jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongbo Shi (2005078) | Yu Bao (1588321) | Shuo Cao (665595) | Ye Su (179577) | Ping Qian (1643260)
Source: Figshare (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Design of Transition Metal Dichalcogenides Using General-PurposeMachine-Learned Interatomic Potentials Exploring the general-purpose interatomic potential for describing multiple compounds is challenging. In this study, we confirm the applicability of machine learning methods. By fitting the density-functional theory (DFT) data set using a feedforward neural network, the general-purpose machine-learned interatomic potentials (GMIPs) for transition metal dichalcogenides (TMDCs) are obtained, which can effectively describe the interatomic interactions within MX<sub>2</sub> (M = Mo and W; X = S, Se, and Te) compounds and MoX<sub>2</sub>/WX<sub>2</sub> heterojunctions. GMIPs demonstrate a high degree of accuracy across a wide range of temperatures and strains. Subsequently, we confirm the effectiveness of the potential through GMIP-based molecular dynamics (MD) simulations. The mechanical behavior of these compounds and heterojunctions is analyzed in large supercells containing 30,000 and 48,000 atoms. Some key parameters, including Poisson’s ratio, elastic modulus, critical strain, and tensile strength, are revealed. The lattice thermal conductivity is predicted by both nonequilibrium molecular dynamics (NEMD) and homogeneous nonequilibrium molecular dynamics (HNEMD). Among all compounds, <i>WS</i><sub>2</sub> exhibits the highest thermal conductivity (168–207 W/mK), while MoTe<sub>2</sub> shows the lowest thermal conductivity (21–35 W/mK). Moreover, the presence of V<sub>S</sub>, V<sub>Mo</sub>, and V<sub>W</sub> vacancies in <i>MoS</i><sub>2</sub> and WS<sub>2</sub> results in the degradation of mechanical properties and a decrease in thermal conductivity. Interatomic potential ; Molecular dynamics ; Thermal conductivity ; Non-equilibrium thermodynamics ; Transition metal ; Thermal ; Lattice (music) 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Valerii Andreichev | Sena Aydin | Kai Töpfer | Markus Meuwly | Luis Itza Vazquez-Salazar
Source: ArXiv.org (preprint, 2025-11-02)
Field: Materials Science — Materials Chemistry

Design, Assessment, and Application of Machine Learning Potential Energy Surfaces Potential Energy Surfaces (PESs) are an indispensable tool to investigate, characterise and understand chemical and biological systems in the gas and condensed phases. Advances in Machine Learning (ML) methodologies have led to the development of Machine Learned Potential Energy Surfaces (ML-PES) which are now widely used to simulate such systems. The present work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on practical and recurrent issues to conceive and use such model. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs. Energy (signal processing) ; Work (physics) ; Table (database) ; Base (topology) ; Energy transfer ; Energy system Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Marta Stefańska (21802237) | Thomas Müntener (2873144) | Sebastian Hiller (2237143)
Source: Figshare (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Predictionsof Steady-State Photo-CIDNP Enhancement by Machine Learning Photochemically induced dynamic nuclear polarization (photo-CIDNP) is a hyperpolarization method used to boost signal sensitivity in NMR spectroscopy. So far, there is no theory to predict the steady-state photo-CIDNP enhancement reliably, and hence, suitable target molecules need to be identified through tedious experimental screenings. Here, we explore the use of machine learning to predict steady-state photo-CIDNP enhancement. For a series of 27 indole-, five amino-acid-, and eight phenol-derivatives, the signal-to-noise enhancement (SNE) of steady-state photo-CIDNP experiments was measured and then connected to a combination of eight molecular features. The nucleophilic Fukui index was identified as a strong qualitative indicator of the site with the highest SNE in each molecule. Furthermore, a semiquantitative machine learning model based on Logistic Regression identified the sites with high enhancements (SNE > 90) in 100% of cases. Among several quantitative machine learning models for enhancement prediction, CatBoost Regressor and K-Nearest Neighbors showed the best performance. The results demonstrate the high potential of machine learning approaches for predictions of photo-CIDNP SNE, which will enable virtual prescreening of compound libraries. Logistic regression ; Hyperpolarization (physics) ; Regression ; Support vector machine ; Experimental data ; Regression analysis ; Sensitivity (control systems) ; Series (stratigraphy) Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Nonlinear Optical Materials Research
Authors: Frank Hu | J. A. Tubb | Dimitris S. Argyropoulos | S. S. Golotvin | Mikhail Elyashberg | Grant M. Rotskoff | Matthew W. Kanan | Thomas E. Markland
Source: arXiv (Cornell University) (preprint, 2025-12-20)
Field: Materials Science — Materials Chemistry

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydrogen atoms, the number of possible structures has been estimated to range from $10^{20} - 10^{60}$. The task of determining the structure (formula and connectivity) of a molecule of this size using only its one-dimensional $^1$H and/or $^{13}$C NMR spectrum, i.e. de novo structure generation, thus appears completely intractable. Here we show how it is possible to achieve this task for systems with up to 40 non-hydrogen atoms across the full elemental coverage typically encountered in organic chemistry (C, N, O, H, P, S, Si, B, and the halogens) using a deep learning framework, thus covering a vast portion of the drug-like chemical space. Leveraging insights from natural language processing, we show that our transformer-based architecture predicts the correct molecule with 55.2% accuracy within the first 15 predictions using only the $^1$H and $^{13}$C NMR spectra, thus overcoming the combinatorial growth of the chemical space while also being extensible to experimental data via fine-tuning. Nuclear magnetic resonance spectroscopy ; Characterization (materials science) ; Molecule ; Chemical space ; Spectroscopy ; Task (project management) ; Organic molecules Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Ruoxuan Guo | Liping Song | Wencheng Yao | Yingzhong Huo | Lu Wang | Yang Liu | Yuejie Ai | Xiangke Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Guided Electron Flow in Anthraquinone-Methoxy D-A1-A2 Covalent Triazine Frameworks Enabling Superior Selective Uranium Capture Ternary operation ; Triazine ; Photocatalysis ; Anthraquinone ; Density functional theory ; Catalysis ; Covalent bond ; Electron ; Water extraction ; Seawater Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Advanced Photocatalysis Techniques
Authors: Enze Hou | Xiaoyang Wang | Han Wang
Source: Chinese Physics B (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential Abstract Lead (Pb) is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses. Under shock-wave loading, its dynamic mechanical behavior comprises two key phenomena: plastic deformation and shock-induced phase transitions. The underlying mechanisms of these processes are still poorly understood. Revealing these mechanisms remains challenging for experimental approaches. Non-equilibrium molecular dynamics (NEMD) simulations are an alternative theoretical tool for studying dynamic responses, as they capture atomic-scale mechanisms such as defect evolution and deformation pathways. However, due to the limited accuracy of empirical interatomic potentials, the reliability of previous NEMD studies has been questioned. Using our newly developed machine learning potential for Pb–Sn alloys, we revisited the microstructural evolution in response to shock loading under various shock orientations. The results reveal that shock loading along the [001] orientation of Pb exhibits a fast, reversible, and massive phase transition and stacking-fault evolution. The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation. Loading along the [011] orientation leads to slow, irreversible plastic deformation, and a localized FCC–BCC phase transition in the Pitsch orientation relationship. This study provides crucial theoretical insights into the dynamic mechanical response of Pb, offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions. Crystal twinning ; Shock (circulatory) ; Deformation (meteorology) ; Reliability (semiconductor) ; Phase (matter) ; Dynamic loading ; Orientation (vector space) High-Velocity Impact and Material Behavior ; Ultrasonics and Acoustic Wave Propagation ; Acoustic Wave Resonator Technologies
Authors: Md Zaibul Anam | Ogheneyoma Aghoghovbia | Mohammed Al-Fahdi | Lingyu Kong | Victor Fung | Ming Hu
Source: Scholar Commons (University of South Carolina) (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

A Comprehensive Assessment and Benchmark Studyof Large Atomistic Foundation Models for Phonons The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs—EquiformerV2, MatterSim, MACE, and CHGNet—on 2429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third-order IFCs, while its fine-tuned counterpart consistently outperforms other models in predicting second-order IFCs, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high-throughput screening of materials with targeted thermal transport properties. Phonon ; Benchmark (surveying) ; Benchmarking ; Force constant ; Thermal conductivity ; Foundation (evidence) ; Lattice (music) ; Thermal Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Frank Hu | J. A. Tubb | Dimitris S. Argyropoulos | S. S. Golotvin | Mikhail Elyashberg | Grant M. Rotskoff | Matthew W. Kanan | Thomas E. Markland
Source: ArXiv.org (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydrogen atoms, the number of possible structures has been estimated to range from $10^{20} - 10^{60}$. The task of determining the structure (formula and connectivity) of a molecule of this size using only its one-dimensional $^1$H and/or $^{13}$C NMR spectrum, i.e. de novo structure generation, thus appears completely intractable. Here we show how it is possible to achieve this task for systems with up to 40 non-hydrogen atoms across the full elemental coverage typically encountered in organic chemistry (C, N, O, H, P, S, Si, B, and the halogens) using a deep learning framework, thus covering a vast portion of the drug-like chemical space. Leveraging insights from natural language processing, we show that our transformer-based architecture predicts the correct molecule with 55.2% accuracy within the first 15 predictions using only the $^1$H and $^{13}$C NMR spectra, thus overcoming the combinatorial growth of the chemical space while also being extensible to experimental data via fine-tuning. Nuclear magnetic resonance spectroscopy ; Characterization (materials science) ; Molecule ; Chemical space ; Spectroscopy ; Task (project management) ; Organic molecules Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Zhiyun Yang | Zhihong Luo | Xinjian Ouyang | Ping Wei | Laijun Liu | Dawei Wang | Nan Zhang | Nanning Zheng
Source: Research Square (preprint, 2025-07-30)
Field: Materials Science — Materials Chemistry

Fitting Pair Distribution Function with Backpropagation Backpropagation ; Function (biology) ; Distribution (mathematics) ; Computer science ; Artificial intelligence ; Mathematics ; Econometrics ; Artificial neural network ; Mathematical analysis X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Liang Zhao | Fei Xu | David Porter | Yachun Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Quantification of Line Dislocations in Fftf Irradiated Ht9 Cladding by Deep Learning Method Cladding (metalworking) ; Materials science ; Irradiation ; Line (geometry) ; Metallurgy ; Nuclear engineering ; Physics ; Engineering ; Nuclear physics ; Mathematics ; Geometry Fusion materials and technologies ; Welding Techniques and Residual Stresses ; Additive Manufacturing Materials and Processes
Authors: Matija Medvidović | Jaylyn Umana | Iman Ahmadabadi | Domenico Di Sante | Johannes Flick | Ángel Rubio
Source: Physical Review Research (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Neural network distillation of orbital dependent density functional theory Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been approximated with increasing levels of complexity ranging from strictly local approximations to nonlocal and orbital dependent expressions with many tuned parameters. In this paper, we formulate a general way of rewriting complex density functionals using deep neural networks in a way that allows for simplified computation of Kohn-Sham potentials as well as higher functional derivatives through automatic differentiation, enabling access to highly nonlinear response functions and forces. These goals are achieved by using a recently developed class of robust neural network models capable of modeling functionals, as opposed to functions, with explicitly enforced spatial symmetries. Functionals treated in this way are then called and can be seamlessly integrated with existing DFT workflows. Tests are performed for a dataset featuring a large variety of molecular structures and popular meta–generalized gradient approximation density functionals, where we successfully eliminate orbital dependencies coming from the kinetic energy density, and discover a high degree of transferability to a variety of physical systems. The presented framework is general and could be extended to more complex orbital and energy dependent functionals as well as refined with specialized datasets. Distillation ; Artificial neural network ; Density functional theory ; Computer science ; Biological system ; Chemistry ; Artificial intelligence ; Biology ; Computational chemistry ; Chromatography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Michael A. Celia | Simone Monaco | Daniele Apiletti
Source: Neural Computing and Applications (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

A comparative study of neural ordinary differential equations and neural operators for modeling temporal dynamics Abstract Capturing the dynamics of relational systems is a key challenge in the natural sciences, with applications ranging from simulating molecular interactions to analyzing particle mechanics. Machine learning approaches have made significant progress in this area by using graph neural networks to learn and visualize spatial interactions effectively. Neural ordinary differential equations (Neural ODEs) and neural operators (NO) represent two distinct paradigms. However, a clear comparative understanding of when to prefer one over the other is still lacking. To address this gap, we present the first systematic comparison between two representative architectures: EGNO (Equivariant Graph Neural Operator) and SEGNO (Second-order Equivariant Graph Neural Ordinary Differential Equation). Through a series of experiments, we investigate their strengths and limitations in various simulation scenarios in the multi-step trajectory prediction tasks. Specifically, we employ rollout strategies and different input/output configurations, including multiple and irregularly sampled time steps. Our findings highlight a key trade-off between precision and stability that is central to model selection. SEGNO demonstrates superior robustness and stability over long prediction horizons, making it well-suited for tasks requiring reliable long-term forecasting. Conversely, EGNO offers higher precision during early stages of the trajectory and better leverages diverse training configurations, thanks to its discretization-invariant design. In summary, Neural Operators (EGNO) are preferable when short-term accuracy and data efficiency are critical, while Neural ODEs (SEGNO) are advantageous for scenarios demanding stable long-term predictions. This work not only clarifies the practical advantages of each approach but also lays the groundwork for informed model selection and future hybrid strategies in dynamical system modeling. Ordinary differential equation ; Artificial neural network ; Ode ; Robustness (evolution) ; Graph ; Stability (learning theory) ; Trajectory ; Dynamical systems theory Machine Learning in Materials Science ; Topic Modeling ; Model Reduction and Neural Networks
Authors: Bowen Wang | Haoyang Lv | Xun Wang | Minghui Hao | Donald W. Kirk | Daniel Guay | Steven J. Thorpe | Zejing Ruan
Source: Chemical Engineering Journal (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Quantifying bubble-induced diffusion resistance through real-time SAM-assisted YOLO high density bubble detection algorithm Bubble ; Diffusion ; Algorithm ; Materials science ; Computer science ; Mechanics ; Biological system ; Physics ; Biology ; Thermodynamics Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing
Authors: Shijie Pan (6584711) | Wenhao Wang (515852) | Jun Zhu (84054)
Source: Figshare (article, 2025-08-06)
Field: Chemistry — Organic Chemistry

Antiaromaticity-PromotedRadical Stability in BorylHeterocyclics and Its Application to Dinitrogen Activation: A CombinedDFT and Machine Learning Study Aromaticity is one of the fundamental concepts in chemistry and generally brings additional thermodynamic stability to a compound. On the other hand, boron radicals have attracted increasing interest from both theoretical and experimental chemists due to their various applications. Here, we carry out density functional theory (DFT) calculations to explore the relationship between the (anti)aromaticity and stability of boron-centered radicals. It is found that stronger antiaromaticity of the parent heterocyclics leads to higher thermodynamic stability of the corresponding radicals, which is in sharp contrast to the general knowledge that antiaromaticity brings compounds’ thermodynamic instabilities. In addition, the boryl radicals derived from aromatic precursors are predicated to be favorable both thermodynamically and kinetically for dinitrogen activation. Furthermore, through principal component analysis (PCA), one of the most commonly used unsupervised machine learning algorithms, we have identified that aromaticity, the HOMO–LUMO gap, and spin density collectively contribute to the reaction barrier of dinitrogen activation. Density functional theory ; Stability (learning theory) ; Radical ; Aromaticity ; Chemical stability ; Antiaromaticity ; Boron Synthesis and Properties of Aromatic Compounds ; Organoboron and organosilicon chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Seung
Kyun Ha (11727068) | Dipannita Kalyani (1325199) | Michael S. West (9993416) | Jessica Xu (6862943) | Yu-hong Lam (1319184) | Thomas Struble (21444433) | Spencer Dreher (2978559) | Shane W. Krska (1339053) | Stephen L. Buchwald (1307121) | Klavs F. Jensen (722008)
Source: Figshare (article, 2025-05-29)
Field: Chemistry — Organic Chemistry

Developing PharmaceuticallyRelevant Pd-CatalyzedC–N Coupling Reactivity Models Leveraging High-Throughput Experimentation This manuscript presents machine learning models for Pd-catalyzed C–N couplings constructed using a large, pharmaceutically relevant, structurally diverse dataset (4204 unique products) generated <i>de novo</i> using high-throughput experimentation. The dataset generation was enabled by the discovery of novel nanomole scale compatible automation friendly C–N coupling reaction conditions using LiOTMS as the base. The large dataset enabled the systematic evaluation of model performance using five different data-splitting strategies that were carefully designed to assess the models’ ability to both interpolate and extrapolate. The models exhibit high predictive performance across all splits as gauged by standard metrics. In addition, the models predicted with high accuracy the outcome of validation libraries that were outside the scope of the training set. Employing these models in the context of medicinal chemistry campaigns should result in significant enrichment of successful C–N couplings. Context (archaeology) ; Scope (computer science) ; Automation ; Coupling (piping) ; Scale (ratio) ; Training set ; Predictive modelling ; Experimental data Catalytic Cross-Coupling Reactions ; Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods