jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Minhuan Li | Kevin M. Dalton | Doeke R. Hekstra
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-01-15)
Field: Materials Science — Materials Chemistry

SFCalculator: connecting deep generative models and crystallography Proteins drive biochemical transformations by transitioning through distinct conformational states. Understanding these states is essential for modulating protein function. Although X-ray crystallography has enabled revolutionary advances in protein structure prediction by machine learning, this connection was made at the level of atomic models, not the underlying data. This lack of connection to crystallographic data limits the potential for further advances in both the accuracy of protein structure prediction and the application of machine learning to experimental structure determination. Here, we present SFCalculator, a differentiable pipeline that generates crystallographic observables from atomistic molecular structures with bulk solvent correction, bridging crystallographic data and neural network-based molecular modeling. We validate SFCalculator against conventional methods and demonstrate its utility by establishing three important proof-of-concept applications. First, SFCalculator enables accurate placement of molecular models relative to crystal lattices (known as phasing). Second, SFCalculator enables the search of the latent space of generative models for conformations that fit crystallographic data and are, therefore, also implicitly constrained by the information encoded by the model. Finally, SFCalculator enables the use of crystallographic data during training of generative models, enabling these models to generate an ensemble of conformations consistent with crystallographic data. SFCalculator, therefore, enables a new generation of analytical paradigms integrating crystallographic data and machine learning. Generative grammar ; Computer science ; Artificial intelligence ; Generative model ; Natural language processing Enzyme Structure and Function ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Bhupalee Kalita | Hatice Gökcan | Olexandr Isayev
Source: Nature Computational Science (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials at the centennial crossroads of quantum mechanics Centennial ; Interpretability ; Bridging (networking) ; Quantum ; Chart ; Computational mechanics ; Transformative learning Machine Learning in Materials Science ; Quantum many-body systems ; Computational Drug Discovery Methods
Authors: Krista G. Steenbergen | Stephanie Lambie | Charlie Ruffman | Nicola Gaston
Source: Small Science (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Atomic‐Scale Dynamics at the Interface of Doped Liquid Gallium: Contrasting Effects of Gallium Oxide and Vacuum Liquid gallium exhibits a unique, geometrically structured surface that directly influences the diffusion and coalescence of metal solutes at its surface. The complex interplay between different chemical species and gallium's unusual interfacial properties remains poorly understood, yet it plays a crucial role in controlling dopant dynamics, with applications spanning catalysis, nanoscale fabrication, flexible electronics, and liquid metal batteries. Herein, large-scale simulations with <i>ab</i> <i>initio</i>-trained machine learning force fields reveal strikingly different interactions of Ag, Au, Bi, Li, Pt, and Sn with liquid gallium interfaces, including both liquid-vacuum and liquid-gallium oxide boundaries. For example, Bi dopants migrate strongly toward vacuum interfaces but are repelled by the oxide interface, while Au is repelled by both interfaces. The results have direct implications for applications involving doped liquid gallium systems, including optimizing Bi surface patterning in plasmonic and catalytic applications or the use of Li in liquid metal batteries. More broadly, these findings underscore the critical role of interfaces in modulating dopant dynamics, offering new pathways for tuning the properties and functionalities of liquid metal technologies. Gallium ; Dopant ; Materials science ; Nanotechnology ; Doping ; Liquid metal ; Oxide ; Atomic units ; Coalescence (physics) ; Chemical physics ; Chemistry ; Optoelectronics ; Metallurgy ; Physics Quantum Dots Synthesis And Properties ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Haotian Liu | Meng‐Tzu Weng | Yi-Ping Huang | Yijia Luo | Yongping Zheng
Source: Computational Materials Science (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Self supervised ensemble learning models for 2D semiconductors bandgap prediction Semiconductor ; Band gap ; Materials science ; Ensemble learning ; Wide-bandgap semiconductor ; Computer science ; Optoelectronics ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Ga2O3 and related materials ; Semiconductor materials and interfaces
Authors: Arslan Mazitov | Ivan A. Kruglov | A. V. Yanilkin | Aleksey V. Arsenin | Valentyn S. Volkov | Dmitry G. Kvashnin | Artem R. Oganov | Kostya S. Novoselov
Source: npj Computational Materials (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Substrate-aware computational design of two-dimensional materials Two-dimensional (2D) materials attract considerable attention due to their remarkable electronic, mechanical and optical properties. Despite their use in combination with substrates in practical applications, computational studies often neglect the effects of substrate interactions for simplicity. This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine-learning interatomic potentials training protocol, and the ab initio thermodynamics approach. Using the molybdenum-sulfur system on a sapphire substrate as a case study, we reveal several new stable and metastable structures, including previously known 1H-MoS2 and newly found Pmma Mo3S2, $$P\bar{1}$$ Mo2S, P21m Mo5S3, and P4mm Mo4S, where the Mo4S structure is specifically stabilized by interaction with the substrate. Finally, we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used chemical vapor deposition technique. Substrate (aquarium) ; Computer science ; Materials science ; Geology Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Geun Ho Kim | Alex Y. Liu | Jack Jarvis | Erika Bonatti | Zhaofei Li | Hua Song
Source: Catalysis Today (article, 2025-06-23)
Field: Chemistry — Analytical Chemistry

Machine learning assisted bio-oil deoxygenation under methane environment: The model compound study on guaiacol Deoxygenation ; Guaiacol ; Hydrodeoxygenation ; Catalysis ; Methane ; Organic chemistry ; Chemistry ; Biochemical engineering ; Environmental science ; Engineering Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Hydrocarbon exploration and reservoir analysis
Authors: Amir Karimdoost Yasuri
Source: Journal of Coatings Technology and Research (article, 2025-11-03)
Field: Materials Science — Surfaces, Coatings and Films

Review of surface engineering through wettability control: innovations, sustainability, and interdisciplinary impact Surface engineering ; Transformative learning ; Surface modification ; Wetting ; Sustainability ; Work (physics) ; Resource (disambiguation) Surface Modification and Superhydrophobicity ; Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials
Authors: Daniele Angioletti | Stefano Raniolo | Vittorio Limongelli
Source: The Journal of Chemical Physics (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

HEroBM: A deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures Molecular simulations play a pivotal role in chemistry, biology, and material sciences, enabling the study of complex dynamic properties within systems. Coarse-grained (CG) techniques have emerged as indispensable tools in this domain, facilitating the sampling of large-scale systems and extending simulation timescales by simplifying system representation. However, CG approaches involve a trade-off: they sacrifice atomistic details that may be crucial for understanding the underlying processes. To address this challenge, a recommended strategy is to identify key CG conformations and employ backmapping methods to retrieve atomistic coordinates. Currently, rule-based methods often yield suboptimal geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. In contrast, machine learning techniques offer higher accuracy but may lack transferability between systems or be tied to specific CG mappings. In this study, we present HEroBM, a dynamic and scalable method that utilizes deep equivariant graph neural networks and a hierarchical approach to achieve high-resolution backmapping. HEroBM is capable of handling any type of CG mapping, providing a versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Grounded in local principles, HEroBM spans the entire chemical space and can be applied across systems of varying composition and sizes. We demonstrate the versatility of our framework through a range of biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach accurately generates atomistic coordinates for a G protein-coupled receptor bound to an organic small molecule within a cholesterol/phospholipid bilayer. The high-fidelity HEroBM backmapping enables researchers to effortlessly transition between CG and all-atom simulations, opening unprecedented avenues for molecular investigations. Computer science ; Scalability ; Theoretical computer science ; High fidelity ; Graph ; Artificial intelligence ; Distributed computing ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Oz Y. Mendelsohn | Michal Hartstein | Stefan Chmiela | Alexandre Tkatchenko | Leeor Kronik
Source: Chemical Physics Letters (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach Halide perovskites (HaPs) have emerged as promising new materials for a wide range of optoelectronic applications, notably solar energy conversion. These materials are well known to exhibit significant dynamical effects even at room temperature, which affect both their electronic properties and their long-term stability. Molecular dynamics (MD) simulations can provide significant insights into such effects. However, long time scale simulations require both accuracy and scalability. The latter is an issue for first principles methods and the former is challenging for classical force fields. Machine-learned force fields (MLFF) are a promising avenue for bridging across this seeming contradiction. Here, we apply the gradient-domain machine learning approach, using CsPbBr 3 as an example. We find that training based on room temperature density functional theory (DFT) data fails to generate an MLFF that provides long-term stable MD, owing to an insufficient sampling of rare events in the training set. We show that this problem is resolved by using a temperature ensemble (TE) method, which can be generated in parallel and yields a combined data set based on MD trajectories from a variety of temperatures. The MLFF model based on the TE method yields high accuracy for long-term simulations, showing remaining errors of the same magnitude of inherent errors in the DFT calculation. • Stable molecular dynamics based on machine-learned force fields. • Temperature ensemble of training data essential to capturing rare events. • Demonstrated on CsPbBr 3 as a model halide perovskite. Halide ; Molecular dynamics ; Domain (mathematical analysis) ; Chemical physics ; Dynamics (music) ; Molecular machine ; Chemistry ; Materials science ; Physics ; Statistical physics ; Computational chemistry ; Nanotechnology ; Inorganic chemistry ; Mathematics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Yoshiro Ikeda | Yuki Akura | Masaki Shimofuri | Amit Banerjee | Toshiyuki Tsuchiya | Jun Hirotani
Source: Journal of Applied Physics (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Estimating depth-directional thermal conductivity profiles using neural network with dropout in frequency-domain thermoreflectance Non-contact and non-destructive methods are essential for accurately determining the thermophysical properties necessary for the optimal thermal design of semiconductor devices and for assessing the properties of materials with varying crystallinity across their thickness. Among these methods, frequency-domain thermoreflectance (FDTR) stands out as an effective technique for evaluating the thermal characteristics of nano/microscale specimens. FDTR varies the thermal penetration depth by modifying the heating frequency, enabling a detailed analysis of the thermophysical properties at different depths. This study introduces a machine learning approach that employs FDTR to examine the thermal conductivity profile along the depth of a specimen. A neural network model incorporating dropout techniques was adapted to estimate the posterior probability distribution of depth-wise thermal conductivity. Analytical databases for both uniform and non-uniform thermal conductivity profiles were generated, and the machine learning model was trained using these databases. The effectiveness of the predictive model was confirmed through assessments of both uniform and non-uniform thermal conductivity profiles, achieving a coefficient of determination between 0.96 and 0.99. For uniform thermal conductivity, the method attained mean absolute percentage errors of 1.362% for thermal conductivity and 3.466% for thermal boundary conductance (compared to actual values in the analytically calculated database). In cases of non-uniform thermal conductivity, the prediction accuracy decreased, particularly near the sample's surface, primarily due to the limited availability of machine learning data at higher heating frequencies. Dropout (neural networks) ; Thermal conductivity ; Frequency domain ; Artificial neural network ; Materials science ; Thermal ; Conductivity ; Domain (mathematical analysis) ; Acoustics ; Computer science ; Composite material ; Physics ; Mathematics ; Artificial intelligence ; Thermodynamics ; Mathematical analysis ; Machine learning Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Heat Transfer and Optimization
Authors: Jun Huo | Hao Dong
Source: The Journal of Physical Chemistry Letters (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Δ-EGNN Method Accelerates the Construction of Machine Learning Potential Recent advancements in molecular simulations highlight the substantial computational demands of generating high-precision quantum mechanical labels for training neural network potentials. These challenges emphasize the need for improvements in delta-machine learning techniques. The Equivariant Graph Neural Network (EGNN) framework, grounded in a message-passing mechanism that preserves structural equivariance, enables refined atomic representations through interaction-driven updates. We introduce the Δ-EGNN model, which achieves high prediction accuracy for both molecular and condensed-phase systems. For example, in periodic water box systems, a mean absolute error of 1.722 meV/atom for energy (global property) and 0.0027 e for partial charge (local property) were achieved with training on just 800 labels. Δ-EGNN is computationally efficient, achieving speedups of 1-2 orders of magnitude compared to conventional methods at the MP2 level. In contrast to models directly trained on total energies, such as NequIP, MACE, and Allegro, the Δ-EGNN model employs delta-machine learning to predict the difference between energies derived from low- and high-level electronic structure methods, providing a significant advantage in reducing computational costs while preserving high accuracy. In summary, Δ-EGNN opens a new avenue for exploring energy landscapes and constructing machine learning potentials with afforable computational overhead, facilitating routine quantum mechanical calculations for complex molecular systems. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: C. M. A. Rahman | A. Romero | Prashnna Gyawali
Source: Communications Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Supervised pretraining for material property prediction Accurate prediction of material properties facilitates the discovery of novel materials with tailored functionalities. Deep learning models have recently shown superior accuracy and flexibility in capturing structure-property relationships. However, these models often rely on supervised learning, which requires large, well-annotated datasets—an expensive and time-consuming process. Self-supervised learning (SSL) offers a promising alternative by pretraining on large, unlabeled datasets to develop foundation models that can be fine-tuned for material property prediction. In this work, we propose supervised pretraining, where available class information serves as surrogate labels to guide learning, even when downstream tasks involve unrelated material properties. We evaluate this strategy on two state-of-the-art SSL models and introduce a novel framework for supervised pretraining. To further enhance representation learning, we propose a graph-based augmentation technique that injects noise to improve robustness without structurally deforming material graphs. The resulting foundation models are fine-tuned for six challenging material property predictions, achieving significant performance gains over baselines, ranging from 2% to 6.67% improvement in mean absolute error (MAE)—and establishing a new benchmark in material property prediction. This study represents the first exploration of supervised pertaining with surrogate labels in material property prediction, advancing methodology and application in the field. Accurate prediction of material properties, largely facilitated by machine learning, is crucial for discovering novel materials with tailored functionalities, yet traditional deep learning models require large annotated datasets. Here, the authors introduce a self-supervised pretraining approach using surrogate labels, achieving up to 6.67% improvement in prediction accuracy, thus setting a benchmark in material property predictions and enhancing methodological robustness. Property (philosophy) ; Robustness (evolution) ; Flexibility (engineering) ; Benchmark (surveying) ; Supervised learning ; Representation (politics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Neural Network Applications
Authors: Lukka Thuyavan Yogarathinam | Sani I. Abba | Jamilu Usman | Abdulhayat M. Jibrin | Isam H. Aljundi | Nadeem Baig
Source: Asian Journal of Organic Chemistry (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Data‐Driven Chemometric Modeling for Predicting the Performance of Sulfonated MXene Thin‐Film Nanocomposite Membranes in Desalination Abstract Machine learning (ML) has emerged as a valuable tool in advancing thin‐film nanocomposite (TFN) membranes for sustainable water treatment. In this study, a novel data‐driven chemometric framework integrated with ML was developed to predict the performance of sulfonated MXene‐incorporated TFN polyamide membranes for desalination applications. Six ML algorithms namely kernel support vector machine (k‐SVM), decision tree (DT), long short‐term memory (LSTM), recurrent neural network (RNN), random forest (RF), and a hybrid k‐SVM model optimized using particle swarm optimization (k‐SVM‐PSO) were systematically evaluated. Feature engineering of sulfonated MXene concentration, membrane characteristics such as contact angle, surface roughness, surface charge, and physiochemical properties of electrolytes showed prominent control in electrolyte flux and rejection. The metaheuristic‐driven k‐SVM‐PSO model showed outstanding predicted accuracy for electrolyte flux with R 2 = 0.983, based on physicochemical properties parameters of TFN membrane and sulfonated MXene and electrolytes. Predictive ML algorithms also strongly agreed with the experimental dataset in determining non‐linear flux dynamics related to organic foulants fouling patterns. Furthermore, the spectral intensity of chlorinated TFN membranes was successfully predicted, with DT and RF models achieving the highest performance (R 2 = 0.999 and minimal error metrics). This chemometric approach enables advanced prediction of membrane performance for desalination. Nanocomposite ; Chemistry ; Desalination ; Membrane ; Chemical engineering ; Thin-film composite membrane ; Thin film ; Nanotechnology ; Reverse osmosis ; Materials science ; Engineering MXene and MAX Phase Materials ; Nanomaterials for catalytic reactions ; Membrane Separation Technologies
Authors: B. G. Beglaryan | A. S. Zakuskin | Viktor A. Nemchenko | Timur A. Labutin
Source: Journal of Chemical Information and Modeling (article, 2025-05-08)
Field: Chemistry — Analytical Chemistry

Toward Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models Polycyclic aromatic hydrocarbons (PAHs) play a crucial role in astrochemistry, environmental studies, and combustion chemistry, yet interpreting their infrared (IR) spectra remains challenging due to the similarity of spectral features of many molecules. The presumable presence of both neutral and charged PAHs in mixtures complicates spectra interpretation, too. While first-principle calculations provide accurate spectral predictions, their high computational cost limits scalability. This study employs machine learning (ML) to predict PAH IR spectra, emphasizing the applicability of the developed models simultaneously for neutral and ionized molecules. Two models are introduced: an XGBoost model trained on Morgan fingerprints and a graph neural network (GNN) that employs molecular graph representations. Molecular charges are treated by incorporating their one-hot or learnable NN encodings to molecular representations. Both models demonstrate excellent predictive capabilities, for the first time enabling fast and accurate prediction of charged PAHs IR spectra. While the XGBoost model demonstrates the highest accuracy achieved to date, the GNN shows significant promise for future advancements due to the inherent capabilities of molecular graph representations. Remaining challenges, such as the scarcity of data on heteroatomic PAHs, and potential approaches of addressing them are also discussed in the manuscript. Charge (physics) ; Deep learning ; Spectral line ; Artificial intelligence ; Computer science ; Chemistry ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research