jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hadi Abroshan | H. Shaun Kwak | Anand Chandrasekaran | Alex K. Chew | Alexandr Fonari | Mathew D. Halls
Source:  (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Hole transport materials for QLEDs: a combined approach of machine learning and atomistic simulation QLEDs have emerged as an alternative for optoelectronic applications. However, for widespread application of QLEDs, the device efficiency is required to be improved. There is a significant energy level mismatch between the valence band of commonly used quantum dots (QDs) and the HOMO level of traditional hole transport materials (HTMs). Given the small energy level mismatch between the conduction bands of the QDs and commercial electron transport materials, charge carriers in the light-emitting layer are imbalanced. Such a charge imbalance decreases the efficiency of QLED devices, and thus it is of great importance to design novel HTL materials with small energy mismatch with the QDs. Given the numerous potential molecules in the organic space, employing expensive and time-consuming approaches based on chemical intuition and trial-and-error experimentation is practically ineffective. Thus, realizing next-generation QLEDs technologies requires a paradigm change in materials design and development. Here, we combine active learning (AL) and high-throughput quantum mechanical calculations as a novel strategy to efficiently navigate the search space in a large materials library. The AL enables a systematic material screening by accounting multiple optoelectronic properties while minimizing the number of calculations. We further evaluated the top candidates using atomistic simulations and machine learning to investigate charge mobility and thermal stability in their amorphous films. This work offers guidelines for efficient computational screening of materials for QLEDs, reducing laborious, time-consuming, and expensive computer simulations, materials synthesis, and device fabrication. Computer science ; Quantum dot ; Materials science ; Light-emitting diode ; Efficient energy use ; Electron mobility ; Fabrication ; Nanotechnology ; Optoelectronics ; Electrical engineering Machine Learning in Materials Science ; Molecular Junctions and Nanostructures
Authors: Ericsson Tetteh Chenebuah | David Tetteh Chenebuah
Source: arXiv (Cornell University) (preprint, 2023-12-18)
Field: Materials Science — Materials Chemistry

An inorganic ABX3 perovskite materials dataset for target property prediction and classification using machine learning The reliability with Machine Learning (ML) techniques in novel materials discovery often depend on the quality of the dataset, in addition to the relevant features used in describing the material. In this regard, the current study presents and validates a newly processed materials dataset that can be utilized for benchmark ML analysis, as it relates to the prediction and classification of deterministic target properties. Originally, the dataset was extracted from the Open Quantum Materials Database (OQMD) and contains a robust 16,323 samples of ABX3 inorganic perovskite structures. The dataset is tabular in form and is preprocessed to include sixty-one generalized input features that broadly describes the physicochemical, stability/geometrical, and Density Functional Theory (DFT) target properties associated with the elemental ionic sites in a three-dimensional ABX3 polyhedral. For validation, four different ML models are employed to predict three distinctive target properties, namely: formation energy, energy band gap, and crystal system. On experimentation, the best accuracy measurements are reported at 0.013 eV/atom MAE, 0.216 eV MAE, and 85% F1, corresponding to the formation energy prediction, band gap prediction and crystal system multi-classification, respectively. Moreover, the realized results are compared with previous literature and as such, affirms the resourcefulness of the current dataset for future benchmark materials analysis via ML techniques. The preprocessed dataset and source codes are openly available to download from github.com/chenebuah/ML_abx3_dataset. Benchmark (surveying) ; Computer science ; Artificial intelligence ; Property (philosophy) ; Band gap ; Stability (learning theory) ; Machine learning ; Reliability (semiconductor) ; Energy (signal processing) ; Data mining ; Materials science ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Physics ; Statistics ; Geology ; Thermodynamics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Jie Li | Jiashu Liang | Zhe Wang | Aleksandra L. Ptaszek | Xiao Liu | Brad Ganoe | Martin Head‐Gordon | Teresa Head‐Gordon
Source: arXiv (Cornell University) (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification. However, accurate prediction of chemical shifts using the best coupled cluster methods can be prohibitively expensive for systems larger than ten to twenty non-hydrogen atoms on today's computers. By contrast machine learning methods offer inexpensive alternatives but are hampered by generalization to molecules outside the original training set. Here we propose a novel machine learning feature representation informed by intermediate calculations of atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics method, and training it to predict NMR chemical shieldings of a high-level composite theory that is comparable to CCSD(T) in the complete basis set limit. The inexpensive shift machine learning (iShiftML) algorithm is trained through a new progressive active learning workflow that reduces the total number of expensive calculations required when constructing the dataset, while allowing the model to continuously improve on data it has never seen. Furthermore, we show that the error estimations from our model correlate quite well with actual errors to provide confidence values on new predictions. We illustrate the predictive capacity of iShiftML across gas phase experimental chemical shifts for small organic molecules and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments. Chemical shift ; Workflow ; Computer science ; Machine learning ; Generalization ; Artificial intelligence ; Quantum chemical ; Representation (politics) ; Set (abstract data type) ; Test set ; Limit (mathematics) ; Chemical species ; Algorithm ; Molecule ; Chemistry ; Mathematics ; Quantum mechanics ; Physics ; Physical chemistry ; Database Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Teng Yang | Zefeng Cai | Zhengtao Huang | Wenlong Tang | Ruosong Shi | A. Godfrey | Hanxing Liu | Yuan‐Hua Lin | Ce‐Wen Nan | Ye, Meng | LinFeng Zhang | Handong Wang | Ben Xu
Source: arXiv (Cornell University) (preprint, 2023-04-19)
Field: Materials Science — Materials Chemistry

Deep Learning Illuminates Spin and Lattice Interaction in Magnetic Materials Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the spin. However, accurately tracing the simultaneous evolution of both lattice and spin in magnetic materials at an atomic scale is a substantial challenge. This is largely due to the complexity involved in depicting the interaction energy precisely, and its influence on lattice and spin-driving forces, such as atomic force and magnetic torque, which continues to be a daunting task in computational science. Addressing this deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials. Atomic units ; Lattice (music) ; Scalability ; Physics ; Statistical physics ; Computer science ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Md. Akib Khan | A. K. M. M. Morshed | Titan C. Paul
Source: Molecular Simulation (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

A machine learning-oriented pseudo-field approach to accelerate runtime of molecular dynamics simulation of liquids Machine learning methods are increasingly used in research to save time and computational expenses. The data-driven approach relies on existing data for predictions based on statistical inference and interpolation. Liquids, in particular, benefit from machine learning due to their higher computational requirements. In this study, we propose a simple data-driven strategy that employs classical molecular dynamics simulations to generate potential energy spatial distribution data. This data is then used to train a machine learning model by using topologically similar scaled-down systems. The trained model is subsequently employed to predict force field behaviour for complex full-scale systems, resulting in time and computational cost savings. During training, the potential energy pseudo field serves as a model descriptor, while the final predictions are generated using multidimensional regression-based learning (Gaussian Process) and neural architecture learning (Artificial Neural Network), which are integrated into the simulation as lookup tables. Comparing the data-driven and conventional approaches reveals a significant acceleration in overall simulation runtime when appropriately trained machine learning models are utilised. Although the initial training phase of the machine learning models is time-consuming, retraining is unnecessary for future simulations with the same setup. Thus, this approach offers a straightforward means of conducting complex simulations in less time. Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Gaussian process ; Field (mathematics) ; Interpolation (computer graphics) ; Kriging ; Deep learning ; Gaussian ; Motion (physics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Maria Bånkestad | Keven M. Dorst | Göran Widmalm | Jerk Rönnols
Source: arXiv (Cornell University) (preprint, 2023-11-21)
Field: Chemistry — Spectroscopy

Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules. An important part of this process is to predict the NMR chemical shift from the molecular structure. This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectra. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models that rely solely on two-dimensional molecular structure. Even with limited data, the model excels, highlighting its robustness and generalization capabilities. The implications are far-reaching and go beyond an advanced understanding of carbohydrate structures and spectral interpretation. For example, it could accelerate research in pharmaceutical applications, biochemistry, and structural biology, offering a faster and more reliable analysis of molecular structures. Furthermore, our approach is a key step towards a new data-driven era in spectroscopy, potentially influencing spectroscopic techniques beyond NMR. Nuclear magnetic resonance spectroscopy ; Robustness (evolution) ; Computer science ; Biological system ; Artificial neural network ; Molecular dynamics ; Chemical shift ; Chemistry ; Machine learning ; Computational chemistry ; Artificial intelligence ; Stereochemistry ; Biology ; Biochemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; NMR spectroscopy and applications
Authors: Atwakyire Moses | Ying Gui | Marembo Micheal | B.H. Chen | Ding Chen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Intelligent Prediction and Analysis of Mechanical Properties of Magnesium Alloys Using Interpretable Machine Learning Magnesium ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Metallurgy Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: tengxin huang | Zhijiang Yang | Yan-Fei Hu | Hang Yang | Li Pan | Jing Yang | Liangliang Wang | Jun-Jue Ding | Yuquan Yuan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Simple and Accurate Pungency Prediction Model Based on Ensemble Machine Learning Embedded with Quantum Chemistry Knowledge Simple (philosophy) ; Pungency ; Quantum chemical ; Computer science ; Machine learning ; Ensemble learning ; Quantum chemistry ; Artificial intelligence ; Chemistry ; Organic chemistry ; Philosophy ; Catalysis ; Reaction mechanism Various Chemistry Research Topics
Authors: K. Song | Silvan Käser | Kai Töpfer | Luis Itza Vazquez-Salazar | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2023-04-25)
Field: Materials Science — Materials Chemistry

PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations Full dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide means for accurate and efficient molecular simulations in the gas- and condensed-phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM API. To illustrate conceiving, validating, refining and using a typical workflow, para-chloro-phenol is considered as an example. The main focus is on how to approach a concrete problem from a practical perspective and applications to spectroscopic observables and the free energy for the -OH torsion in solution are discussed in detail. For the computed IR spectra in the fingerprint region the computations for para-chloro-phenol in water are in good qualitative agreement with experiment carried out in CCl$_4$. Also, relative intensities are largely consistent with experimental findings. The barrier for rotation of the -OH group increases from $\sim 3.5$ kcal/mol in the gas phase to $\sim 4.1$ kcal/mol from simulations in water due to favourable H-bonding interactions of the -OH group with surrounding water molecules. Observable ; Molecular dynamics ; Water model ; Chemistry ; Molecule ; Workflow ; Phenol ; Computer science ; Computational chemistry ; Statistical physics ; Thermodynamics ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies
Authors: Chuannan Li | Han‐Pu Liang | Xie Zhang | Zijing Lin | Su‐Huai Wei
Source: arXiv (Cornell University) (preprint, 2023-02-07)
Field: Materials Science — Materials Chemistry

Graph deep learning accelerated efficient crystal structure search and feature extraction Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. As a case study, we apply our new approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our new framework is generally applicable to all types of systems for precise and efficient structural search, providing new insights into the relationship between ML-extracted structural features and physical properties. Computer science ; Feature (linguistics) ; Feature extraction ; Graph ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Algorithm ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Tomoya Ishizeki | Kazuaki Tsuji | Masafumi Yashima | Tomoyuki Sato | Takuya Watanabe
Source: IEEJ Transactions on Power and Energy (article, 2023-04-30)
Field: Materials Science — Materials Chemistry

Proposal of Prediction Method of Leakage Current Generation of Polymeric Insulators for Distribution Lines using Neural Network Compared to conventional porcelain insulators, polymeric insulators are easier to work because of their lightness, and have better withstand voltage performance under discharge, rain, and contaminated conditions due to their higher hydrophobicity. Therefore, their application is expected to reduce the size and cost of power distribution facilities, so the introduction of polymeric insulators is being promoted in Japan. On the other hand, the use of polymeric materials for the outer coating raises concerns about their deterioration over time, and clarifying the long-term performance in the contaminated and humid environment unique to Japan is an important issue for practical application. To evaluate the long-term weather resistance of polymeric insulators for distribution lines, Tohoku Electric Power Network Co., Inc. has conducted field tests in the coastal areas along the Sea of Japan. In this study, we developed prediction models of leakage current generation based on meteorological conditions using feed-forward neural networks and verified the effectiveness of the models. And we also evaluated meteorological factors which affect the prediction results with the neural network models. Artificial neural network ; Environmental science ; Coating ; Leakage (economics) ; Voltage ; Insulator (electricity) ; Materials science ; Automotive engineering ; Process engineering ; Computer science ; Electrical engineering ; Engineering ; Composite material ; Artificial intelligence High voltage insulation and dielectric phenomena ; Traffic Prediction and Management Techniques ; Advanced Data and IoT Technologies
Authors: Adrian L. Harris | Juan A. Nepomuceno
Source: arXiv (Cornell University) (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

A Data-Driven Machine Learning Approach for Electron-Molecule Ionization Cross Sections Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient machine learning model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that with training on as few as 10 molecular datasets, the network is able to predict the experimental cross sections of additional molecules with an accuracy similar to experimental uncertainties in existing data. As the number of training molecular datasets increased, the network's predictions became more accurate and, in the worst case, were within 30% of accepted experimental values. In many cases, predictions were within 10% of accepted values. Using a network trained on datasets for 25 different molecules, we present predictions for an additional 27 molecules, including alkanes, alkenes, molecules with ring structures, and DNA nucleotide bases. Ionization ; Artificial neural network ; Computer science ; Cross section (physics) ; Molecule ; Machine learning ; Artificial intelligence ; Experimental data ; Training set ; Data mining ; Physics ; Mathematics ; Ion ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Binh Duong Nguyen | Pavlo Potapenko | Aytekin Demirci | Kishan Govind | Stefan Sandfeld
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Efficient Surrogate Models for Materials Science Simulations: Machine Learning-Based Prediction of Microstructure Properties Surrogate model ; Microstructure ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Hakan Demir | Seda Keskın
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Revealing Acetylene Separation Performances of Anion-Pillared Mofs by Combining Molecular Simulations and Machine Learning Acetylene ; Ion ; Materials science ; Gas separation ; Separation (statistics) ; Nanotechnology ; Computer science ; Chemistry ; Machine learning ; Organic chemistry ; Membrane Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization