Machine Learning Science and Technology

Journal Article

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

Vasudevan, Rama K; Orozco, Erick; Kalinin, Sergei V

2022 Machine Learning: Science and Technology

The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization...

Open Access

Journal Article

Constraints on parameter choices for successful time-series prediction with echo-state networks

Storm, L; Gustavsson, K; Mehlig, B

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/aca1f6

Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, the network may synchronize with the driving signal. Exploiting...

Open Access

Journal Article

Physics-based representations for machine learning properties of chemical reactions

van Gerwen, Puck; Fabrizio, Alberto; Wodrich, Matthew D; Corminboeuf, Clemence

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac8f1a

Physics-based representations constructed using only atomic positions and nuclear charges (also known as quantum machine learning, QML) allow for the reliable and efficient inference of molecular properties from training data. Chemistry is a science rooted in chemical reactions, naturally...

Open Access

Journal Article

Training neural networks using Metropolis Monte Carlo and an adaptive variant

Whitelam, Stephen; Selin, Viktor; Benlolo, Ian; Casert, Corneel; Tamblyn, Isaac

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/aca6cd

We examine the zero-temperature Metropolis Monte Carlo (MC) algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis MC can train a neural net with an accuracy comparable to...

Open Access

Journal Article

Shift-curvature, SGD, and generalization

Bradley, Arwen V; Gomez-Uribe, Carlos A; Vuyyuru, Manish Reddy

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac92c4

A longstanding debate surrounds the related hypotheses that low-curvature minima generalize better, and that stochastic gradient descent (SGD) discourages curvature. We offer a more complete and nuanced view in support of both hypotheses. First, we show that curvature harms test performance...

Open Access

Journal Article

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Baek, Jihye; O’Connell, Avice M; Parker, Kevin J

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac9bcc pmid:36698865

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide...

Open Access

Journal Article

Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation

Malavath, Pallavi; Devarakonda, Nagaraju

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac9fa5

Due to its importance in various applications that need human-computer interaction (HCI), the field of 3D hand pose estimation (HPE) has recently got a lot of attention. The use of technological developments, such as deep learning networks has accelerated the development of reliable 3D HPE...

Open Access

Journal Article

BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale

Poelking, Carl; Faber, Felix A; Cheng, Bingqing

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac4d11

We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model...

Open Access

Journal Article

Training-free hyperparameter optimization of neural networks for electronic structures in matter

Fiedler, Lenz; Hoffmann, Nils; Mohammed, Parvez; Popoola, Gabriel A; Yovell, Tamar;

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac9956

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite...

Open Access

Journal Article

Near-optimal control of dynamical systems with neural ordinary differential equations

Böttcher, Lucas; Asikis, Thomas

2022 Machine Learning: Science and Technology

doi:10.1088/2632-2153/ac92c3

Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from an initial state x0 to a desired target state x∗in finite time T. Recent advances in deep learning and neural network–based optimization have contributed to the development...

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