AMR parsing is an important problem in natural language processing (NLP) research and it has a broad application in downstream tasks such as question answering [Kapanipathi et al., 2020] and common sense reasoning [Lim et al., 2020].

AMR parsing is an important problem in natural language processing (NLP) research and it has a broad application in downstream tasks such as question answering [Kapanipathi et al., 2020] and common sense reasoning [Lim et al., 2020].

Encouraged early on by Nobel laureate Enrico Fermi, the "Queen of Carbon" laid the foundation for countless advances in nanotechnology--and mentored countless young scientists along the way. At MIT, Mildred Dresselhaus became a beloved professor who pushed her students to be their very best and provided support in ways big and small. Institute Professor Mildred "Millie" Dresselhaus forever altered our understanding of matter--the physical stuff of the universe that has mass and takes up space. Over 57 years at MIT, Dresselhaus also played a significant role in inspiring people to use this new knowledge to tackle some of the world's greatest challenges, from producing clean energy to curing cancer. Although she became an emerita professor in 2007, Dresselhaus, who taught electrical engineering and physics, remained actively involved in research and all other aspects of MIT life until her death in 2017. She would have been 95 this November.

Accurate prediction of electronic band structures in two-dimensional materials remains a fundamental challenge, with existing methods struggling to balance computational efficiency and physical accuracy. We present the Symmetry-Constrained Multi-Scale Physics-Informed Neural Network (SCMS-PINN) v35, which directly learns graphene band structures while rigorously enforcing crystallographic symmetries through a multi-head architecture. Our approach introduces three specialized ResNet-6 pathways - K-head for Dirac physics, M-head for saddle points, and General head for smooth interpolation - operating on 31 physics-informed features extracted from k-points. Progressive Dirac constraint scheduling systematically increases the weight parameter from 5.0 to 25.0, enabling hierarchical learning from global topology to local critical physics. Training on 10,000 k-points over 300 epochs achieves 99.99% reduction in training loss (34.597 to 0.003) with validation loss of 0.0085. The model predicts Dirac point gaps within 30.3 µ eV of theoretical zero and achieves average errors of 53.9 meV (valence) and 40.5 meV (conduction) across the Brillouin zone. This framework establishes a foundation for extending physics-informed learning to broader two-dimensional materials for accelerated discovery. Introduction The accurate prediction of electronic band structures in two-dimensional materials represents a fundamental challenge at the intersection of quantum mechanics, materials science, and machine learning, with profound implications for next-generation electronic and optoelectronic devices [1, 2, 3]. Graphene, the archetypal two-dimensional material, exhibits unique electronic properties arising from its honeycomb lattice structure and linear dispersion relation near the Dirac points, making it both a fascinating subject for fundamental research and a promising candidate for technological applications [4, 5]. Corresponding author Email address: wslee@g.puiching.edu.mo Density functional theory (DFT) has long served as the workhorse for electronic structure calculations, providing reliable results for a wide range of materials systems [6, 7]. Traditional tight-binding approaches offer computational efficiency but fail to capture complex many-body effects, strain-induced modifications, and the subtle interplay between electronic and structural degrees of freedom that are essential for accurate device modeling [10, 11]. Semi-empirical methods attempt to bridge this gap but require extensive parameterization and often lack transferability across different material conditions [12, 13].

AMR parsing is an important problem in natural language processing (NLP) research and it has a broad application in downstream tasks such as question answering [Kapanipathi et al., 2020] and common sense reasoning [Lim et al., 2020].

Machine learning in materials science faces challenges due to limited experimental data, as generating synthesis data is costly and time-consuming, especially with in-house experiments. Mining data from existing literature introduces issues like mixed data quality, inconsistent formats, and variations in reporting experimental parameters, complicating the creation of consistent features for the learning algorithm. Additionally, combining continuous and discrete features can hinder the learning process with limited data. Here, we propose strategies that utilize large language models (LLMs) to enhance machine learning performance on a limited, heterogeneous dataset of graphene chemical vapor deposition synthesis compiled from existing literature. These strategies include prompting modalities for imputing missing data points and leveraging large language model embeddings to encode the complex nomenclature of substrates reported in chemical vapor deposition experiments. The proposed strategies enhance graphene layer classification using a support vector machine (SVM) model, increasing binary classification accuracy from 39% to 65% and ternary accuracy from 52% to 72%. We compare the performance of the SVM and a GPT-4 model, both trained and fine-tuned on the same data. Our results demonstrate that the numerical classifier, when combined with LLM-driven data enhancements, outperforms the standalone LLM predictor, highlighting that in data-scarce scenarios, improving predictive learning with LLM strategies requires more than simple fine-tuning on datasets. Instead, it necessitates sophisticated approaches for data imputation and feature space homogenization to achieve optimal performance. The proposed strategies emphasize data enhancement techniques, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

There's no doubt Great Britain lays claim to some of the greatest scientific discoveries and inventions that have changed the face of modern society. Now, MailOnline's interactive map reveals the birthplace of 30 of these famous British marvels, from stainless steel to the jet engine and the electric motor. Who can forget Alan Turing's Bombe machine, used to break Enigma-enciphered messages about enemy military operations during WWII? Turing developed the Bombe in 1939 at Bletchley Park in Buckinghamshire and hundreds were built, marking a crucial contribution to the war effort. Also on the map is the hovercraft invented by Christopher Cockerell in 1955 and first launched four years later on the the Isle of Wight.

Two-dimensional (2D) materials are poised to revolutionize current solid-state technology with their extraordinary properties. Yet, the primary challenge remains their scalable production. While there have been significant advancements, much of the scientific progress has depended on the exfoliation of materials, a method that poses severe challenges for large-scale applications. With the advent of artificial intelligence (AI) in materials science, innovative synthesis methodologies are now on the horizon. This study explores the forefront of autonomous materials synthesis using an artificial neural network (ANN) trained by evolutionary methods, focusing on the efficient production of graphene. Our approach demonstrates that a neural network can iteratively and autonomously learn a time-dependent protocol for the efficient growth of graphene, without requiring pretraining on what constitutes an effective recipe. Evaluation criteria are based on the proximity of the Raman signature to that of monolayer graphene: higher scores are granted to outcomes whose spectrum more closely resembles that of an ideal continuous monolayer structure. This feedback mechanism allows for iterative refinement of the ANN's time-dependent synthesis protocols, progressively improving sample quality. Through the advancement and application of AI methodologies, this work makes a substantial contribution to the field of materials engineering, fostering a new era of innovation and efficiency in the synthesis process.

This note considers an innovative interdisciplinary methodology that bridges the gap between the fundamental principles of quantum mechanics applied to the study of materials such as tellurium nanoparticles (TeNPs) and graphene and the complex dynamics of social systems. The basis for this approach lies in the metaphorical parallels drawn between the structural features of TeNPs and graphene and the behavioral patterns of social groups in the face of misinformation. TeNPs exhibit unique properties such as the strengthening of covalent bonds within telluric chains and the disruption of secondary structure leading to the separation of these chains. This is analogous to increased cohesion within social groups and disruption of information flow between different subgroups, respectively. . Similarly, the outstanding properties of graphene, such as high electrical conductivity, strength, and flexibility, provide additional aspects for understanding the resilience and adaptability of social structures in response to external stimuli such as fake news. This research note proposes a novel metaphorical framework for analyzing the spread of fake news within social groups, analogous to the structural features of telluric nanoparticles (TeNPs). We investigate how the strengthening of covalent bonds within TeNPs reflects the strengthening of social cohesion in groups that share common beliefs and values. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.