adsorbents for mercury (Hg) remediation. Their chemical tunability, abundance of soft Lewis base sulfur moieties, and alignment with green chemistry principles position them as potentially sustainable and cost-effective solutions for addressing Hg, one of the most toxic and persistent pollutants. This promise is further reinforced by their green credentials: IV PS are synthesized from elemental sulfur, an abundant industrial by-product, often in combination with renewable monomers, enabling sustainable, high-capacity sorbents with low production cost. Yet, a critical question remains: have IV PS truly delivered on their potential as ideal Hg adsorbents? This review explores the purpose-driven development of IV PS-based Hg adsorbents, spanning foundational synthesis strategies to emerging computational approaches. Key motivations discussed include: (1) comonomer selection; (2) enhancement of adsorbent properties (e.g., surface area, wettability, reactivity, and topology); (3) advances in inverse vulcanization strategies (e.g., catalyst-free multicomponent polymerization/nucleophilic activation and catalysis); (4) performance evaluation; and (5) integration of computational tools. Addressing whether IV PS have reached their potential, this review critically examines performance metrics (i.e., adsorption isotherms, kinetics, selectivity, and cost-effectiveness) underscoring the need for standardized performance testing reporting and introduces the concept of percent binding site utilization to better leverage their high sulfur content. By identifying critical gaps in design development and performance evaluation of IV PS, this review provides a foundation for advancing IV PS from promising laboratory materials to practical, scalable solutions for Hg remediation. • Inverse vulcanized polysulfides emerge as green, low-cost Hg 2+ adsorbents. • Comonomer design tunes IV PS morphology, wettability, and adsorption sites. • Normalized Hg 2+ adsorption metrics are critical for fair performance evaluation. • Digital tools (e.g., Machine learning and CFD) accelerate IV PS design and process optimization. • Life cycle assessment validates the sustainability of IV PS for Hg remediation. Adsorption ; Vulcanization ; Mercury (programming language) ; Comonomer ; Leverage (statistics) ; Green chemistry ; Sulfur ; Environmental remediation ; Renewable energy Synthesis and properties of polymers ; Covalent Organic Framework Applications ; Industrial Gas Emission Control
Authors: Asim Muhammad | Muhammad Naveed | Tariq Aziz | Maida Salah Ud Din | Fatma Alshehri | Ashwag Shami | Maher S. Alwethaynani | Deema Fallatah | Abeer M. Alghamdi | Fakhria A. Al‐Joufi
Source: Antonie van Leeuwenhoek (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Artificial neural network optimized green synthesis of cysteine-conjugated silver nanoparticles for antibacterial activity against staphylococcus nepalensis to combat cystitis Silver nanoparticle ; Cytotoxicity ; Antibacterial activity ; In vivo ; Hemolysis ; Nanoparticle ; Staphylococcus aureus ; Antimicrobial Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Carbon and Quantum Dots Applications