jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qi Kong | Juntao Ye
Source: Journal of Chemical Education (article, 2026-02-12)
Field: Chemistry — Physical and Theoretical Chemistry

Teaching Physical Organic Chemistry with the Latest Literature: An Illustrative Example on Radical Philicity and Reactivity Physical organic chemistry is a core subject for graduate students in organic chemistry, yet it is often considered highly challenging due to its abstract theories and mechanistic complexities. To improve comprehension of core concepts within a constrained curriculum, we have investigated various active learning strategies over the past five years. Among these, the integration of the latest research literature proved to be particularly effective. This paper details a case study using a selection of recent publications to teach radical philicity and reactivity. This two-hour module was designed with clear learning objectives for advanced undergraduate or beginning graduate students: to determine radical philicity, predict reaction rates and selectivities, and apply these concepts to current research problems. Analysis of student performance and feedback indicates that this literature-based approach not only successfully achieved these learning goals but also fostered a deeper interest in the subject and enhanced engagement with the primary research literature. Physical organic chemistry ; Comprehension ; Reactivity (psychology) ; Subject (documents) ; Graduate students ; Core (optical fiber) ; Chemistry education Various Chemistry Research Topics ; Innovative Teaching Methods ; Chemical Reactions and Mechanisms
Authors: Michael Caspary | Peter Poliak | Radek Crha | Edgar Galicia-Andrés | Chris Oostenbrink
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Data for: Buffer Region Embedding for Hybrid Machine-Learned / Molecular-Mechanical Simulations in Complex Environments Multiscale approaches combining machine-learned interatomic potentials with classical molecular mechanics force fields are emerging as a scalable alternative to QM/MM approaches, in which a quantum mechanical calculation is embedded in a molecular mechanics environment. They enable quantum-level accuracy for localized chemistry at substantially reduced cost. However, reliable coupling across region boundaries and application in heterogeneous environments remains a central challenge. Here, we extend the buffer region embedding strategy (BuRNN) for hybrid machine-learned interaction potentials / molecular mechanics (MLIP/MM) simulations in complex environments. The buffer region elevates the interactions of the inner region with its immediate surroundings to the MLIP level, whereas the interactions within the buffer region and with the outer region are still described at the MM level. We validate the approach across four test systems of increasing complexity: methanol/water mixtures benchmarked against experimental total X-ray structure factors, a functionalized fullerene designed to describe a covalent boundary between the buffer and outer region, aqueous heme b with an axial cysteine ligand to probe coordinative bond dissociation, and the resting-state of the cytochrome P450 1A2 enzyme. Across these systems, BuRNN reproduces experimental observables or the underlying QM reference data and yields stable dynamics, including challenging metal-ligand interactions. We compare explicit and implicit link-atom treatments at the buffer-outer boundary and find that explicit capping provides more robust uncertainty estimates, which is critical for active-learning model generation. These results establish BuRNN as a practical approach to perform MLIP/MM simulations in biomolecular systems. Observable ; Embedding ; Molecular dynamics ; Multiscale modeling ; Boundary (topology) ; Scalability ; QM/MM ; Boundary value problem ; Fragment molecular orbital Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum many-body systems
Authors: Chenghao Zhang | Amke Nimmrich | Axel Gomez | Munira Khalil | Niranjan Govind
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Accelerating Instanton Theory with the Line Integral String Method, Gaussian Process Regression, and Selective Hessian Modeling We develop a Gaussian process regression enhanced line integral string method to accelerate ring polymer instanton calculations of tunneling rates and tunneling splittings in molecular proton transfer reactions. By exploiting uncertainty estimates from the surrogate representation, we show that the number of force evaluations required to converge an instanton path becomes effectively independent of the number of beads used to discretize the pathway. To reduce the computational overhead associated with training, particularly when Hessian information is included, we implement graphics processing unit accelerated black box matrix matrix multiplication, achieving an order of magnitude speedups relative to standard implementations. For rate calculations, we introduce a selective Hessian training strategy that distinguishes flexible modes strongly coupled to the transferring proton from more rigid modes weakly coupled to the reaction coordinate. This enables the construction of accurate surrogate potential energy surfaces with substantially fewer Hessian evaluations. Applications to malonaldehyde and Z-3-aminopropenal demonstrate that tunneling rates can be predicted within 20% of exact values while reducing force and Hessian evaluations. The approach is further extended to tunneling splitting calculations for the formic acid dimer and malonaldehyde, yielding splittings in reasonable agreement with experiment and high level theoretical results. Hessian matrix ; Instanton ; String (physics) ; Line (geometry) ; Quantum tunnelling ; Matrix (chemical analysis) ; Gaussian process Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chemical Reactions and Mechanisms
Authors: Chenghao Zhang | Amke Nimmrich | Axel Gomez | Munira Khalil | Niranjan Govind
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Accelerating Instanton Theory with the Line Integral String Method, Gaussian Process Regression, and Selective Hessian Modeling We develop a Gaussian process regression enhanced line integral string method to accelerate ring polymer instanton calculations of tunneling rates and tunneling splittings in molecular proton transfer reactions. By exploiting uncertainty estimates from the surrogate representation, we show that the number of force evaluations required to converge an instanton path becomes effectively independent of the number of beads used to discretize the pathway. To reduce the computational overhead associated with training, particularly when Hessian information is included, we implement graphics processing unit accelerated black box matrix matrix multiplication, achieving an order of magnitude speedups relative to standard implementations. For rate calculations, we introduce a selective Hessian training strategy that distinguishes flexible modes strongly coupled to the transferring proton from more rigid modes weakly coupled to the reaction coordinate. This enables the construction of accurate surrogate potential energy surfaces with substantially fewer Hessian evaluations. Applications to malonaldehyde and Z-3-aminopropenal demonstrate that tunneling rates can be predicted within 20% of exact values while reducing force and Hessian evaluations. The approach is further extended to tunneling splitting calculations for the formic acid dimer and malonaldehyde, yielding splittings in reasonable agreement with experiment and high level theoretical results. Hessian matrix ; Instanton ; String (physics) ; Line (geometry) ; Quantum tunnelling ; Matrix (chemical analysis) ; Gaussian process Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chemical Reactions and Mechanisms
Authors: Zicong Marvin Wong | Teck Leong Tan | Shi Jun Ang
Source: Advanced Theory and Simulations (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

How Do Initial Spins Affect Density Functional Theory Convergence for Magnetic High Entropy Alloys? ABSTRACT Machine‐learned interatomic potentials (MLIPs) have proven to be effective in accelerating catalytic simulations, achieving density functional theory (DFT) accuracy at a small fraction of the computational cost. To streamline the acquisition of representative DFT data, autonomous active learning workflows have been integrated with unmanned DFT calculations. As a result, DFT settings are typically standardized across various geometries selected for computation. However, magnetic systems often require more human intervention to determine the correct final magnetizations—the ones that correspond to the lowest electronic energy—leading to MLIPs being predominantly developed for non‐magnetic systems. Despite this, magnetic high‐entropy alloys (HEAs) are recognized for their promising catalytic properties, including CO 2 reduction, NH 3 decomposition and transport, as well as their desirable physical attributes such as high‐temperature strength and corrosion resistance. Given their importance, it is crucial to optimize the modeling of these materials. This study proposes a methodology for selecting appropriate initial atomic spin states for DFT optimization and single‐point calculations of representative HEA bulk, slab, and nanoparticle structures. The goal is to resolve a critical bottleneck in the development of magnetic HEA MLIPs with the automated and reliable generation of consistent DFT training data. By establishing a robust protocol for magnetic systems, this work enables the creation of high‐quality potential energy surfaces necessary for training next generation interatomic potentials. Density functional theory ; Bottleneck ; Workflow ; Spins ; Convergence (economics) ; Spin (aerodynamics) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Masahi OKADA | Kazuki Sakai | Hiroaki Yoshida | Masaki Okoshi | Tadahiro Taniguchi
Source: arXiv (Cornell University) (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic We study sample-efficient molecular optimization under a limited budget of oracle evaluations. We propose MolLIBRA (MultimOdaLity and Language Integrated Bayesian and evolutionaRy optimizAtion), a genetic algorithm based framework that pre-ranks candidate molecules using multiple critics before oracle calls: (i) an ensemble of Gaussian process (GP) surrogates defined over multiple molecular fingerprints and (ii) a pretrained text-molecule aligned encoder CLAMP. The GP ensemble enables adaptive selection of task-appropriate fingerprints, while CLAMP provides a zero-shot scoring signal from task descriptions by measuring the similarity between molecular and text embeddings. On the Practical Molecular Optimization (PMO) benchmark with a budget of 1,000 evaluations (PMO-1K), MolLIBRA-L, our variant with a language-model-based candidate generator, attains the best Top-10 AUC on 14/22 tasks and the highest overall sum of Top-10 AUC across tasks among prior methods. Bayesian optimization ; Oracle ; Benchmark (surveying) ; Task (project management) ; Selection (genetic algorithm) ; Bayesian probability ; Gaussian process ; Metric (unit) ; Encoder Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Masahi OKADA | Kazuki Sakai | Hiroaki Yoshida | Masaki Okoshi | Tadahiro Taniguchi
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic We study sample-efficient molecular optimization under a limited budget of oracle evaluations. We propose MolLIBRA (MultimOdaLity and Language Integrated Bayesian and evolutionaRy optimizAtion), a genetic algorithm based framework that pre-ranks candidate molecules using multiple critics before oracle calls: (i) an ensemble of Gaussian process (GP) surrogates defined over multiple molecular fingerprints and (ii) a pretrained text-molecule aligned encoder CLAMP. The GP ensemble enables adaptive selection of task-appropriate fingerprints, while CLAMP provides a zero-shot scoring signal from task descriptions by measuring the similarity between molecular and text embeddings. On the Practical Molecular Optimization (PMO) benchmark with a budget of 1,000 evaluations (PMO-1K), MolLIBRA-L, our variant with a language-model-based candidate generator, attains the best Top-10 AUC on 14/22 tasks and the highest overall sum of Top-10 AUC across tasks among prior methods. Bayesian optimization ; Oracle ; Benchmark (surveying) ; Task (project management) ; Selection (genetic algorithm) ; Bayesian probability ; Gaussian process ; Metric (unit) ; Encoder Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Qingliang Jiao | Yingying Chen | Xiaorong Hong | Yang Xu | Changyin Ji | Jiafang Li
Source: Optics Express (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nano-opto-electro-mechanical nano-kirigami metasurface for on-chip dynamic spectral shaping Dynamic spectral shaping at the nanoscale has been limited by large free-space setups and slow response times. Here, we propose a fully pixelated nano-opto-electro-mechanical nano-kirigami metasurface that enables arbitrary on-chip spectral synthesis within a 9 × 9 μm<sup>2</sup> area. By exploiting 2D-to-3D morphological transformations of nano-kirigami metasurfaces that tailor the resonance wavelength and the ratio of radiation loss to intrinsic loss of a Fabry-Pérot (FP)-like cavity, the device achieves broadband tunability (>200 nm), as well as versatile single- and multi-mode spectral shaping capabilities at the pixel level. Combined with a Bayesian optimization inverse-design framework, we demonstrate accurate reconstruction of arbitrary reflectance spectra, with a root-mean-square error below 0.1. This work establishes a promising pathway toward compact, programmable spectral processors for applications in spectroscopy, imaging, and communication. Broadband ; Spectral imaging ; Pixel ; Wavelength ; Spectral shape analysis ; Radiation ; Rigorous coupled-wave analysis ; Resonance (particle physics) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Materials and Mechanics
Authors: Yancheng Li | Huifeng Zhang | Li Li | Lusheng Yang | Zhenghe Liu | Haojie Lian
Source: Processes (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

A Unified Framework for Uncertainty Quantification and Sensitivity Analysis of Shaped Charge Jet Penetration in Oil Shale Shaped charge is widely used in petroleum drilling, yet the inherent parametric uncertainty of oil shale introduces significant uncertainties that affect perforation outcomes. The complex coupling of oil shale constitutive parameters under extreme strains poses challenges for uncertainty quantification. A coupled algorithm integrating an improved material point method (MPM) and polynomial chaos expansion (PCE) is presented, and polynomial chaos expansion (PCE) is used to systematically analyze the uncertainty and sensitivity of shaped charge jet penetration depth. Mechanical parameters from oil shale samples at Checun Coal Mine well No. 1 were tested to define key parameter ranges and establish a reliable uncertainty space. A benchmark simulation of a single isolated shaped charge jet validated the algorithm, and Sobol’ global sensitivity analysis identified internal friction angle, density, and Poisson’s ratio as strongly sensitive parameters, while tensile strength, Young’s modulus, and cohesion showed weak sensitivity, supporting surrogate model dimensionality reduction. Composite detonation models of three and five charges further revealed the effects of multi-projectile blast wave coupling on jet dynamics, providing new theoretical insights into cluster effects under high-energy, high-pressure, and extreme-strain conditions. Sensitivity and uncertainty analyses based on surrogate models emphasized the critical influence of internal friction angle alongside Poisson’s ratio and density. A reliable numerical framework is established for multi-physics coupled simulations of geomechanical responses under complex multi-source explosive loading. Uncertainty quantification ; Sensitivity (control systems) ; Parametric statistics ; Explosive material ; Uncertainty analysis ; Polynomial chaos ; Oil shale ; Jet (fluid) High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Structural Response to Dynamic Loads
Authors: Zefeng Cai | Malcolm J. A. Jardine | Maituo Yu | Chenbo Min | Jiatian Wu | Hantian Liu | Derek Dardzinski | Christopher Palmstrøm | Noa Marom
Source: Open MIND (preprint, 2026-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Combining Quasiparticle Self-Consistent $GW$ and Machine-Learned DFT+$U$ in Search of Half-Metallic Heuslers Half-metallic Heusler compounds are of significant interest for spintronics. For device fabrication, compounds that can be epitaxially grown on III-V semiconductors are particularly attractive. We present a first-principles investigation of four Co-based and two Ni-based Heusler compounds that are lattice-matched to InAs. The results of density functional theory (DFT) using semi-local and hybrid functionals are compared to quasiparticle self-consistent $GW$ (QPGW). We also consider DFT with machine-learned Hubbard $U$ corrections [npj Computational Materials 6, 180 (2020)] with a new Bayesian optimization (BO) objective function to determine the $U$ values that yield the closest agreement with the QPGW band structure and magnetic moments. We find that DFT+U(BO) can adequately reproduce the key QPGW features in most cases. Our results reveal a strong method dependence of the degree of spin polarization at the Fermi level and, in some cases, even the dominant spin channel (majority or minority). Of the materials studied here, Co$_2$TiSn and Co$_2$ZrAl are the most likely to be half-metals, and Co$_2$MnIn is likely to be a near-half-metal. Quasiparticle ; Density functional theory ; Ferromagnetism ; Spin (aerodynamics) ; Fermi level ; Spin polarization ; Yield (engineering) ; Spins ; Electronic band structure Heusler alloys: electronic and magnetic properties ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Mahdi Mansoury | Mohammad Ali Badamchizadeh | Peyman Roozafzoon Bashsiz | Sina Pakkhesal | Elaheh Rahimpour | Abolghasem Jouyban
Source: Fluid Phase Equilibria (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Evaluating machine learning models for accurate solubility prediction of azole drugs in binary solvents at various temperatures Solubility ; Boosting (machine learning) ; Binary number ; Support vector machine ; Stability (learning theory) ; Solvent ; Process (computing) ; COSMO-RS Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Ömer Tayfuroğlu | Seda Keskin
Source: Journal of Chemical Information and Modeling (article, 2026-02-03)
Field: Chemistry — Inorganic Chemistry

Transforming MOF Modeling with Machine-Learned Potentials: Progress and Perspectives Machine-learned potentials (MLPs) have emerged as transformative tools for modeling metal-organic frameworks (MOFs), bridging the accuracy of quantum mechanics with the efficiency required for large-scale molecular simulations. By learning the potential energy surface directly from quantum-mechanical reference data, MLPs enable a unified description of the complex nature of MOFs and their interactions with guest molecules across multiple length and time scales. Recent developments have demonstrated the capability of MLPs to model intrinsic MOF properties such as lattice dynamics, thermal expansion, and mechanical response, as well as to describe adsorption thermodynamics, diffusion, and cooperative host-guest behavior in flexible frameworks. Developing reliable and transferable MLPs for MOFs remains a significant challenge due to the vast chemical and structural diversity of MOFs and the complexity of sampling guest-framework configurations. The lack of openly shared, standardized, and user-friendly MLP implementations also limits their broader adoption. This review focuses on the current progress in MLP-based modeling of MOFs, highlighting methodological advances, data-generation strategies, and active-learning protocols, while outlining the key challenges and future directions for developing transferable, accessible, and universal MLPs for the predictive design and discovery of MOFs. Bridging (networking) ; Transformative learning ; Implementation ; Key (lock) ; Computational model Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Magnetism in coordination complexes
Authors: Matheus F. Flores | Kerlyn K. M. Hiraga | Antonio A. S. Curvelo
Source: Química Nova (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

INTEGRATING COMPUTATIONAL THINKING AND WRITING-TO-LEARN AS A METHOD FOR ORGANIC COMPOUND ANALYSIS AND STRUCTURAL ELUCIDATION This report describes how 25 second-year undergraduate chemistry students perceived and were impacted by an active learning strategy introduced in the Organic Compounds Analysis course at the São Carlos Institute of Chemistry, University of São Paulo. The intervention centered on a class activity where students developed structured protocols for interpreting organic spectroscopic data. The activity integrated principles from Computational Thinking and Writing-to-Learn, encouraging students to decompose complex problems, recognize data patterns, and articulate reasoning in written form. To evaluate the impact of this intervention, a descriptive educational study was conducted using post-activity questionnaires to collect quantitative data on students’ motivation, confidence, and perceptions of the protocols’ usefulness. Results showed that 15 out of 25 students felt more motivated to study in advance, and 13 reported increased confidence during exams when using the protocols. Additionally, 23 students indicated that preparing the protocols deepened their understanding of organic spectroscopy concepts and improved exam performance. Although some students reported difficulties synthesizing information from multiple spectroscopic techniques, the overall reception was positive. The findings suggest this activity not only enhanced learning efficacy in organic spectroscopy but also helped students develop greater confidence during assessments. Class (philosophy) ; Perception ; Intervention (counseling) ; Computational thinking ; Data collection ; Qualitative analysis ; Critical thinking Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods