This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this second part, we reflect on a selection of …

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model
biomolecular structures such as proteins and their interactions with drug-like small …

The introduction of accelerator devices such as graphics processing units (GPUs) has had
profound impact on molecular dynamics simulations and has enabled order-of-magnitude …

Anton 3 is the newest member in a family of supercomputers specially designed for atomic-
level simulation of molecules relevant to biology (eg, DNA, proteins, and drug molecules) …

We present the GPU version of DeePMD-kit, which, upon training a deep neural network
model using ab initio data, can drive extremely large-scale molecular dynamics (MD) …

Conducting long-timescale simulations of small molecules using Molecular Dynamics (MD)
is crucial in drug design. However, traditional methods to accelerate the process, including …

Molecular dynamics (MD) is an indispensable atomistic-scale computational tool widely-
used in various disciplines. In the past decades, nearly all ab initio MD and machine …

Deep convolutional Neural Networks (CNNs) have revolutionized numerous applications,
but the demand for ever more performance remains unabated. Scaling CNN computations to …

Tensor cores, along with tensor processing units, represent a new form of hardware
acceleration specifically designed for deep neural network calculations in artificial …

Strong scaling of long-range electrostatic force computation, which is a central concern of
long timescale molecular dynamics simulations, is challenging for CPUs and GPUs due to …