jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benedikt E. Hörfarter | Stefan Seiwald | Clemens Hofstötter | Armin Penz | Josef M. Gallmetzer | Thomas S. Hofer
Source: Journal of Computational Chemistry (article, 2026-04-02)
Field: Chemistry — Inorganic Chemistry

Tackling Orientational Isomerism in Metal–Organic Frameworks Comprising Low‐Symmetry Linker Molecules via Simulated Annealing Featuring a Neural Network Potential The perplexing structural diversity of metal-organic frameworks (MOFs) leads to astonishing unique properties and versatile potential technological applications of this porous materials class. However, whenever the organic linker molecules in a specific MOF compound may adopt multiple spatial orientations in-between the inorganic nodes they coordinate to, a near infinite number of possible MOF configurations arises through orientational isomerism. The latter is particularly challenging to account for in computational studies. In this work, we present a novel simulated annealing (SA) approach via Monte Carlo (MC) runs to gain insights into linker molecule orientations within such compounds, at the example of the three increasingly complex MOF systems MOF-5-OH, SNU-70 and UiO-66(Zr)-NH<sub>2</sub>. We thereby successfully identify reasonable approximations to the global minimum structures for all MOFs with regard to collective linker molecule orientations by performing random re-orientation attempts of a randomly chosen organic linker in each MC step. The efficiency of the simulated annealing procedure is improved and guaranteed by exploiting the accuracy and speed of the state-of-the-art neural network potential (NNP) MACE-MP-0a in all energy and force calculations. Critically, a substantial potential energy gain is observed during annealing, accompanied by systematic structural changes within the MOF compounds. The combined results of these energetic and structural characteristics highlight how it is indeed cooperative effects arising from the organic linker molecules that play a decisive role in determining the most favorable MOF configuration. Intriguingly, root-mean-square-deviation (RMSD) calculations based exclusively on atoms of the inorganic nodes are found to be sensitive structural descriptors for tracking linker molecule re-orientations, while powder X-ray diffraction (PXRD) patterns and radial distribution functions (RDFs) derived from molecular dynamics (MD) simulations at standard conditions show less distinctive trends. In summary, the utility of the presented simulated annealing strategy is justifiable by its broad applicability for investigating linker-related orientational isomerism in any relevant MOF as well as the possibility for studying even more complex host-guest systems in a similar manner. Linker ; Simulated annealing ; Molecule ; Molecular dynamics ; Annealing (glass) ; Artificial neural network ; Monte Carlo method ; Potential energy Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Haiping Zhou | Jiawang Liu | Ronglin Zhong | Qingqing Pan | Zhongmin Su
Source: Chinese Chemical Letters (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted prediction of emission energies in radical luminescent materials Luminescence ; Analytical Chemistry (journal) ; Emission spectrum ; Energy (signal processing) Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Patrick W.V. Butler | Simon D. Rihm | Sebastian Mosbach | Jethro Akroyd | Markus Kraft
Source: Journal of Chemical Information and Modeling (article, 2026-04-01)
Field: Chemistry — Inorganic Chemistry

Inverse Design of Metal–Organic Polyhedra through Molecular Fragmentation and Evolutionary Optimisation Reticular materials have come to the fore of chemistry with exceptional potential in applications ranging from CO<sub>2</sub> capture and chemical separations to catalysis and drug delivery. However, due to the vast combinatorial space of molecular building blocks that can form these materials, designing high-performing reticular materials for applications remains a considerable challenge. Here, we present a computational approach that combines a library of molecular fragments suitable for constructing organic building units, template-based reassembly, and evolutionary optimization to accelerate the discovery of reticular materials. Applied to metal-organic polyhedra (MOPs), this approach produces a design space of nearly 800,000 MOP configurations. A genetic algorithm (GA) based on the molecular fragments is shown to be effective at rapidly identifying optimal MOPs within this space, demonstrated through optimizing cavity properties for host-guest applications and CO<sub>2</sub> interaction energies estimated by machine-learning-accelerated simulations. An important component of our approach is that it is fully ontologized and integrated within The World Avatar, forming part of a broader, interoperable knowledge model for the discovery of reticular materials. Polyhedron ; Chemical space ; Reticular connective tissue ; Inverse ; Interoperability ; Ranging ; Component (thermodynamics) ; Space (punctuation) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: Rajesh Egala | Kunal Dewangan | Srinivasu Gangi Setti
Source: Journal of Composite Materials (article, 2026-02-12)
Field: Materials Science — Polymers and Plastics

Investigating moisture resistance of castor oil cortex fiber-reinforced epoxy composites through chemical treatments: Experimental characterization and prediction by machine learning The susceptibility of natural fiber composites to moisture-induced degradation poses a significant challenge for their application in humid and marine environments. This study investigates the effectiveness of chemical treatments in mitigating this issue for castor oil cortex fiber-reinforced epoxy composites. Fibers were treated with NaOH (1-10%) and KMnO 4 (0.1-0.5%) solutions, and unidirectional composites with a 40% fiber volume fraction were fabricated. Both untreated and treated samples were subjected to moisture absorption studies through immersion in distilled water and seawater for up to 60 days, followed by tensile, flexural, and impact testing. The 5% NaOH treatment (NaOH-2) proved most effective, enhancing the dry tensile, flexural, and impact strengths by 28%, 34%, and 115%, respectively, and demonstrating superior property retention after aging. A machine learning framework was developed to predict mechanical properties based on processing and exposure parameters. Gradient Boosting excelled in predicting flexural strength (Test R 2 = 0.98), Random Forest was optimal for impact strength (Test R 2 = 0.99), and Linear Regression performed best for tensile strength (Test R 2 = 0.92). The analysis identified immersion time as the most critical factor influencing property degradation. This integrated experimental and computational approach provides a robust tool for the design and lifetime prediction of durable natural fiber composites. Epoxy ; Ultimate tensile strength ; Natural fiber ; Distilled water ; Moisture ; Absorption of water ; Fiber ; Flexural strength ; Composite number Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: Ilyes Batatia | William J. Baldwin | Domantas Kuryla | Joseph Hart | Elliott Kasoar | Alin M. Elena | Harry Moore | Mikołaj Jan Gawkowski | Benjamin X. Shi | Venkat Kapil | Panagiotis Kourtis | Ioan-Bogdan Magdău | Gábor Csányi
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery. Electrostatics ; Charge (physics) ; Formalism (music) ; Supramolecular chemistry ; Molecular dynamics ; Force field (fiction) ; Molecule ; Molecular model ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: B. W. Majewski | Marta Łabuda
Source: International Journal of Molecular Sciences (article, 2026-02-05)
Field: Chemistry — Spectroscopy

Hybrid Deep Learning Model for EI-MS Spectra Prediction Electron ionization (EI) mass spectrometry (MS) is a widely used technique for the compound identification and production of spectra. However, incomplete coverage of reference spectral libraries limits reliable analysis of newly characterized molecules. This study presents a hybrid deep learning model for predicting EI-MS spectra directly from molecular structure. The approach combines a graph neural network encoder with a residual neural network decoder, followed by refinement using cross-attention, bidirectional prediction, and probabilistic, chemistry-informed masks. Trained on the NIST14 EI-MS database (≤500 Da), the model achieves strong library matching performance (Recall@10 ≈ 80.8%) and high spectral similarity. The proposed hybrid GNN (Graph Neural Network)-ResNet (Residual Neural Network) model can generate high-quality synthetic EI-MS spectra to supplement existing libraries, potentially reducing the cost and effort of experimental spectrum acquisition. The obtained results demonstrate the potential of data-driven models to augment EI-MS libraries, while highlighting remaining challenges in generalization and spectral uniqueness. Artificial neural network ; Deep learning ; Residual ; Pattern recognition (psychology) ; Deep neural networks ; Generalization ; Encoder ; Identification (biology) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Raphael T. Husistein | Markus Reiher
Source: ArXiv.org (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

A New Paradigm for Computational Chemistry Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potential energy hypersurface, key to all chemistry and materials science, as it assigns an energy to a molecular structure, the necessary ingredient for reaction mechanism elucidation and reaction rate calculation. Density functional theory (DFT) has been the most important method in practice for obtaining such energies, which is mirrored in the use of high-performance computing hardware. In the last two decades, a new class of surrogate potential energy functions has been evolving with remarkable properties: quantum accuracy combined with force-field speed. Until very recently, their application was hampered by the fact that they needed to be trained on truly large system-specific data sets, generated before a computational chemistry study could be started (in sharp contrast to DFT, which, as a first-principles method, works out of the box, but at a far higher price of computational cost). Very recently, this roadblock has been overcome by so-called foundation machine learning interatomic potentials, which are poised to completely change the way we do computational chemistry, likely prompting us to abandon DFT as the prime method of choice for this purpose in less than a decade. Computational model ; Density functional theory ; Quantum chemistry ; Potential energy ; Computational resource ; Computational complexity theory ; Mechanism (biology) ; Class (philosophy) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Neal Kapadia | Jiaqi Ke | Laisuo Su
Source: Discover Energy (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

A transferable data driven framework for electrolyte discovery Widespread electrification in transportation and grid storage demands rapid development in batteries. In particular, electrolytes that deliver fast ion transport, wide electrochemical stability windows, durable electrode interfaces, safety under abuse, and scalable manufacturing. Machine learning (ML) combined with chemical experimentation can accelerate electrolyte development. Unsupervised clustering, supervised learning, and Bayesian optimization recover structure-property relations and navigate multi-objective trade-offs across diverse battery chemistries, even under small-data regimes. However, the dominant barrier is data quality and comparability: dispersed reports, missing or inconsistent metadata, and nonstandard test conditions undermine benchmarking and cross-system transfer, weakening the predictive value of ML models. In this perspective, a data-centric framework highlights (i) minimum information standards for electrolyte reporting, (ii) Findability, Accessibility, Interoperability, and Reuse (FAIR) community databases, (iii) Natural Language Processing (NLP) in literature-scale conversion of results, and (iv) shared benchmark tasks with reference cells for reproducible comparison. A unified feature space that respects system-specific constraints is proposed to enable transfer across chemistries. Implemented together, these elements establish a reproducible pathway toward autonomous, closed-loop electrolyte discovery. Data-driven ; Stability (learning theory) ; Scalability ; Battery (electricity) ; Electrolyte ; Benchmark (surveying) ; Benchmarking ; Grid ; Reuse Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Zepeng Zhang | Qikun Wu | Xinlong Wang | Yansong Liao | Yuzhou Lin | Ziqin Li | Shaoping Wu | Quancong Zhang | Zongyuan Xiao | Mingbin Gao | Wenqiu Su | Wenjing HONG
Source: Acta Physico-Chimica Sinica (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Design of Multifunctional Polyimides: Revolutionizing the Paradigm from Data to Synthesis Generative grammar ; Field (mathematics) ; Artificial neural network ; Deep learning ; Bayesian optimization ; Robot ; Core (optical fiber) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Hao Deng | Quanwen Sun | Meng Li | Zeyu Zhao | Wenjuan Bian | Bin Liu | Dong Ding
Source: Small Methods (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Atomistic Simulations of Thermal and Chemical Expansions of PrNi <sub>x</sub> Co <sub>1‐x</sub> O <sub>3‐δ</sub> Accelerated by Machine Learning Potentials (Small Methods 2/2026) Front Cover In article number 2500816, Liu, Ding, and co-workers present an atomistic simulation framework for unraveling thermal and chemical expansion behaviors in protonic ceramic oxide PrNixCo1−xO3−δ. Leveraging phonon calculations powered by a machine learning potential method, complex lattice expansions induced by heat, hydration, and composition are disentangled, guiding the design of more stable interfaces in protonic ceramic electrochemical cells. Ceramic ; Lattice (music) ; Thermal expansion ; Thermal ; Oxide ; Phonon Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials
Authors: Masugu Hamaguchi | T. Yokoyama | Hideki Miwake | Ryoichi Nakatake | Noriyoshi Arai
Source: Industrial & Engineering Chemistry Research (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

In Silico Prediction of Multicomponent Functional Material Formulations via Machine Learning Coupled with Molecular Simulation: A Case Study on Cleansing Foam Formulations Multi-ingredient cleansing foams pose a combinatorial design challenge because many component ratios must be experimentally screened. We integrate DPD-derived descriptors with machine learning to enable prescreening and prioritization of formulations, thereby reducing exploratory batches and accelerating design cycles while maintaining a traceable physical rationale. The modeling descriptors─the hydrophilic fraction (fHI) and the solubility parameter contrast (Δδ) defined relative to the polyethylene glycol thresholds─probe amphiphilicity, while DPD-derived potential energy and pressure summarize mesoscale self-assembly. Using 430 historical recipes, we benchmark nested cross-validation under three generalization regimes: Points Out (random formulations), Mixtures Out (novel combinations of known ingredients), and Compounds Out (novel raw ingredients; polyols, including humectants and amphiphilic derivatives, in this data set). To prevent leakage, all preprocessing (imputation and scaling) is fit strictly on training folds only, and identical outer-CV partitions are held across feature conditions to enable paired comparisons. Incorporating modeling and simulation descriptors improves mean R2 from 0.665 to 0.716 (Points Out) and from 0.420 to 0.573 (Mixtures Out), and raises Compounds Out R2 from 0.023 to 0.341. Paired difference tests with HC3-robust OLS and Holm correction confirm statistically significant gains─small to moderate for Points Out and moderate to large for Mixtures and Compounds Out. Among algorithms, tree-based ensembles outperform linear, kernel, and neural baselines, reflecting nonlinear composition–property relations. This workflow operationalizes AI-assisted formulation design by triaging candidate recipes prior to wet-lab screening, enabling faster decision-making and tangible experimental savings while retaining physical interpretability via DPD-derived descriptors. Compounds out results apply only to polyols in the present data set; generalization beyond polyols is out-of-scope and will require larger, more diverse data sets and transfer learning. Interpretability ; Generalization ; Workflow ; Benchmark (surveying) ; Preprocessor ; Feature (linguistics) ; Fraction (chemistry) ; Artificial neural network ; Molecular descriptor Polymer Foaming and Composites ; Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization
Authors: Di Jin | Shicong Ding | Hailong Qiu | Yuan Li | Xinyang Li | Guochun Yang
Source: npj Computational Materials (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Structural insights and predictive screening of ion transport in Li-rich alloys via neuroevolution potentials Fast and efficient lithium-ion transport is crucial for high-performance rechargeable batteries but remains poorly understood in complex alloy anodes due to the coexistence of multiple competing diffusion mechanisms. A key challenge is disentangling how defect type, local coordination, and structural connectivity collectively govern migration barriers, which has so far limited the development of generalizable design rules. Here, we develop neuroevolution potentials (NEPs), a machine-learning framework trained to near-density functional theory (DFT) accuracy, providing both reliability and transferability for large-scale simulations of Li diffusion in Li-In and Li-Sn alloys. Using NEPs, we identify three primary factors controlling Li mobility: the dominant defect carrier (Li vacancy, Li interstitial, or metal vacancy), the connectivity of low-barrier diffusion pathways, and the local atomic environment, with migration distance and charge redistribution as the most critical determinants of diffusion barriers. Li diffusion shows strong structural inheritance across intermetallics, where the crystal backbone dictates long-range connectivity and chemical composition fine-tunes local behavior. The favorable diffusion structural frameworks revealed by NEPs can be extended to other Li-rich intermetallics, enabling predictive screening of fast-diffusing phases. This work establishes a predictive framework for designing next-generation alloy anodes with high ionic conductivity. Transferability ; Diffusion ; Anode ; Reliability (semiconductor) ; Public records ; Work (physics) ; Redistribution (election) ; Ionic bonding Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Rani H. Nandkhile | Shilpa S. Nandkhile
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence in Spectroscopy Data Interpretation: A Comprehensive Review Abstract Spectroscopy is one of the most widely used analytical techniques in chemistry, physics, biology, and materials science for identifying molecular structures and determining chemical compositions. However, modern spectroscopic instruments generate complex, high-dimensional datasets that require sophisticated analytical methods for accurate interpretation. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has emerged as a transformative approach for analyzing spectroscopic data. AI-based methods can automate spectral interpretation, enhance pattern recognition, and enable real-time analysis of complex samples. This review presents a comprehensive overview of the integration of AI techniques in spectroscopy data interpretation. It discusses the fundamental principles of spectroscopic data analysis, machine learning algorithms used in spectral interpretation, and applications in major spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, nuclear magnetic resonance (NMR), and mass spectrometry. The review also highlights challenges including data scarcity, model interpretability, and standardization issues. Finally, emerging trends such as explainable AI, generative models, and autonomous laboratories are discussed as future directions for AI-driven spectroscopy. Standardization ; Transformative learning ; Deep learning ; Generative grammar ; Big data ; Applications of artificial intelligence ; Data acquisition Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Molecular spectroscopy and chirality