jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richard A. Bryce | Christopher D. Williams | Neil A. Burton
Source: ChemRxiv (preprint, 2025-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Charged Small Molecule Conformations in Solution using a Balanced ML/MM Potential Reliably evaluating the most stable conformations of charged small organic molecules in solution poses a major challenge to computational chemistry, due to the accuracy or computational efficiency limitations of conventional methodologies. In this paper, we present a hybrid machine learning/molecular mechanics (ML/MM) potential, with dynamic, conformationally-dependent charges on the atoms in the ML region, that can be used to address this important challenge in drug design. By way of a case study, molecular dynamics simulations were used to compare the performance of intermolecular potentials in evaluating the solution phase conformational free energy differences of pharmaceutically relevant ligands based on the protonated 2-phenylethylamine scaffold. We show that satisfactory performance of an MM-only force field in predicting the preferred conformations of these molecules in solution (with a mean absolute error (MAE) = 0.9 kcal/mol) is due a convenient cancellation of errors in the intramolecular and intermolecular contributions to the conformational free energy difference. Specifically, the underestimation of intramolecular cation- interactions which preferentially stabilise the folded conformation is compensated by underestimated solute-solvent interactions which preferentially stabilise the extended conformation. A straightforward approach to an ML/MM potential, in which the intramolecular MM representation of the solute is simply substituted by a ML model without making other changes to the potential, disrupts this error cancellation and results in a systematic underperformance relative to the MM-only approach, with a MAE of 1.9 kcal/mol. To remedy this shortcoming, a new ML/MM potential is presented that combines an accurate, trained-for-purpose ML (PairFEQ-Net) model of the charged solute, with the polarisable SWM3 MM model of water. Crucially, by employing an empirical parameter to scale gas phase charges, the ML model predicts dynamic, solvent-polarised charges that effectively electrostatically embed the solute in the solvent in a balanced way, outperforming the MM-only approach when it comes to conformational free energy predictions, reducing the MAE to 0.5 kcal/mol. This balanced ML/MM potential furnishes a possible route to the chemically accurate prediction of the shapes of organic molecules in aqueous solution. Intramolecular force ; Intermolecular force ; Force field (fiction) ; Molecule ; Protonation ; Small molecule ; Representation (politics) ; Molecular dynamics Nonlinear Optical Materials Research
Authors: Apurva Sharma | Tarandeep Singh | Neerja Mittal Garg | Quoc Cuong Ngo | Nemuel Daniel Pah | Dinesh Kumar
Source: Smart Agricultural Technology (article, 2025-08-08)
Field: Chemistry — Analytical Chemistry

S2NN: Spectra-spiking neural network for energy-efficient wheat protein estimation using hyperspectral imaging • First application of SNN for HSI regression analysis. • Spectra-Spiking Neural Network (S 2 NN) is proposed for wheat protein estimation. • The model built for protein content ranges from 9.5 % to 17.25 % by weight. • The S 2 NN reduces approximately 15 to 108 times energy consumption and uses approximately 5 to 34 times fewer FLOPs compared to ANN, CNN, and Transformer. Hyperspectral imaging (HSI) combined with traditional machine learning techniques has demonstrated an accurate assessment of seed chemical composition. However, their high computational demands and energy consumption make them unsuitable for real-time applications. Spiking neural networks (SNNs) are recognized as an energy-efficient alternative due to their spike-based mechanism. We explore SNN in the field of HSI for the regression analysis to estimate the wheat protein content. We proposed a spectra-spiking neural network (S 2 NN) over a single timestep that uses direct input encoding to analyze spectral data more efficiently. S 2 NN achieved an R² value of 0.9969, RMSE, and RPD of 0.0813 and 17.85, respectively. The S 2 NN was compared with other neural network models (i.e., ANN, CNN, and Transformer) in terms of accuracy, training time, floating-point operations (FLOPs), and energy consumption. We first compared the performance of the S 2 NN with ANN while maintaining a similar network architecture for both models. In this comparison, S 2 NN achieved a 5.08 % higher R² value and was approximately 2.06 times more energy-efficient. Next, we train the ANN, CNN, and Transformer to reach their maximum predictive accuracy, yielding results comparable to those of S 2 NN. In this case, the S 2 NN reduces energy consumption by approximately 15 to 108 times, requires approximately 5 to 34 times fewer FLOPs, and demonstrates significantly shorter training times compared to other neural network architectures. These findings show the potential of S 2 NNs as an efficient alternative to other neural networks, particularly for resource-constrained real-time agricultural applications where high-end computational devices may not be available. The code is available at https://github.com/Apurva-Git-Hub/Spectra-SNN.git . Hyperspectral imaging ; Artificial neural network ; Estimation ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Biological system ; Pattern recognition (psychology) ; Environmental science ; Mathematics ; Biology ; Statistics ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yoshiro Ito | Tatsunori Taniai | Ryo Igarashi | Yoshitaka Ushiku | Kanta Ono
Source: ArXiv.org (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or"frames." However, unlike molecules, determining frames for crystal structures is challenging due to their infinite and highly symmetric nature. In particular, existing methods rely on a statically fixed frame for each structure, determined solely by its structural information, regardless of the task under consideration. Here, we rethink the role of frames, questioning whether such simplistic alignment with the structure is sufficient, and propose the concept of dynamic frames. While accommodating the infinite and symmetric nature of crystals, these frames provide each atom with a dynamic view of its local environment, focusing on actively interacting atoms. We demonstrate this concept by utilizing the attention mechanism in a recent transformer-based crystal encoder, resulting in a new architecture called CrystalFramer. Extensive experiments show that CrystalFramer outperforms conventional frames and existing crystal encoders in various crystal property prediction tasks. Property (philosophy) ; Frame (networking) ; Graph ; Reference frame ; Task (project management) ; Crystal (programming language) ; Encoder X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds
Authors: Xinping Feng | You Xu | Jing Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-03)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity Abstract Machine learning force fields (MLFFs) offer a promising balance between quantum mechanical (QM) accuracy and molecular mechanics efficiency. While MLFFs have shown strong performance in modeling short-range interactions and reproducing potential energy surfaces, their ability to capture long-range cooperative effects remains underexplored. In this study, we assess the ability of three MLFF models — ANI, MACE-OFF, and Orb — to reproduce cooperative interactions arising from environmental induction and dispersion, which are essential for many biomolecular processes. Using a recently proposed framework, we quantify hydrogen bond (H-bond) cooperativity in N-methylacetamide polymers. Our results show that all MLFFs capture cooperativity to some extent, with MACE-OFF yielding the closest agreement with QM data. These findings highlight the importance of evaluating many-body effects in MLFFs and suggest that H-bond cooperativity can serve as a useful benchmark for improving their physical fidelity. Benchmarking ; Cooperativity ; Hydrogen bond ; Computer science ; Nanotechnology ; Mechanical engineering ; Artificial intelligence ; Materials science ; Machine learning ; Chemistry ; Engineering ; Physics ; Molecule ; Economics ; Management ; Quantum mechanics Machine Learning in Materials Science
Authors: P. V. Stishenko | Qian Chen | Julia Westermayr | Reinhard J. Maurer | Andrew J. Logsdail
Source: ChemRxiv (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Practical integration of machine learning into ab initio calculations and workflows: Accelerating the SCF cycle via density matrix predictions Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials but their transferability is often limited due to the vast amount of data needed for training, including when addressing the need to fine-tune universal models for each specific system to be studied. Here, we demonstrate how contributions from system-specific electronic structure machine learning (ESML) models may be combined (“stitched”) to deliver density matrices of entire systems of interest, improving the initial guess for the self-consistent field cycle and delivering gains in computational efficiency. The “stitching” of density matrices is demonstrated for sequential calculations, such as geometry optimization and molecular dynamics, and we show that the synergistic use of ESML models and density matrix extrapolation algorithms can accelerate standard computational calculations. The algorithms are demonstrated for test cases relating to water clusters and a methane clathrate cage, with the benefits discussed. The future opportunities for hybrid quantum mechanical and ML (QM/ML), and also ML/ML paradigms, are broad-ranging with significant computational speed-up attainable. Extrapolation ; Ab initio ; Field (mathematics) ; Density matrix ; Density functional theory ; Matrix (chemical analysis) ; Transferability ; Electronic structure Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Yueyang Lin | Pengfei Ou
Source: AI Agent (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

From descriptors to machine learning interatomic potentials: a review of AI-accelerated electrocatalyst design Electrocatalysis underpins key energy-conversion reactions, such as carbon dioxide reduction and oxygen evolution/reduction reactions; however, catalyst screening based on density functional theory (DFT) is constrained by rapidly escalating computational costs. In recent years, coupling machine learning (ML) with DFT has opened a promising path around this bottleneck. This review synthesizes recent advances in artificial intelligence (AI)-accelerated electrocatalysis along three pillars: descriptors, ML techniques, and machine learning interatomic potentials (MLIPs). First, we compare the acquisition strategies for three classes of foundational descriptors: intrinsic statistical, electronic structure, and geometric/microenvironmental. Second, we summarize how algorithms such as tree ensembles and kernel methods perform across varying feature dimensionalities and extrapolation regimes, and discuss recent research on customized composite descriptors in the context of these models. Third, we categorize mainstream MLIPs into three families: “general graph-network”, “symmetry-equivariant”, and “extreme-efficiency”. We then compare their trade-offs in accuracy, computational cost, and scope of application. Finally, we highlight limitations of available datasets and propose practical paths forward. Computational learning theory ; Context (archaeology) ; Extrapolation ; Categorization ; Electrocatalyst ; Feature (linguistics) ; Multi-task learning Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Chenyang Wei | Wenbo Mu | Hongyuan Zhang | Zhenghui Liu | Tiancheng Mu
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-guided design of double-atom catalysts for enhanced electrocatalytic performance Key features of double-atom catalysts are revealed through DFT-integrated machine learning analysis, and a new ML-based screening framework is developed to enable rapid identification of efficient catalysts for CO 2 RR, HER, and OER. Catalysis ; Atom (system on chip) ; Materials science ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Computer science ; Embedded system ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Jiahao Cao | Ding He | Y. Liu | Lei Wang | Qi Liu | Guoqiang Yang | Zhigang Li | Long Zhang
Source: Microchemical Journal (article, 2025-11-04)
Field: Chemistry — Analytical Chemistry

NIR spectroscopy and machine learning for tobacco product authentication: A non-destructive approach with broader safety applications Preprocessor ; Convolutional neural network ; Artificial neural network ; Support vector machine ; Pipeline (software) ; Identification (biology) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smoking Behavior and Cessation ; Water Quality Monitoring and Analysis
Authors: E Zhang | Wei Li | Dongmei Huang | Nataša Trišović
Source: International Journal of Dynamics and Control (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

LBFNN algorithm for studying response of shape memory alloy oscillator under Gaussian colored noise and periodic excitation SMA* ; Artificial neural network ; Stochastic differential equation ; Noise (video) ; Transient (computer programming) ; Nonlinear system ; Basis function ; Gaussian noise ; Control theory (sociology) ; Stochastic resonance Shape Memory Alloy Transformations ; Acoustic Wave Phenomena Research ; Magnetic Properties and Applications
Authors: Peichen Zhong | Dongjin Kim | David A. King | Bingqing Cheng
Source: ArXiv.org (preprint, 2025-04-07)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential can infer electrical response Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO$_3$ perovskite. This work thus extends the capability of MLIPs to predict electrical response--without training on charges or polarization or BECs--and enables accurate modeling of electric-field-driven processes in diverse systems at scale. Ferroelectricity ; Polarization (electrochemistry) ; Interatomic potential ; Phase transition ; Electric potential energy ; Hysteresis ; Electrostatics ; Charge (physics) ; Work (physics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides
Authors: Gergő Róth | Ákos Kuki | Aron Kipyegon Rop | Lajos Nagy | Zuura Kyzy Kaldybek | Máté Benedek | Miklós Zsuga | Tibor Nagy | Sándor Kéki
Source: Analytical Chemistry (article, 2025-09-07)
Field: Chemistry — Spectroscopy

Rapid Copolymer Analysis of Unresolved Mass Spectra by Artificial Intelligence In this Article, we present a novel data analysis method for the determination of copolymer composition from low-resolution mass spectra, such as those recorded in the linear mode of time-of-flight (TOF) mass analyzers. Our approach significantly extends the accessible molecular weight range, enabling reliable copolymer composition analysis even in the higher mass regions. At low resolution, the overlapping mass peaks in the higher mass range hinder a comprehensive characterization of the copolymers. Our approach, however, extracts the hidden information on the unique macromolecular chains embedded in these unresolved peaks. Regression relationships were developed between easily computable mass spectral parameters derived from the measured data and key characteristics of the copolymer structure including the average mole fraction, the number-average repeat unit count, and the monomer distribution. Two regression models were constructed: one using conventional statistical methods and the other employing a machine learning approach based on an Artificial Neural Network (ANN). The spectrum evaluation process is demonstrated for the analysis of various poly(<i>N</i>-acryloylmorpholine)-<i>block</i>-poly(<i>N</i>-isopropylacrylamide) (PNAM-<i>b</i>-PNIPAM) diblock copolymers. Our simplified approach is validated using experimental data and by comparison with our recently developed and reported highly computationally demanding method. Copolymer ; Artificial neural network ; Mass spectrum ; Characterization (materials science) ; Range (aeronautics) ; Monomer ; Principal component analysis ; Mass spectrometry ; Analytical Chemistry (journal) Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Mukesh K. Choudhary | Aditya Raj | Gowri Sankar S | P. Ravindran
Source: Advanced Theory and Simulations (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Composition and Structure Based GGA Bandgap Prediction Using Machine Learning Approach Abstract Machine learning (ML) regression models are developed to predict the energy bandgap of materials using only their chemical compositions and crystal structures. The study evaluates eight stand‐alone ML regression models and eleven ensemble models, four based on stacking and seven on bagging, incorporating Ridge regression with cross‐validation (RidgeCV) and Least Absolute Shrinkage and Selection Operator with cross‐validation (LassoCV). All models are trained on the matbench‐mp‐gap dataset from Matminer, which includes 106,113 compounds with energy band gaps computed using the generalized gradient approximation with the Perdew Burke Ernzerhof functional (GGA‐PBE). Feature engineering is performed using Matminer and Pymatgen, while feature importance and correlation are assessed through permutation and Pearson methods. Among the stand‐alone models, the Random Forest (RF) model achieves the highest accuracy with a maximum coefficient of determination (R2) of 0.943 and a root mean square error (RMSE) of 0.504 eV. The best ensemble model, based on bagging, reaches R2 of 0.948 and RMSE of 0.479 eV. The model is applied to predict band gaps of new half‐Heusler (HH) compounds with 18 valence electrons. These predictions are validated through density functional theory (DFT) calculations without much computational effort, accelerating the discovery of potential optoelectronic materials from wide chemical space. Band gap ; Artificial intelligence ; Composition (language) ; Computer science ; Materials science ; Machine learning ; Optoelectronics ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Vijay Subramanya | Cho‐Pei Jiang | Chinmai Bhat | Yulius Shan Romario | Xiaopeng Li | Maziar Ramezani
Source: The International Journal of Advanced Manufacturing Technology (review, 2025-08-28)
Field: Chemistry — Organic Chemistry

A comprehensive review of supervised machine learning algorithms integrated with vat photopolymerization process for enhanced prediction of functionalities Photopolymer ; Process (computing) ; Computer science ; Algorithm ; Machine learning ; Artificial intelligence ; Materials science ; Polymer ; Programming language ; Composite material Photopolymerization techniques and applications
Authors: Thanh-Nam Huynh | Bassim Mounssef | Dmitry Sharapa | Felix Studt | Tomáš Bučko
Source: The Journal of Physical Chemistry C (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Anharmonic Correction to the Adsorption Free Energy of Oxygen-Containing Intermediates on Pt(111) by Machine-Learned Force Field-Based Thermodynamic Integrations Accurate and efficient descriptions of adsorption free energy are of fundamental importance to bringing theoretical findings closer to experiments. The present study introduced a robust combination of machine learning and (un)constrained molecular dynamics-based λ-path thermodynamic integration methods to accurately estimate the anharmonic correction to the free energy of adsorbing systems and, thus, the adsorption free energy. The approach was demonstrated for the adsorptions of the oxygenated species exhibiting different anharmonic behaviors, namely, atomic oxygen (O), hydroxyl (OH), and hydroperoxyl (OOH), on the Pt(111) surface. The corrected adsorption free energies reveal the significant influence of anharmonic effects, which could account for up to 39% of the entropy loss estimated by the conventional harmonic approximation, even at near room temperature. This highlights the limitations of the current harmonic approximation and underscores the need to account for anharmonicity for better descriptions of the adsorption processes. The present method paves the way for the accurate adsorption of free energy calculations to be performed routinely toward more powerful theoretical predictions of heterogeneously catalyzed reactions. Anharmonicity ; Adsorption ; Force field (fiction) ; Oxygen ; Field (mathematics) ; Energy (signal processing) ; Gibbs free energy ; Thermodynamics ; Chemistry ; Physics ; Physical chemistry ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science