jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yujing Zhang | Zhongkang Han | Beien Zhu | Xiaojuan Hu | Maria Troppenz | Santiago Rigamonti | Hui Li | Claudia Draxl | M. V. Ganduglia-Pirovano | Yi Gao
Source: arXiv (Cornell University) (preprint, 2024-06-22)
Field: Materials Science — Materials Chemistry

Decoupling Many-Body Interactions in CeO2 (111) Oxygen Vacancy Structure: Insights from Machine-Learning and Cluster Expansion Oxygen vacancies (VO's) are of paramount importance in influencing the properties and applications of ceria (CeO2). Yet, comprehending the distribution and nature of the VO's poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among VO's and polarons (Ce3+'s). In this study, we employed a combination of LASSO regression in machine learning, in conjunction with a cluster expansion model and first-principles calculations to decouple the interactions among the Ce3+'s and VO's, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable VO-Ce3+ attractions and the least Ce3+-Ce3+/VO-VO repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10^8 Metropolis Monte Carlo samplings of Vo's and Ce3+ in the near-surface of CeO2(111), we explored potential configurations within an 8x8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are most abundant in the third oxygen layer, primarily due to extensive geometric relaxation-an aspect previously overlooked. This behavior is notably dependent on the concentration of Vo. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry. Decoupling (probability) ; Cluster (spacecraft) ; Cluster expansion ; Oxygen ; Vacancy defect ; Chemical physics ; Computer science ; Materials science ; Physics ; Chemistry ; Crystallography ; Engineering ; Thermodynamics ; Control engineering ; Operating system Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Lucía Morán‐González | Jørn Eirik Betten | Hannes Kneiding | David Balcells
Source: ChemRxiv (preprint, 2024-09-02)
Field: Materials Science — Materials Chemistry

The AABBA Graph Kernel: Atom–Atom, Bond–Bond, and Bond–Atom Autocorrelations for Machine Learning Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expressive both globally (molecular topology) and locally (atom and bond properties). Graph kernels are used to transform molecular graphs into fixed-length vectors, which, based on their capacity of measuring similarity, can be used as fingerprints for machine learning (ML). To date, graph kernels have mostly focused on the atomic nodes of the graph. In this work, we developed a graph kernel based on atom–atom, bond–bond, and bond–atom (AABBA) autocorrelations. The resulting vector representations were tested on regression ML tasks on a dataset of transition metal complexes; a benchmark motivated by the higher complexity of these compounds relative to organic molecules. In particular, we tested different flavors of the AABBA kernel in the prediction of the energy barriers and bond distances of the Vaska’s complex dataset (Friederich et al., Chem. Sci., 2020, 11, 4584). For a variety of ML models, including neural net- works, gradient boosting machines, and Gaussian processes, we showed that AABBA outperforms the baseline including only atom–atom autocorrelations. Dimensionality reduction studies also showed that the bond–bond and bond–atom autocorrelations yield many of the most relevant features. We believe that the AABBA graph kernel can accelerate the exploration of large chemical spaces and inspire novel molecular representations in which both atomic and bond properties play an important role. Atom (system on chip) ; Bond ; Bond graph ; Chemistry ; Computer science ; Combinatorics ; Mathematics ; Business ; Parallel computing ; Finance Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Silvan Käser | Jeremy O. Richardson | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2024-07-31)
Field: Materials Science — Materials Chemistry

Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions This work combines state-of-the-art machine learning techniques with highest-level electronic structure calculations and full-dimensional quantum tunneling calculations to obtain a quantitative characterization of tunneling splittings for system sizes that are currently out of reach using traditional approaches. For intramolecular hydrogen transfer in tropolone, the best computed splitting including perturbative corrections in the ring-polymer instanton calculations is 0.94 cm$^{-1}$ and compares with 0.974 cm$^{-1}$ from experiments. On the other hand, for intermolecular double hydrogen transfer in the (propiolic acid)-(formic acid) dimer, the computations yield 0.0147 cm$^{-1}$ which is larger by 40 % compared with experiment (0.0097 cm$^{-1}$) but still in much better agreement than previous attempts (0.63 cm$^{-1}$). The strategy pursued in the present work is applicable to yet larger systems and other properties of interest and provides a rational route for highest-accuracy energy functions for prediction and benchmarking electronic structure methods vis-a-vis experiments. Quantum tunnelling ; Quality (philosophy) ; Energy transfer ; Transfer (computing) ; Energy (signal processing) ; Physics ; Statistical physics ; Quantum mechanics ; Atomic physics ; Computer science Lanthanide and Transition Metal Complexes ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Zihan Pengmei | Zhengyuan Shen | Zichen Wang | Marcus D. Collins | Huzefa Rangwala
Source: arXiv (Cornell University) (preprint, 2024-10-29)
Field: Materials Science — Materials Chemistry

Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer Constructing transferable descriptors for conformation representation of molecular and biological systems finds numerous applications in drug discovery, learning-based molecular dynamics, and protein mechanism analysis. Geometric graph neural networks (Geom-GNNs) with all-atom information have transformed atomistic simulations by serving as a general learnable geometric descriptors for downstream tasks including prediction of interatomic potential and molecular properties. However, common practices involve supervising Geom-GNNs on specific downstream tasks, which suffer from the lack of high-quality data and inaccurate labels leading to poor generalization and performance degradation on out-of-distribution (OOD) scenarios. In this work, we explored the possibility of using pre-trained Geom-GNNs as transferable and highly effective geometric descriptors for improved generalization. To explore their representation power, we studied the scaling behaviors of Geom-GNNs under self-supervised pre-training, supervised and unsupervised learning setups. We find that the expressive power of different architectures can differ on the pre-training task. Interestingly, Geom-GNNs do not follow the power-law scaling on the pre-training task, and universally lack predictable scaling behavior on the supervised tasks with quantum chemical labels important for screening and design of novel molecules. More importantly, we demonstrate how all-atom graph embedding can be organically combined with other neural architectures to enhance the expressive power. Meanwhile, the low-dimensional projection of the latent space shows excellent agreement with conventional geometrical descriptors. Scaling ; Zero (linguistics) ; Training (meteorology) ; Graph ; Transfer (computing) ; Artificial neural network ; Shot (pellet) ; Atom (system on chip) ; Physics ; Computer science ; Artificial intelligence ; Statistical physics ; Theoretical physics ; Geometry ; Mathematics ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Memory and Neural Computing
Authors: Ling Chen | Suming Chen | Xiangwei Mou | Zhaoxiang Liu | Xu Ma | Xiaokun Wu | Xiangwu Deng
Source: PLoS ONE (article, 2024-10-22)
Field: Materials Science — Polymers and Plastics

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF parameters to provide a reference for the simulation of BF processing operationsand the development of related equipment. The average particle size of the 5 types of BFs ranges from 0.136 mm to 0.293 mm, and the small particle size of BF particles causes to the number of BF particles in bamboo processing equipment to reach tens of millions or even billions. When conventional methods are used for simulation, ordinary computers cannot provide the required computing power. To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. Using particle scaling theory and dimensional analysis methods, the average particle size of the BF was increased to 1 mm, and the main discrete element contact parameters of the five types of BF to be tested were used as input layers. The injection method and sidewall collapse method were used to obtain the angle of repose (AR) as the output layer. Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. The relative error between the simulated AR obtained through calibration parameters and the physical experimental values is less than 2.3%. Through BF parameter validity verification, the simulated maximum compression displacement and compression ratio after stabilization were 34.81 mm and 0.477, which were close to the actual experimental results of 34.77 mm and 0.461, respectively, verifying the accuracy of the neural network prediction model. The research results provide a reference for the simulation of BF processing operations and the development of related equipment. Discrete element method ; Artificial neural network ; Calibration ; MATLAB ; Algorithm ; Backpropagation ; Particle (ecology) ; Biological system ; Computer science ; Mathematics ; Materials science ; Simulation ; Statistics ; Artificial intelligence ; Mechanics ; Physics ; Geology Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Bamboo properties and applications
Authors: Christoforos Brozos | Jan G. Rittig | Sandip Bhattacharya | Elie Akanny | Christina Kohlmann | Alexander Mitsos
Source: arXiv (Cornell University) (preprint, 2024-01-03)
Field: Chemistry — Organic Chemistry

Graph Neural Networks for Surfactant Multi-Property Prediction Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many quantitative structure-property relationship (QSPR) models have been developed for surfactants. Each predictive model typically focuses on one surfactant class, mostly nonionics. Graph Neural Networks (GNNs) have exhibited a great predictive performance for property prediction of ionic liquids, polymers and drugs in general. Specifically for surfactants, GNNs can successfully predict critical micelle concentration (CMC), a key surfactant property associated with micellization. A key factor in the predictive ability of QSPR and GNN models is the data available for training. Based on extensive literature search, we create the largest available CMC database with 429 molecules and the first large data collection for surface excess concentration ($Γ$$_{m}$), another surfactant property associated with foaming, with 164 molecules. Then, we develop GNN models to predict the CMC and $Γ$$_{m}$ and we explore different learning approaches, i.e., single- and multi-task learning, as well as different training strategies, namely ensemble and transfer learning. We find that a multi-task GNN with ensemble learning trained on all $Γ$$_{m}$ and CMC data performs best. Finally, we test the ability of our CMC model to generalize on industrial grade pure component surfactants. The GNN yields highly accurate predictions for CMC, showing great potential for future industrial applications. Pulmonary surfactant ; Computer science ; Property (philosophy) ; Quantitative structure–activity relationship ; Graph ; Artificial neural network ; Component (thermodynamics) ; Micelle ; Transfer of learning ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Organic chemistry ; Theoretical computer science ; Thermodynamics ; Engineering Surfactants and Colloidal Systems ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kamila Kucharska-Ambrożej | Joanna Karpińska | Artur Ratkiewicz
Source: Preprints.org (preprint, 2024-08-15)
Field: Chemistry — Analytical Chemistry

Performance of the Multi-Class Classification Tools for Small Samples of Lamiaceae Family Herb Species The primary objective of this study was to identify a rapid and noninvasive methodology for effectively differentiating various herbal species cultivated in Eastern Poland. Typically, a plant&amp;#039;s chemical profile is obtained using chromatographic methods; however, spectroscopic methods can serve as a complementary or independent tool for determining similarities in the analyzed samples. The investigation centered on leaves of plant species from the Lamiaceae family, encompassing basil, lavender, oregano, sage, and thyme. UV-VIS and ATR-FTIR spectra of dried plant samples&amp;#039; powder were recorded and subsequently analyzed using chemometric tools. This paper assesses the relative performance of different discrimination methods, including both traditional methods based on machine learning and those dealing with specimens associated with a specific category. Among the methodologies tested, SIMCA showed the best performance, particularly effective for small training sets. Lamiaceae ; Lavender ; Herb ; SAGE ; Plant species ; Plant taxonomy ; Medicinal herbs ; Botany ; Traditional medicine ; Biology ; Mathematics ; Chromatography ; Chemistry ; Essential oil ; Taxonomy (biology) ; Physics ; Medicine Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Phytochemistry and Biological Activities
Authors: Michael D. White | Gowtham Nimmal Haribabu | Jeyapriya Thimukonda Jegadeesan | Bikramjit Basu | Philip J. Withers | Chris Peter Race
Source: arXiv (Cornell University) (preprint, 2024-01-22)
Field: Materials Science — Materials Chemistry

Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders Microstructure is key to controlling and understanding the properties of metallic materials, but traditional approaches to describing microstructure capture only a small number of features. To enable data-centric approaches to materials discovery, allow efficient storage of microstructural data and assist in quality control in metals processing, we require more complete descriptors of microstructure. The concept of microstructural fingerprinting, using machine learning (ML) to develop quantitative, low-dimensional descriptors of microstructures, has recently attracted significant attention. However, it is difficult to interpret conclusions drawn by ML algorithms, which are commonly referred to as "black boxes". Here we explore variational autoencoders (VAEs), which can be trained to produce microstructural fingerprints in a continuous latent space. VAEs enable the reconstruction of images from fingerprints, allowing us to explore how key features of microstructure are encoded. We develop a VAE architecture based on ResNet18 and train it on Ti-6Al-4V optical micrographs as an example of an industrially important alloy where microstructural control is critical to performance. The latent space is explored in several ways, including by supplying interpolated and randomly perturbed fingerprints to the trained decoder and via dimensionality reduction to explore the distribution of microstructural features within the latent space of fingerprints. We show that the VAE fingerprints exhibit smooth, interpolable behaviour with stability to local perturbations, supporting their suitability as general purpose descriptors for microstructure. We also show that key properties of the microstructures are strongly correlated with position in the latent space, supporting the use of VAE fingerprints for quantitative exploration of process-structure-property relationships. Microstructure ; Artificial intelligence ; Computer science ; Curse of dimensionality ; Pattern recognition (psychology) ; Dimensionality reduction ; Machine learning ; Materials science Machine Learning in Materials Science ; Molecular Biology Techniques and Applications ; Thermography and Photoacoustic Techniques
Authors: Д. О. Потапов | Nikolay Kondratyuk
Source: Journal of Experimental and Theoretical Physics Letters (article, 2024-06-01)
Field: Chemistry — Organic Chemistry

Influence of Nuclear Quantum Effects on the Vibrational Spectrum and Structure of H2O@C60 Endofullerene The structural and dynamic properties of a water molecule undergo changes if it is located inside a fullerene (H2O@C60). In this work, the atomistic simulation method including nuclear quantum effects is used for the first time to describe the low-temperature dynamics and changes in the structure of the water molecule in the fullerene at 5 K. A machine-learning potential on density functional theory trajectories is used to calculate the interactions in this system. Zero-point energy and delocalization of nuclei are taken into account using path integral molecular dynamics. Spectrum (functional analysis) ; Vibrational spectrum ; Solid-state physics ; Nuclear structure ; Quantum ; Materials science ; Physics ; Atomic physics ; Condensed matter physics ; Quantum mechanics ; Molecule Fullerene Chemistry and Applications ; Advanced Chemical Physics Studies ; Atomic and Molecular Physics
Authors: Tianyu Zhang | Yuxiang Ren | Chengbin Hou | Hairong Lv | Xuegong Zhang
Source: arXiv (Cornell University) (preprint, 2024-08-19)
Field: Materials Science — Materials Chemistry

Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models Molecular property prediction is a crucial foundation for drug discovery. In recent years, pre-trained deep learning models have been widely applied to this task. Some approaches that incorporate prior biological domain knowledge into the pre-training framework have achieved impressive results. However, these methods heavily rely on biochemical experts, and retrieving and summarizing vast amounts of domain knowledge literature is both time-consuming and expensive. Large Language Models (LLMs) have demonstrated remarkable performance in understanding and efficiently providing general knowledge. Nevertheless, they occasionally exhibit hallucinations and lack precision in generating domain-specific knowledge. Conversely, Domain-specific Small Models (DSMs) possess rich domain knowledge and can accurately calculate molecular domain-related metrics. However, due to their limited model size and singular functionality, they lack the breadth of knowledge necessary for comprehensive representation learning. To leverage the advantages of both approaches in molecular property prediction, we propose a novel Molecular Graph representation learning framework that integrates Large language models and Domain-specific small models (MolGraph-LarDo). Technically, we design a two-stage prompt strategy where DSMs are introduced to calibrate the knowledge provided by LLMs, enhancing the accuracy of domain-specific information and thus enabling LLMs to generate more precise textual descriptions for molecular samples. Subsequently, we employ a multi-modal alignment method to coordinate various modalities, including molecular graphs and their corresponding descriptive texts, to guide the pre-training of molecular representations. Extensive experiments demonstrate the effectiveness of the proposed method. Computer science ; Graph ; Representation (politics) ; Domain (mathematical analysis) ; Natural language processing ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Political science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Genetics, Bioinformatics, and Biomedical Research
Authors: Alin M. Elena | P.S. Kamath | Théo Jaffrelot Inizan | Andrew Rosen | Federica Zanca | Kristin A. Persson
Source: arXiv (Cornell University) (preprint, 2024-12-03)
Field: Materials Science — Materials Chemistry

Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics. Metal-organic framework ; Throughput ; Phonon ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Physics ; Condensed matter physics ; Physical chemistry ; Telecommunications Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications