jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhen Liu (74646) | Jessica Vinskus (22418104) | Yue Fu (64387) | Peng Liu (120506) | Kevin J. T. Noonan (9317214) | Olexandr Isayev (1352004)
Source: Figshare (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Fast and AccurateRing Strain Energy Predictions withMachine Learning and Application in Strain-Promoted Reactions Ring strain energy (RSE) is crucial for understanding molecular reactivity, with broad implications in polymerization, click chemistry, drug discovery and beyond. However, quantitatively determining RSE through experiments or quantum mechanics (QM) is resource-intensive, limiting its application on a large scale. We present a machine learning (ML)-based workflow that enables the reliable and efficient prediction of RSE, entirely bypassing traditional QM calculations. Our workflow employs AIMNet2 machine learning interatomic potentials and Auto3D for the identification of low-energy conformers and RSE computation. Remarkably, it achieves an <i>R</i><sup>2</sup> of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol when benchmarked against the ωB97M-D4/Def2-TZVPP method, while running orders of magnitude faster than DFT calculations. To demonstrate the utility of our workflow, we successfully differentiated reactive from nonreactive molecules in copper-free click chemistry, [3 + 2] cycloaddition reaction and ring-opening metathesis polymerization, underscoring its transferability to diverse molecular systems. Additionally, we compiled the RSE Atlas, a computational database encompassing 16,905 single-ring molecules, offering a valuable resource for investigating factors influencing RSE. Our approach transforms RSE into a readily computable property, facilitating its integration into reaction designs. Workflow ; Identification (biology) ; Energy (signal processing) ; Cycloaddition ; Ring (chemistry) ; Probably approximately correct learning ; Limiting ; Potential energy surface ; Fragment (logic) Machine Learning in Materials Science ; Synthetic Organic Chemistry Methods ; Computational Drug Discovery Methods
Authors: Winston Gee | Abigail G. Doyle | Santiago Vargas | Anastassia N. Alexandrova
Source: ChemRxiv (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

Multi-level QTAIM-Enriched Graph Neural Networks for Resolving Properties of Transition Metal Complexes Here we evaluate the robustness and utility of quantum mechanical descriptors for machine learning with transition metal complexes. We utilize ab initio information from the quantum theory of atoms-in-molecules (QTAIM) for 60k transition metal complexes at multiple levels of theory (LOT), presented here in the tmQM+ dataset, to inform flexible graph neural network (GNN) models. We evaluate these models with several experiments, including training on limited charge and elemental compositions 1 and testing on unseen charges and elements, as well as training on smaller portions of the dataset. Results show that additional quantum chemical information improves performance on unseen regimes and smaller training sets. Furthermore, we leverage the tmQM+ dataset to analyze how QTAIM descriptors vary across different LOT and probe machine learning performance with less computationally expensive LOT. We determine that ab initio descriptors provide benefits across LOT, thereby motivating the use of lower-level DFT descriptors, particularly for predicting expensive or experimental molecular properties. Transition (genetics) ; Artificial neural network ; Graph ; Cognitive science ; Computer science ; Psychology ; Chemistry ; Theoretical computer science ; Artificial intelligence ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Itamar Wallwater | Yonatan Dubi | Ari Pakman | Anat Milo
Source: ChemRxiv (article, 2025-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

Can crystalline polymorphism be predicted from single-molecule properties? Understanding the crystallization of molecules, and specifically the appearance of polymorphs, is a great challenge to modern chemistry, with both fundamental and practical aspects. Here, motivated by the established ability of Machine Learning (ML) algorithms to perform classification tasks, we harness ML-based tools and existing chemical datasets to ask whether the existence of polymorphs of a molecular crystal can be predicted based solely on properties of the corresponding single molecules. We implemented an algorithm that can predict the existence of polymorphism with an average accuracy of 65\%. Although this is not enough to generate a reliable “polymorph predicting” engine, our results reveal intriguing trends. We suggest that the limited performance of the model reflects the inherent bias of crystallographic data towards monomorphs. Namely, the fact that only one crystal form of a certain compound has been observed in experiments to date does not rule out the possibility that additional stable crystal structures could exist. Our results provide statistical support for a broadly held claim in the crystal engineering community that the proportion of possible polymorphs is, in fact, much larger than represented in the data. Polymorphism (computer science) ; Molecule ; Crystallography ; Genetics ; Chemistry ; Biology ; Allele ; Gene ; Organic chemistry Various Chemistry Research Topics
Authors: Xiangyu Wen | Xiao Geng | Guosheng Su | Yizheng Li | Qidong Li | Yuxuan Yi | Lifen Liu
Source:  (review, 2025-03-01)
Field: Materials Science — Materials Chemistry

Author response for "Machine Learning-Driven Design of Single-Atom Catalysts for Carbon Dioxide Valorization to High-Value Chemicals: A Review of Photocatalysis, Electrocatalysis, and Thermocatalysis" Electrocatalyst ; Photocatalysis ; Catalysis ; Carbon dioxide ; Atom (system on chip) ; Value (mathematics) ; Carbon atom ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Engineering ; Physical chemistry ; Organic chemistry ; Machine learning ; Electrochemistry ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Didier Mathieu
Source: AI Chemistry (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Fast Conversion of Molecular Diagrams into Plausible Crystal Structures Using Graph-Based Force Fields Despite the value of molecular packing (MP) calculations in modeling the properties of organic crystals, its widespread adoption is hindered by the absence of a simple tool broadly accessible to non-specialists, and by the lack of reliability inherent to transferable force fields. To fill these gaps, we describe a versatile workflow, leveraging recent progress in the application of machine learning to the parameterization of interatomic potentials. It is provided as a Python script based only on free academic software running on any Linux system. A key ingredient to this workflow is a recent neural network pretrained to predict bespoke force field parameters for any organic compound on the basis of its molecular diagram. The resulting graph-based force field (GB-FF) is fed into the Tinker simulation engine and applied to crystal structures generated using the USPEX crystal structure prediction package. This low-cost workflow is found to outperform current state-of-the-art procedures based on heavily parameterized force fields, thus demonstrating the value of machine-learned bespoke potential parameters. Bespoke ; Workflow ; Python (programming language) ; Force field (fiction) ; Parameterized complexity ; Crystal structure prediction ; Molecular dynamics ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Fanzhi Meng | Wei Xu | Yanan Qian | Fengchun Sun | Bing Sun | Zhe Yang
Source: Journal of Loss Prevention in the Process Industries (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Artificial intelligence (AI)-enabled thermochemical risk modeling via self-attentive deep neural networks for predicting the SADT of organic peroxides Interpretability ; Hyperparameter ; Artificial neural network ; Bayesian optimization ; Deep learning ; Convergence (economics) ; Residual ; Feature selection ; Bayesian probability Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yuki Makinose
Source: Hosokawa Powder Technology Foundation ANNUAL REPORT (article, 2025-05-24)
Field: Chemistry — Organic Chemistry

Exploring Monodisperse Condition for Ceramic Nanoparticles Using Hydrothermal Synthesis and Machine Learning 本研究では「アルカリ処理水熱合成法」を用いて酸化鉄ナノ粒子をさまざまな条件で作製し，単分散条件の探索を機械学習により試みた．機械学習には，水熱処理温度，時間，アンモニア水量，Ole/M比率を説明変数として，CV値を目的変数として，SVR（サポートベクター回帰）分析を用いた．実験の結果，220°C，24 h，NH3量3.00 ml，Ole/M 1.00においてCV値11.4%を達成し，単分散に近いナノ粒子が得られた．CV値と実験条件のSVR分析により最適条件を推定したが，得られたCV値は25%と期待を下回った．さらに粒径とCV値を組み合わせた複合変数を設定しSVR分析を行ったが，最大推定値が実測値を超えられず，低CV値の推定が困難であることが明らかとなった．また，アルカリ処理水熱合成法を用いてCeO2ナノ粒子を合成したところ，粒度分布が広がる傾向がみられ，出発原料の選定が重要であることが判明した．今後は最小CV値の類似実験条件の探索などにより，CV値のさらなる低減を目指す． Hydrothermal circulation ; Dispersity ; Ceramic ; Nanoparticle ; Materials science ; Hydrothermal synthesis ; Nanotechnology ; Computer science ; Chemical engineering ; Engineering ; Composite material ; Polymer chemistry Nanomaterials for catalytic reactions
Authors: Muhammad Shaquille Hisham | Nisrina Anindya | Sparisoma Viridi | Nadya Amalia | Irfan Dwi Aditya | Sasfan Arman Wella
Source: Journal of Physics Conference Series (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Accelerated Lattice Thermal Conductivity Calculations for <i>α</i> -C <i>X</i> ( <i>X</i> = N, P, As) Using Machine Learning Interatomic Potentials, Density Functional Theory, and Molecular Dynamics Abstract Lattice thermal conductivity (LTC) plays a significant role in evaluating thermoelectric performance, as it reflects how efficiently heat is transported by phonons. However, conventional LTC calculations based on density functional theory (DFT) are computationally expensive and time-consuming. To address this challenge, we adopt a machine learning (ML)-based approach to construct interatomic force fields from a limited DFT dataset. These ML potentials are then used in molecular dynamics (MD) simulations to compute LTC efficiently. As a case study, we investigate carbon pnictide monolayers ( α -C X , with X = N, P, As), which are used as benchmark systems due to their promising optoelectronic and thermoelectric properties. Our ML model achieves comparable accuracy when trained with only half of the DFT dataset, reducing computational cost by about 1.7 times. Furthermore, when trained on a significantly smaller, randomly generated dataset, the approach achieves cost reductions of up to an order of magnitude. This combined DFT–ML–MD framework provides a fast, accurate, and scalable approach for predicting thermal transport in low-dimensional materials. Thermal conductivity ; Density functional theory ; Molecular dynamics ; Scalability ; Lattice (music) ; Thermoelectric effect ; Interatomic potential ; Computation Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Shuyuan Zhang | Ting Ye | Baocai Jing | Huiqi Yin | Dingyi Pan
Source: Journal of Computational Physics (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Non-Markovian intelligent dissipative particle dynamics integrated with machine learning for enhancing coarse-grained simulations • Utilizing machine learning to bridge molecular dynamics and dissipative particle dynamics. • Incorporating non-Markovian effects in coarse-grained modeling to accurately capture system dynamics. • Introducing pressure constraint mechanism to accurately replicate the pressure from all-atom simulations. • Quantitative comparison between Non-Markovian and Markovian models in molecular systems. We propose a machine learning-based coarse-grained method that integrates molecular dynamics (MD) with dissipative particle dynamics (DPD) to address the limitations of the Markovian approximation in systems where particle motion and fluctuating forces exhibit overlapping time scales. Our approach, termed non-Markovian intelligent dissipative particle dynamics (NM-IDPD), utilizes MD data to train a neural network capable of predicting both conservative and dissipative forces within the DPD framework, effectively accounting for non-Markovian effects. We have also incorporated a pressure constraint mechanism into the neural network to accurately capture the system pressure, which is a challenging issue for most traditional coarse-grained methods. Through applications to star polymers, methane, and water systems, NM-IDPD has demonstrated good performance in replicating both the static and dynamic properties of simulated systems across various time scales. This advancement offers a promising avenue for material dynamics simulation, enhancing the accuracy and efficiency of computational modeling in complex systems. Dissipative particle dynamics ; Dissipative system ; Artificial neural network ; Constraint (computer-aided design) ; Mechanism (biology) ; Particle (ecology) ; System dynamics ; Dynamics (music) ; Molecular dynamics Block Copolymer Self-Assembly ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Xiangyu Zhang (630437) | Lei Zhou (19808) | Tianshu Chu (2180654) | Chao Rong (2032945) | Weiwei Cheng (582223) | Jiaqing Zhu (9327641) | Bowei Zhang (334192) | Tao Wang (12008) | Fu-Zhen Xuan (19249340)
Source: Figshare (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Data-Driven High-ThroughputScreening of High-PerformanceSingle-Atom Catalysts for Hydrogen Evolution and Hydrogen Sensing The exploration of high-performance catalytic materials has attracted significant attention due to their substantial economic value. However, the vast material search space and inherent limitations of conventional experimental trial-and-error methods pose significant challenges in exploring these catalytic materials. Herein, we propose a data-driven high-throughput approach for screening high-performance single-atom catalysts (SACs) suitable for hydrogen evolution reactions (HER) and hydrogen sensing applications. This methodology integrates density functional theory (DFT) calculations and a graph neural network (GNN)-based machine learning algorithm. Our results indicate that this data-driven approach effectively predicts SACs for HER and hydrogen sensing applications. This integrated framework significantly accelerates the discovery and development of high-performance catalytic materials, thereby advancing hydrogen-related technologies. Catalysis ; Hydrogen ; Artificial neural network ; Density functional theory ; Hydrogen production Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Surajit Das | Raghunathan Ramakrishnan
Source: ArXiv.org (preprint, 2025-10-07)
Field: Materials Science — Materials Chemistry

Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations Accurate prediction of nuclear magnetic resonance (NMR) shielding with machine learning (ML) models remains a central challenge for data-driven spectroscopy. We present atomic variants of the Coulomb matrix (aCM) and bag-of-bonds (aBoB) descriptors, and extend them using radial basis functions (RBFs) to yield smooth, per-atom representations (aCM-RBF, aBoB-RBF). Local structural information is incorporated by augmenting each atomic descriptor with contributions from the n nearest neighbors, resulting in the family of descriptors, aCM-RBF(n) and aBoB-RBF(n). For 13C shielding prediction on the QM9NMR dataset (831,925 shielding values across 130,831 molecules), aBoB-RBF(4) achieves an out-of-sample mean error of 1.69 ppm, outperforming models reported in previous studies. While explicit three-body descriptors further reduce errors at a higher cost, aBoB-RBF(4) offers the best balance of accuracy and efficiency. Benchmarking on external datasets comprising larger molecules (GDBm, Drug12/Drug40, and pyrimidinone derivatives) confirms the robustness and transferability of aBoB-RBF(4), establishing it as a practical tool for ML-based NMR shielding prediction. Electromagnetic shielding ; Robustness (evolution) ; Benchmarking ; Coulomb ; Transferability ; Support vector machine ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Lyndon Emsley
Source: Structural Dynamics (article, 2025-03-01)
Field: Chemistry — Spectroscopy

Structure determination by NMR Crystallography, from microcrystalline powders to amorphous molecular solids Structure elucidation of amorphous materials and microcrystalline solids is one of the key challenges in chemistry today. While techniques such as single crystal diffraction and cryo-electron microscopy are generally not able to characterize such materials, we will show how an approach based on measured NMR chemical shifts in combination with methods for large scale computation of shifts can rapidly determine full three-dimensional structures from powders. For example, using a machine learning model of chemical shifts, we determine the complete atomic-level structure of the amorphous form of a drug molecule by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with chemical shifts predicted using machine learning for MD simulations of large systems.(1,2) From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular interaction energies. In other examples, we will show how chemical shift driven NMR crystallography can be applied to determine the complete three-dimensional structures of challenging complex materials such as calcium-silicate-hydrates,(3,4,5) or hybrid organic- inorganic photovoltaic perovskites.(6,7) Microcrystalline ; Amorphous solid ; Materials science ; Crystallography ; X-ray crystallography ; Chemistry ; Diffraction ; Physics ; Optics Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Dehe Fan | Qifeng Luo | Mianting Wu | Bo Peng
Source:  (article, 2025-06-20)
Field: Materials Science — Polymers and Plastics

Signal2seq: Generation Model of Textile Component Analysis Sequence for Textile Fiber Composition Analysis The analysis of textile fiber composition using deep learning technology represents an intersection between the textile industry and artificial intelligence, holding significant theoretical and practical importance. The goal of textile fiber composition analysis is to identify the constituent materials and their respective proportions, which can be categorized into qualitative and quantitative analysis. Previous approaches have primarily relied on traditional machine learning methods. In this paper, we propose a unified modeling approach for composition analysis tasks using sequence generation techniques in deep learning, simply treating it as a sequence generation task based on input signals from collected samples. Component descriptions (category labels and content labels) are represented as discrete label sequences. We train neural networks to perceive signals and generate the desired sequences. During model training, we observed that different label orders significantly impact model performance. Compared to traditional machine learning algorithms, our method requires minimal assumptions and prior knowledge, achieving state-of-the-art experimental results on the FENIR dataset. Textile ; Component (thermodynamics) ; Intersection (aeronautics) ; Sequence (biology) ; Deep learning ; Artificial neural network ; Composition (language) ; Task (project management) Textile materials and evaluations
Authors: Andreas Mielke | Alexander Scrimgeour | Enrico Tapavicza
Source: Journal of Chemical Information and Modeling (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Chemical Space of Molecular Nanomotors: Optimizing Photochemical Properties for One- and Two-Photon Applications Light-driven molecular nanomotors hold promise for applications in materials science and biomedicine. Significant efforts have focused on improving their efficiency, often targeting single candidate molecules. Here, we present a systematic, data-driven approach to design nanomotors with high isomerization quantum yields for one- and two-photon applications, the latter being critical for biomedical applications requiring near-infrared light. We analyze the excited state properties of a data set of 2016 nanomotors substituted with electron-donating and electron-withdrawing (push-pull) groups. Among the top candidates, we achieved an increase in two-photon absorption strengths of up to 2 orders of magnitude compared to existing nanomotors. To ensure that the π-π* character of the excited state is preserved, which is necessary to achieve the required photoisomerization, we introduce a transition density similarity score that gauges the excited state character based on the transition. Furthermore, we benchmark three machine learning (ML) models─Kernel Ridge Regression, XGBoost, and a Neural Network─using physical and connectivity-based molecular descriptors. The excellent accuracy of our ML predictions holds promise to replace computationally costly quantum chemistry calculations in chemical space explorations. Excited state ; Chemical space ; Quantum chemical ; Benchmark (surveying) ; Isomerization ; Quantum ; Quantum chemistry ; Space (punctuation) ; Set (abstract data type) Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Micro and Nano Robotics
Authors: Weiyi Xia | Maxim Moraru | Ying Wai Li | Timothy Liao | James R. Chelikowsky | Cai‐Zhuang Wang
Source: ArXiv.org (preprint, 2025-06-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning-assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the Fe6Co17Zr6 compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary Fe5Co18Zr6 phase with a strong anisotropy of K1 = 1.1 MJ/m3, and a stable quaternary magnetic Fe5Co16Zr6Mn4 compound. Ternary operation ; Magnet ; Chemical space ; Metastability ; Convolutional neural network ; Magnetic anisotropy ; Crystal (programming language) ; Anisotropy Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials